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17 Best Books for Data Analysts in 2024

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9-day data science interview crash course, join course, 17 must-read data analytics books for data analysts.

Here's 17 Data Analytics books we recommend every serious Data Analyst reads. Trust us when we say these books are a must-read – as the best-selling authors of Ace the Data Science Interview and creators of Data Analytics Interview Practice Platform DataLemur we've read many Data Analytics books and these truly are the 17 best books on Statistics, SQL, Business Analytics, and Job Hunting out there for Data Analysts.

What are the best books to learn Data Analytics?

The 3 best books to learn Data Analytics are Advancing Into Analytics for people who know Excel well, R for Data Science for a practical introduction to Data Analytics in R, and Data Science for Business to learn how data analytics is applied to solve real-world business problems.

Advancing Into Analytics: From Excel to Python and R

If your a new Data Analyst, and you don’t have any programming experience but are handy at Excel, Advancing Into Analytics by George Mount is the perfect gentle introduction to using R & Python for analytics. By covering fundamental concepts in Excel first, and then showing how they directly translate into a programming language, this book eases you into data analytics making it the best book for beginner Data Analysts.

My only issue with this book is that I do think maybe jumping into Python and R for Data Analysts is too big a leap, when SQL is just a perfectly fine intro into the world of Data Analytics. If you want to start with SQL instead, checkout this free SQL tutorial for Data Analysts .

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data

Don't let the word "Data Science" in the book title for R for Data Science scare you – this book is the perfect hands-on introduction to both Data Science AND Data Analytics. The book does a great job balancing implementation details in R while also giving you a big-picture understanding of the data analytics process. See for yourself - the author graciously made the book free online . One caveat: if you do have previous experience with programming in R, go read Advancing into Analytics first

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Data Science for Business is a great conceptual introduction to Data Analytics and Data Science. The authors do a great job showing you how Data Analytics impacts day-to-day business decisions. However, this books lacks practical exercises and code snippets, making it not a great hands-on book to learn Data Analytics. That said, having the correct mental models for Data Analytics is important, and being able to connect high-level data analysis techniques to high-level business problems is a crucial skill, so we do think it's still worth reading for Data Analysts.

What are the best Statistics books for Data Analysts?

The 3 best Statistics books for Data Analysts to read are Naked Statistics for a fun introduction to statistics, How to Lie with Statistics to see how statistics can be manipulated, and Practical Statistics for Data Scientists to really master the statistical foundations on which Data Analytics is built upon.

Naked Statistics: Stripping the Dread from the Data

If you don't know statistics and need a fun way to get started, Data Analysts should read Naked Statistics: Stripping the Dread from the Data by Charles Wheelan. This book doesn't have any complicated math or statistics formulas – instead, it provides you with the high-level intuition behind import statistics concepts like inference, correlation, and linear regression.

How to Lie with Statistics

Data Analysts yield great power – they can present statistics to support the truth, or corrupt statistics to further their own lies. Every Data Analyst needs to read How to Lie with Statistics understand how journalists, politicians, and uninformed people manipulate statistics to serve their own narratives. Just liked Naked Statistics, this book doesn't have too many statistics formulas or complicated math – instead, it serves as a mental model on how to use statistics well, and guard against its misuse.

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

For Data Analysts trying to master statistics, Practical Statistics for Data Scientists is a must-read book. This book provides a clear and concise introduction to the fundamental concepts of statistics, and has 50+ code examples in Python and R which demonstrate statistical theory. We LOVE this book, because it makes you a better programmer AND a better statistician at the same time, and you'll easily be able to ace probability and statistics interview questions after reading this book!

What are the best SQL books for Data Analysts?

The 3 best books for Data Analysts to learn SQL are:

Practical SQL: A Beginner's Guide to Storytelling with Data

Sql for data scientists: a beginner's guide for building datasets for analysis.

• Minimum Viable SQL Patterns: Hands on Design Patterns for SQL

SQL shows up in most Data Analyst job listings, so if you don't know this important skill, Practical SQL: A Beginner's Guide to Storytelling with Data is the best book for Data Analysts to start learning SQL. Written by Anthony DeBarros, a data journalist at the Wall Street Journal, this book has a particular focus on using SQL to extract insights from data which can help you uncover a story. The real-world case studies mimic the day-to-day work Anthony does at WSJ, which makes this book an extremely practical way to learn SQL.

Don't let the name fool you – SQL for Data Scientists is one of the best SQL books specifically geared towards Data Scientists AND to Data Analysts as well. Unlike other books, which cover SQL broadly because they are written for a Database Administrator or Back-end Software Engineer, this book focuses on the subset of SQL skills that data analysts and data scientists use frequently, like joins , window functions , subqueries , and preparing your data for Machine Learning.

While this book isn't exactly for SQL interview prep, I do think it covers 90% of the technical concepts that SQL interviews cover . For a more comprehensive guide on how to get interview ready, read the Ultimate SQL Interview Guide:

Minimum Viable SQL Patterns

Minimum Viable SQL Patterns is an e-book by Ergest Xheblati, a former Business Intelligence Analyst turned Data Architect. I recommend this book to Data Analysts who are trying to take their SQL to the next level. By focusing on the workflows and patterns that repeat themself day-to-day, the book will have you writing clean and efficient code to solve the most common workplace SQL problems you'll encounter.

What are the best books for your Data Analytics career?

The 4 best books for Data Analysts who are trying to land their dream job in Data Analytics are How to Get a Job in Analytics, Ace the Data Science Interview , Build a Career in Data Science, and the Startup of You.

How to Get a Job in Data Analytics

In the e-book How to Get a Job in Data Analytics , author Michael Dillon interviews 40 professionals in the Data Analytics industry on how to break-in. Michael is a Data Analyst for Manchester United, and previously was a poker player and trader, so he's intimately familiar with transitioning into the field.

Ace the Data Science Interview

Ace the Data Science Interview is the best book to prepare for a technical Data Analyst interview . It covers the most frequently-tested topics in data interviews like Probability, Statistics, SQL query questions , Coding (Python), and Business Analytics. With 201 real data science and data analytics interview questions to practice with, this book is a must-read for those trying to land data jobs at FAANG, tech startups, or on Wall Street. It also includes job-hunting advice, such as mistakes Data Analysts make on their resume, and ways to build a Data Analytics portfolio project to show recruiters and hiring managers you're a good fit.

Of course, we wrote this Amazon Best-Seller, so we’re a tiny bit prejudiced!

If you're looking for the eBook of Ace the Data Science Interview, we're sorry to announce that there aren't any online PDF or Kindle downloads of Ace the Data Science Interview available. However, you can read many of the SQL interview tips in my 5,000-word SQL interview guide . You can also solve many of the data interview questions from the book are on DataLemur - a SQL & Data Science interview platform. For example, you'll find 100+ SQL Interview Questions from FAANG on there to practice with!

Build a Career in Data Science

Switching to a career in data isn't easy, but the book Build a Career in Data Science makes things much easier. This comprehensive guide covers the ins and outs of a Data Analytics career – from everything about what to study for Data Analytics and Data Science, to how to job hunt effectively, to what it takes to succeed in your first few Data Analytics roles. Be warned: the detailed nature of this book might dishearten you, once you realize how much time and effort you need to make this career move. Then again, if Data Analytics was easy, everyone would be doing it!

The Startup of You: Adapt, Take Risks, Grow Your Network, and Transform Your Career

Drawing on the best career advice Silicon Valley has to offer, the book " The Startup of You " helps you think of your Data Analytics career in a more entrepreneurial and scrappy light. Written by Reid Hoffman, Founder of LinkedIn turned VC at Greylock, the book challenges traditional career advice in many places because it argues we no longer live in a world where it's reasonable to work at one company for 30 years and retire with a pension. In today's fast-moving world of Data Analytics & Technology, there's a new set of rules for career success, and the Startup of You explains exactly how to transform your career in this new age.

What are the best books for Data Analysts to improve their business skills?

The 3 books we recommend Data Analysts read to improve their business skills are the Personal MBA, On Strategy by the Boston Consulting Group, and Lean Analytics.

Personal MBA: Master the Art of Business

As a Data Analyst, you'll be closely collaborating with business stakeholders, so it only makes sense you understand more about their world! Otherwise, how can you do a financial analytics project or marketing analytics project, when you don't even know the basics of finance or marketing? That's where the Personal MBA shines, by distilling a 2-year Harvard MBA into something which takes a tiny fraction of the time and money.

The Boston Consulting Group on Strategy

On Strategy is written by the top Management Consultants at the prestigious Boston Consulting Group (BCG). You'll learn big-picture concepts like organization design, change management, and developing business strategies! As a Data Analyst, you might find yourself presenting data-driven recommendations to the C-Suite, or doing analysis that informs the company’s larger strategic vision, so having an understanding of the buzzwords and thought process at the top rungs on the ladder will be an invaluable asset.

Lean Analytics: Use Data to Build a Better Startup Faster

Lean Analytics is valuable to any Data or Business Analyst who frequently has to define new metrics at their workplace. The book walks through the most important metrics to measure for a variety of tech business models, and talks about what makes a metric good or bad. This book is also an excellent resource for anyone interviewing for a Product Analytics role, because many of the interview questions for those types of jobs can be answered by frameworks found in this book.

Curious what Data Science Books to Read?

Because Data Analytics work closely related to the field of Data Science, you'll also enjoy the suggestions made in our article on 13 must-read books for Data Scientists . There, you'll find some of our top books to learn Data Science, the top books on Machine Learning for Data Scientists, and best Product Management books for Data Scientists.

About The Authors: Nick Singh & Kevin Huo

Nick Singh is a former Software Engineer at Facebook & Google, now turned career coach. His career advice on LinkedIn has earned him over 120,000 followers on the platform. Kevin Huo is a former Data Scientist at Facebook, and now a quant on Wall Street. He's helped coach hundreds of people to land data jobs at Amazon, Two Sigma, and Lyft. Together they wrote the Amazon #1 Best-Seller, Ace the Data Science Interview , which solves 201 real Data Science & Data Analytics interview questions from FAANG, Tech Startups, and Wall Street.

Nick Singh then went on to found DataLemur - an interactive SQL & Data Science Interview platform, that features hundreds of real Data Analyst and Data Science questions from companies like Facebook, Google, and Accenture.

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• Data, Analysis, & AI

Want To Learn Data Science? Check Out These Data Science Books

In this article, I share the 12 best data science books in 2024.

Whether you’d like to land a job as a data scientist or you want to further your data science career by learning new skills, I’ve included the most up-to-date data science books for beginners and experienced professionals.

In 2024 and beyond, data science remains essential for modern businesses that want to unlock valuable insights from their data while improving efficiency and creating innovative solutions. 

With the ability to add tremendous value, data science remains a highly lucrative field, with the Bureau of Labor Statistics reporting a median salary in excess of $100,000 for data scientists.

So, if you’re ready, let’s review some of the best data science books available in 2024 to help you learn the skills you need to excel as a data scientist.  

• How To Choose The Best Data Science Book in 2024?

When looking for the best book to learn data science, we considered the following criteria and recommend you use these as well:

• Author credentials: We looked for authors with extensive experience in data science to ensure they have the necessary expertise to provide you with the knowledge you need.
• Level of experience: We looked for data science books for a range of skill levels, including beginner-friendly books and options or experienced data science professionals.
• Publish Date: Being one of the older programming languages, we looked for a mixture of recent publications and classics that are still relevant for data scientists in 2024.
• Reviews from previous readers: We evaluated first-person reviews from our community and from sites like Amazon to gain valuable insights into each book’s strengths and weaknesses.
• Preferred learning style: Some data science books are more hands-on with practical examples, while others take a more theoretical approach, so we included a range of options to help you find one to match your preferred learning style.

Whichever data science book you choose, we’d also recommend pairing it with one of the world-class AI courses offered by Stanford . With access to thought leaders like Andrew Ng, these courses are an excellent way to complement data science skills with AI and ML.

• Best Data Science Books for Beginners

1. Data Science from Scratch: First Principles with Python

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Why we chose this book

If you're starting your journey into data science, Data Science from Scratch by Joel Grus is really an excellent starting point, especially for beginners who want to leverage Python for data science or if you're taking a data science course .

It's also nice that the author has a solid resume, having been a research engineer at the Allen Institute for Artificial Intelligence and a software engineer at Google.

For me, this book stands out for its clear explanation of the fundamentals of data science and its hands-on approach using Python. Of course, you could get into the debate of whether Python or R is better for data science, but let's roll with it and use Python!

I also appreciate how Grus breaks down complex ideas into digestible, easy-to-understand segments. Expect to start with the basics of Python, which is ideal if you're new to the language, before diving into the intricacies of data science. I like this, as it helps form a solid foundation for beginners.

It's also great that each chapter builds upon the last, introducing topics such as statistics, data wrangling, machine learning, and more, all tailored towards practical applications. What I really like is that the 2nd edition focuses on updated techniques and tools, reflecting the latest trends and practices in data science.

Overall, this book is a fantastic starting point for anyone aspiring to understand and apply data science concepts from the ground up. It's a comprehensive guide that not only teaches you the technical skills but also helps you develop the analytical thinking necessary for a data scientist.

• Written by Joel Grus, a seasoned data scientist with real-world experience.
• Python crash course included to get you up to speed.
• Provides a hands-on approach to learning data science with Python.
• Covers fundamental concepts like statistics, machine learning, and data analysis.
• Includes practical examples and exercises to reinforce learning.
• Updated content in the latest edition to reflect current data science practices.
• Focuses on understanding the 'why' behind data science techniques.

2. A Hands-On Introduction to Data Science

Next on my list is this option from Chirag Shah, which is an essential read if you want to gain practical data science and data analytics skills.

Shah’s approach to teaching is very hands-on, which I very much appreciate, and it also focuses on real-world applications and data science projects , making it a pragmatic guide for beginners and intermediate learners alike.

Expect to start with the basics of data manipulation and cleaning, crucial skills for any data scientist, before learning how to handle and prepare data for analysis, a fundamental step in the data science process.

As the book progresses, you delve into more advanced topics like statistical analysis and machine learning. Plus, as promised, you will gain hands-on experience with key techniques like regression analysis, classification, and clustering. These skills are vital for understanding patterns and making predictions from data.

The book also covers essential tools and programming languages used in data science, with a significant focus on Python. This is great, as it means you will learn how to use Python libraries like Pandas for data manipulation, Matplotlib for data visualization, and Scikit-learn for machine learning.

Data visualization, another critical skill, is also thoroughly explored. After all, so much of data science is storytelling , and what better way than with plots? So, get ready to learn how to create insightful, visually appealing representations of data.

It's also nice that in the later chapters, Shah introduces more complex concepts, such as natural language processing (NLP) and deep learning, providing a comprehensive view of the data science landscape.

• Practical skills in data manipulation and cleaning.
• Hands-on experience with statistical analysis and machine learning.
• Proficiency in Python and its libraries for data science tasks.
• In-depth learning of data visualization techniques.
• Introduction to advanced topics like NLP and deep learning.
• Real-world examples and exercises to solidify understanding.

3. Data Science For Dummies

Part of the famous 'For Dummies' series, this option from Lillian Pierson is an excellent starting point for anyone beginning their journey into data science.

For me, what stands out the most about this data science book is how it makes complex concepts accessible to beginners, offering a straightforward, jargon-free introduction to the field of data science.

I also appreciate that from the outset, Pierson focuses on imparting practical skills.

Expect to begin with an overview of what data science is and why it's important before diving into data collection and mining basics, which is ideal for learning how to gather and analyze large sets of data effectively.

Much of the book also explains statistical methods and predictive analytics. This is great, as it means you can learn essential techniques such as regression analysis, classification, and hypothesis testing, which are foundational to making sense of data patterns and trends.

Pierson also introduces the basics of programming for data science, with an emphasis on Python and R, two of the most popular languages in the field. Plus, it's nice to see that there are lots of practical examples and exercises on how to use these programming languages for data analysis.

Data visualization is another key skill covered in the book, so you'll be ready to present data clearly and compellingly with graphs, charts, and other tools.

Another stand-out feature of this book is its coverage of the proprietary STAR Framework, a process that’s been proven to lead profitable data science projects.

To round things off, the later chapters also explore more advanced topics like machine learning and big data technologies, offering a glimpse into the future of data science.

• An easy-to-understand introduction to data science concepts.
• Practical guidance on data collection and data mining.
• Essential techniques in statistical methods and predictive analytics.
• Basics of Python and R programming for data analysis.
• Skills in creating effective data visualizations.
• Lillian Pierson's proprietary STAR Framework for leading profitable data science projects
• Insight into advanced topics like machine learning and big data.

4. Essential Math for Data Science: Take Control of Your Data

One thing's for sure: if you want to pursue data science, you need math! This is why I had to include Essential Math for Data Science by Thomas Nield, as it's an amazing resource for anyone looking to deepen their understanding of the mathematical foundations crucial to data science.

I particularly like how this data science book offers clear and concise explanations of complex mathematical concepts that are tailored for data scientists.

Expect to start out by learning about the basic mathematical principles necessary for data science, including algebra and calculus. The idea here is to refresh your foundational knowledge and ensure you have a solid base to build on with more advanced skills.

For me, one of this book’s key strengths is its focus on statistics and probability, as these are both essential for understanding data analysis and machine learning.

This means you'll be learning about descriptive statistics, probability distributions, and statistical inference, enabling you to interpret data and draw meaningful conclusions effectively.

Linear algebra, another key component of data science, is also covered in detail. This means you'll go in-depth with concepts like vectors, matrices, and linear transformations, which are fundamental in understanding algorithms used in machine learning and data processing.

The book also delves into optimization techniques by covering how to find the most efficient solutions to various data science problems. This includes discussions on gradient descent and other algorithms that are pivotal in machine learning.

Overall, Nield does an excellent job of linking mathematical concepts to real-world data science applications with practical examples and exercises.

I like this as it makes it easier for you to see how these mathematical principles are applied in actual data science tasks; plus, it can really help you feel ready for any upcoming data science interviews you might have planned.

• Comprehensive coverage of algebra, calculus, and their applications in data science.
• In-depth focus on statistics and probability for data analysis.
• Clear explanations of linear algebra concepts crucial for machine learning.
• Practical insights into optimization techniques used in data science.
• Real-world examples linking mathematical theory to data science applications.

5. Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

Becoming a Data Head is another great option for aspiring data scientists, as it covers not only the hard skills you need to work in data but also the lesser-discussed soft skills you need to succeed. 

I particularly like its approachable style and practical insights, which are ideal for beginners or anyone looking to enhance their data literacy.

From the outset, Gutman demystifies the core concepts of data science and analytics with an accessible introduction to the key terms and principles, such as data types, data structures, and the basics of data collection and storage. This foundational knowledge is crucial for anyone looking to become proficient in data analysis.

I also think this book excels in explaining data analysis techniques in a way that's easy to grasp.

So, not only will you learn about various methods for data exploration, including statistical analysis and data visualization, but you'll benefit from relatable examples and real-life scenarios to help you understand how to apply these techniques to uncover insights from data.

Perhaps the most unique aspect of this data science book is its focus on the human element in data science, including the importance of critical thinking, problem-solving, and communication skills in the field.

These are the types of skills you absolutely need to have, and I like that he provides practical advice on how to interpret data results and communicate findings effectively.

To cap things off, this book also offers insights into popular data analysis tools, including an overview of Excel, SQL, and more specialized data science software.

• Clear explanations of fundamental data science and analytics concepts.
• Guidance on data analysis techniques and their practical application.
• Emphasis on critical thinking and problem-solving skills in data analysis.
• Insightful tips on effective communication of data findings.
• Overview of popular data analysis tools and software.

6. Introduction to Data Science: Data Analysis and Prediction Algorithms with R

Written by a professor of data science and a fellow of the American Statistical Association, Introduction to Data Science by Rafael Irizarry is a great choice for anyone looking for a comprehensive and accessible read, which makes it an excellent choice for both students and professionals who are new to the field.

You'll start out with the fundamental concepts of data science, including the basics of data collection and data types, which are crucial for understanding how to handle and analyze data effectively.

A major portion of this data science book is also dedicated to data visualization and exploratory data analysis (EDA), as you'll learn the importance of visualizing data to uncover patterns, trends, and outliers.

I also like that it provides practical examples using popular data visualization tools, helping you to develop essential skills in presenting data insights.

Statistical inference is another key area that's covered in depth, with concepts like probability, hypothesis testing, and confidence intervals being tackled in a clear and concise way.

You'll even get an introduction to the basics of machine learning, including supervised and unsupervised learning techniques, with concepts like regression, classification, and clustering being discussed.

Finally, you'll also learn about the practical applications of data science with R and Python with a range of hands-on examples and exercises, allowing you to apply what you've learned in real-world data analysis scenarios.

• Comprehensive overview of fundamental data science concepts.
• Detailed guidance on data visualization and exploratory data analysis.
• Clear explanations of statistical inference and its applications.
• Introduction to machine learning techniques and their use in data science.
• Practical programming examples using R and Python.
• Best Intermediate Data Science Books

7. Data Science on the Google Cloud Platform

Kicking off our list of intermediate-level data science books is this terrific read from Valliappa Lakshmanan, Director of Analytics and AI Solutions at Google Cloud.

If you're interested in data science in the cloud, especially with the Google Cloud Platform (GCP), this is an essential guide thanks to its practical approach and focus on using Google Cloud's sophisticated tools and services for data science projects.

Expect to start with an introduction to GCP, making it accessible to anyone that's new to cloud computing. You'll also get a detailed overview of the platform's architecture and services, which is invaluable for understanding how to effectively utilize GCP for data science.

For me, this book shines in its coverage of how to set up and manage data processing pipelines on GCP. This is really great, as you'll learn to leverage services like BigQuery for large-scale data analysis, Cloud Dataflow for data processing, and Cloud Machine Learning Engine for building and deploying machine learning models.

This hands-on knowledge is crucial for data scientists who want to work with big data in a cloud environment.

Another plus point for me is the focus on practical scenarios and real-world applications, with case studies and examples that demonstrate how to apply GCP tools in various data science tasks, from data ingestion and cleaning to advanced analytics and machine learning.

It's also nice that this book delves into important topics like building scalable and reliable data pipelines, exploring data using SQL and machine learning, and visualizing data insights. These are all fundamental for data scientists who need to work with complex datasets and derive actionable insights.

• Comprehensive introduction to the Google Cloud Platform for data science.
• Practical guidance on setting up data processing pipelines on GCP.
• In-depth tutorials on using BigQuery, Cloud Dataflow, and Cloud Machine Learning Engine.
• Case studies demonstrating real-world applications of GCP tools in data science.
• Techniques for scalable data analysis, machine learning, and data visualization.

8. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Remember when I mentioned that math is essential for data science? Well, if you want to be granular, you also need to think stats, as these are possibly some of the most important math skills you need, which is why I had to include Practical Statistics for Data Scientists by Peter Bruce.

If you want to deepen your understanding of statistics within the context of data science, this is such a great read, as it helps to present complex statistical concepts in a practical, easy-to-understand manner, making it ideal for data scientists at all levels.

Expect to start off by getting a solid foundation in descriptive statistics, which is essential for understanding how to summarize and describe data sets effectively. This includes coverage of central tendency measures, variability, and data distribution.

You'll then move on to focus on inferential statistics by learning how to make predictions and generalizations about data. This includes topics like hypothesis testing, confidence intervals, and p-values, all fantastic tools to make informed decisions based on data.

I also appreciate that this book dives into regression analysis, one of the most critical techniques in data science. You'll even learn simple and multiple regression methods, gaining skills in modeling relationships between variables and making predictions.

Another key area covered in this book is exploratory data analysis (EDA). Bruce even emphasizes the importance of EDA in discovering patterns, spotting anomalies, and testing hypotheses in a dataset, providing practical examples to illustrate these concepts.

To round things off, you'll also get an introduction to key machine learning concepts and techniques, such as classification, clustering, and decision trees, demonstrating their application in statistical analysis.

• Solid grounding in descriptive and inferential statistics.
• Detailed explanations of hypothesis testing and regression analysis.
• Practical skills in exploratory data analysis (EDA).
• Introduction to key machine learning concepts for statistical applications.
• Real-world examples demonstrating the application of statistical techniques in data science.

9. Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

Here, we have another great choice for data science in the cloud, but this time, we're talking about Data Science on AWS by Chris Fregly.

If you want to utilize the power and flexibility of Amazon Web Services, this is a great starting point thanks to its practical approach to implementing data science solutions on one of the most popular cloud platforms.

You'll start out with a comprehensive overview of AWS services and architecture, which is really crucial for understanding how to effectively use AWS for data science projects.

With the basics done, you'll then learn how to set up and manage robust data processing pipelines on AWS with AWS services like Amazon S3 for data storage, Amazon EMR for big data processing, and AWS Lambda for serverless computing.

These are all essential skills for handling large-scale data efficiently in the cloud.

It's also nice to see that machine learning on AWS is another major focus, as Fregly guides readers through using Amazon SageMaker, a service that allows data scientists to build, train, and deploy machine learning models at scale.

You also get the added benefit of practical examples and insights into using SageMaker and other AWS machine-learning tools.

Plus, if you're already used to working with popular data science tools and programming languages, such as Python, R, and Jupyter notebooks, you'll really like the sections on AWS integration with them.

To round things off, I also appreciate that real-world case studies and examples are provided throughout the book. This practical application is really helpful for understanding the capabilities and advantages of using AWS in various scenarios.

• Detailed introduction to Amazon Web Services for data science.
• Practical guidance on setting up data processing and storage solutions on AWS.
• In-depth tutorials on using Amazon SageMaker for machine learning.
• Integration of AWS with popular data science tools and languages.
• Real-world case studies demonstrating AWS applications in data science.
• Best Advanced Data Scientist Books

10. Cleaning Data for Effective Data Science  

Perhaps one of the most important duties of any data scientist is data cleaning, so it made perfect sense to me to include Cleaning Data for Effective Data Science by David Mertz.

If you want to master one of the most crucial aspects of data science, I think this book really stands out for its detailed and practical approach to this often-overlooked yet critical process of preparing data for analysis.

The book begins by highlighting the importance of clean data in data science, and you'll learn how even the most sophisticated data analysis techniques can lead to misleading results if the underlying data is not properly cleaned and prepared. These are the types of skills that can help you earn a data science certification .

I really appreciate that this data science book provides detailed explanations of various types of data impurities, such as missing values, inconsistent formatting, and outliers. Mertz also covers how to identify these issues along with effective strategies for dealing with them.

Another major plus point of this book is its focus on practical tools and techniques for data cleaning. This means you will learn to use popular programming languages like Python and R, along with their libraries and tools, for data-cleaning tasks.

This even includes detailed explanations on how to use Pandas in Python and dplyr in R for data manipulation and cleaning.

It's also nice to see advanced topics like data transformation and feature engineering, which are essential for preparing data for machine learning models. You also get the benefit of practical examples that show how to transform raw data into formats suitable for analysis.

• In-depth understanding of the importance of data cleaning in data science.
• Techniques to identify and rectify common data impurities.
• Practical guidance on using Python and R for data cleaning.
• Advanced topics in data transformation and feature engineering.
• Iterative approaches to refining data cleaning processes.
• Specific focus on time series data, de-trending, and interpolation

11. Practical Data Science with Python

When it comes to the practical aspects of data science, Python is one of the most popular languages for working professionals, whether they're using popular tools like T ensorFlow or Keras .

So, it made a lot of sense to me to include Practical Data Science with Python by Nathan George.

George begins by introducing Python and its significance for data science, making it accessible to readers with varying levels of Python proficiency while also offering a solid foundation for beginners or advanced insights for more experienced programmers.

For me, one of this book’s key strengths is its comprehensive coverage of Python libraries for data science, such as Pandas for data manipulation, NumPy for numerical computations, and Matplotlib and Seaborn for data visualization.

I also like that the author provides practical examples and exercises to help you understand how to leverage these libraries effectively in data analysis.

Expect to delve into critical data science processes like data cleaning, data exploration, and data visualization, with an emphasis on the importance of these processes in deriving meaningful insights from data and how to execute them efficiently using Python.

You'll also learn about various machine learning algorithms and techniques, including supervised and unsupervised learning, and how to implement them using Python’s Scikit-learn library. This is ideal if you want to learn how to develop predictive models and analyze complex datasets.

To round things off, you'll also cover advanced topics like natural language processing (NLP) and deep learning, providing a well-rounded perspective on the applications of Python in data science.

• Introduction to Python and its role in data science.
• In-depth exploration of Python libraries like Pandas, NumPy, and Matplotlib.
• Practical guidance on data cleaning, exploration, and visualization.
• Comprehensive overview of machine learning algorithms and their implementation in Python.
• Insights into advanced data science topics like NLP and deep learning.

12. The Handbook of Data Science and AI

Finishing off my list of data science books is The Handbook of Data Science and AI by Stefan Papp.

If you're looking for an authoritative resource and a deep dive into the interconnected worlds of data science and artificial intelligence, this is a great choice.

Papp begins by laying out the foundational principles of data science, ensuring you have a good understanding of the basics of data analysis, statistics, and data management. This sets the stage for more advanced discussions and ensures you have a solid grounding.

Another unique aspect of this book is its comprehensive coverage of artificial intelligence, particularly its relationship with data science. It's nice to see the author explore the historical context of AI, its evolution, and its current state, providing a thorough background that's often missing in more narrowly focused texts.

Machine learning, a critical component of both data science and AI, is then covered extensively. Expect to dive into various machine learning algorithms, from basic to advanced, and discuss their practical applications. This also includes a focus on deep learning, neural networks, and their increasing importance in AI research and applications.

It's also interesting to see Papp address the ethical and societal implications of data science and AI, an increasingly important aspect as these technologies become more common. He even prompts readers to consider the responsibilities of data scientists and AI practitioners in shaping a future where technology is beneficial and ethical.

Finally, I also like that this book is rich with real-world examples, case studies, and practical applications, bridging the gap between theoretical knowledge and real-world implementation.

• Solid foundation in the principles of data science.
• Comprehensive exploration of artificial intelligence and its evolution.
• In-depth coverage of machine learning and deep learning techniques.
• Discussion of the ethical and societal implications of data science and AI.
• Real-world examples and case studies illustrating practical applications.
• Data Science Career Opportunities and Growth

Data science offers a wealth of career opportunities. From data scientist to machine learning engineer, the field is ripe with possibilities. Plus, it’s nice to know that the Bureau of Labor Statistics is projecting 36% growth for data science jobs by 2031. 

If you’re new to the field of data and data science, here are some of the most common roles:

• Data Scientists not only perform data analysis, but they also design and implement models that use data to predict and optimize outcomes.
• Machine Learning Engineers apply predictive models and leverage natural language processing while working with vast datasets.
• Data Engineers prepare the "big data" infrastructure to be analyzed by data scientists.
• Wrapping Up

And there you have it, the 12 best data science books to read in 2024, with a range of data science books for beginners and experienced data scientists alike.

As we continue to live in a world defined by data, data science continues to be in high demand by organizations that want to capitalize on the hidden value within their ever-evolving datasets.

By taking the time to review our recommended data science books, you should be able to find a range of data science books that align with your goals and learning style.

Whichever book you choose, we wish you luck as you continue your journey into the world of data science. 

Happy reading!

Are you new to data science and not sure where to start? Check out:

Dataquest’s Career Path for Data Science with Python

• Frequently Asked Questions

1. What Is Data Science?

Data Science is an interdisciplinary field combining programming, statistical analysis, and domain expertise to extract insights from data. It uses machine learning and AI models to predict outcomes, enhance decision-making, and discover patterns in data. 

2. Which Are the Best Data Science Books?

The best data science books will vary depending on your experience level and specific interests, and we’d recommend any of the books on our list. That said, if you have little to no background, Data Science from Scratch is a friendly introduction, and if you’re more experienced, we’d recommend Practical Data Science with Python for a great hands-on guide.

3. How Can I Learn Data Science?

To learn data science, start by understanding statistics, mathematics, and programming languages such as Python or R. To get the most out of your time learning data science, consider combining online courses with one of the best data science books. We’d also recommend participating in Kaggle competitions to apply what you've learned.

4. Can 12th Graders Do Data Science?

Yes, 12th graders can begin learning data science, particularly if they're studying calculus, statistics, and programming. Learning Python, a versatile programming language used in data science, is a good start. There are resources like online tutorials and educational platforms tailored for this age group.

5. Can I Learn Data Science in One Year?

Yes, it's possible to learn the basics of Data Science in a year, but proficiency requires consistent practice. This includes learning programming languages, statistics, and machine learning algorithms and applying these skills in real-world projects. Self-study, using resources like our recommended data science books, and following a structured learning path can aid in achieving this.

6. What Book Should I Read for Data Science?

The best book to learn data science depends on your current level and specific area of interest. If you're seeking one comprehensive book for Data Science, consider Data Science from Scratch , as it offers an in-depth overview of the tools, ideas, and principles behind data science. It also includes a crash course in Python, making it a valuable asset for those starting their data science journey.

7. Is Data Science Stressful?

Data science, like any profession, can be stressful at times due to factors like tight project deadlines, data complexities, or high expectations. The role involves continuous learning, which can also feel overwhelming. However, it is often mitigated by the intellectual stimulation and satisfaction derived from solving complex problems and making impactful decisions. 

8. What Is a Data Scientist’s Salary?

The salary of a Data Scientist can vary significantly based on geographical location, years of experience, industry, and the specific role within data science. In 2024, the median base salary for a data scientist in the U.S. is over $100,000 per year . 

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• 14 Best System Design Books in 2024 | Beginner to Advanced Books

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Rafiya Khan

great job and nice list of data science book for different languages :) keep it up.

4 years ago

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Table of Content

12 excellent data analytics books you should read

Ayesha Saleem

Learning data analytics is a challenge for beginners. Take your learning experience of data analytics one step ahead with these twelve data analytics books. Explore a range of topics, from big data to artificial intelligence.

Data Analytics Books

1. data science for business: what you need to know about data mining and data-analytic thinking  by foster provost and tom fawcett.

This book is written by two globally esteemed data science experts who introduce their readers to the fundamental principles of data science   and then dig deep into the important role data plays in business-related decision-making. They do a great job of demonstrating different techniques and ideas related to analytical thinking without getting into too many technicalities.

Through this book, you can not only begin to appreciate the importance of communication between business strategists and data scientists but can also discover how to approach business problems analytically to generate value.

2. The Data Science Design Manual (Texts in Computer Science) eBook:  S. Skiena, Steven: Books

To survive in a data-driven world, we need to adopt the skills necessary to analyze datasets acquired. Data Science is critical to statistics, data visualization, machine learning, and mathematical modeling, Steven in this book give an overview of data science introduction for beginners in this emerging discipline.

The second part of the book highlights the essential skills, knowledge, and principles required to collect, analyze and interpret data. This book leaves learners spellbound with its step-by-step guidance to develop an inside-out theoretical and practical understanding of data science.

The Data Science Design Manual is a thorough instructor guide for learners eager to kick off their learning journey in Data Science. Lastly, Steven added the application of data science in the world, a wide range of exercises, Kaggle challenges, and most interestingly the examples from a data science show, The Quant Shop to excite the learners. 

3. Data Analytics Made Accessible by Anil Maheshwari

Are you a data enthusiast looking to finally dip your toes in the field? Start with Data Analytics Made Accessible by Anil Maheshwari.  Get a sense of what data analytics is all about and how significant a role it plays in real-world scenarios with this informative, easy-to-follow read.

In fact, this book is considered such a vital resource that numerous universities across the globe have added it to their required textbooks list for their analytics courses. It sheds light on the relationship between business and data by talking at length about business intelligence, data mining, and data warehousing.  

4. Python for Data Analysis  by Wes McKinney

Written by the main author of the  Pandas  library, Python for Data Analysis is a book that spells out the basics of manipulating, processing, cleaning, and crunching data in Python. It is a hands-on book that walks its readers through a broad set of real-world case studies and enables them to solve different types of data analysis problems. 

It introduces different data science tools in Python to the readers in order to get them started on loading, cleaning, transforming, merging, and reshaping data. It also walks you through creating informative visualizations using Matplotlib. 

5. Big Data: A Revolution That Will Transform How We Live, Work, and Think  by Viktor Mayer-Schönberger and Kenneth Cukier

This book is tailor-made for those who want to know the significance of data analytics across different industries. In this work , these two renowned domain experts bring the buzzword ‘big data’ under the limelight and try to dissect how it’s impacting our world and changing our lives, for better or for worse. 

It does not delve into the technical aspects of data science algorithms or applications, rather it’s more of a theoretical primer on what big data really is and how it’s becoming central to different walks of life. Apart from encouraging the readers to embrace this ground-breaking technological development, it also reminds them of the potential digital hazards it poses and how we can protect ourselves from them.

6. Business Unintelligence: Insight and Innovation beyond Analytics and Big Data by Barry Devlin

This book is great for someone who is looking to read through the past, present, and future of business intelligence. Highlighting the great successes and overlooked weaknesses of traditional business intelligence processes, Dr. Devlin delves into how analytics and big data have transformed the landscape of modern-day business intelligence. 

It identifies the tried-and-tested business intelligence practices and provides insight s into how the trinity of information, people, and process conjoin to generate competitive advantage and drive business success in this rapidly advancing world. Furthermore, in this book, Dr. Delvin recommends several new models and frameworks that businesses and companies can employ for an even better tomorrow.

Join our  Data Science Bootcamp  today to start your career in the world of data.

7. Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic

Globally, the culture is visual. Everything we consume from art, and advertisements to TV is visual. Data visualization is the art of narrating stories with a purpose. In this book , Knaflic highlights key points to effectively tell a story backed by data. The book journeys through the importance of situating your data story within a context, guides on the most suitable charts, graphs, and maps to spot trends and outliers, and discusses how to declutter and retain focus on the key points. 

This book is a valuable addition for anyone eager to grasp the basic concepts of data communication. Once you finish reading the book, you will gain a general understanding of several graphs that add a spark to the stories you create from data. Knaflic instills in you the knowledge to tell a story with an impact.

Learn about lead generation through data analytics in this blog

10 ways data analytics can help you generate more leads 

8. Developing Analytic Talent: Becoming a Data Scientist by Vincent Granville

Granville leveraged his lifetime’s experience of working with big data, business analytics, and predictive modeling to compose a “handbook” on data science and data scientists. In this book , you will find learnings that are rarely found in traditional statistical, programming, or computer science textbooks as the author writes from experiential knowledge rather than theoretical. 

Moreover, this book covers all the most valuable information to help you excel in your career as a data scientist. It talks about how data science came to the fore in recent times and became indispensable for organizations using big data. 

The book is divided into three components:

• What is data science and how does it relate to other disciplines
• Data science technical applications along with tutorials and case studies
• Career resources for future and practicing data scientists

This data science book also helps decision-makers to build a better analytics team by informing them about specialized solutions and their uses. Lastly, if you plan to launch a startup around data science, giving this book a reader will give you an edge with some quick ideas based on 20+ industrial experience in Granville.

9. Learning R: A Step-By-Step Function Guide to Data Analysis  by Richard Cotton

Non-technical users are scared off by programming languages. This book is an asset for all non-tech learners of the R language. The author compiled a list of tools that make access to statistical models much easier. This book, step-by-step, introduces the reader to R without  digging into the details of statistics and data modeling. 

The first part of this data science book introduces you to the basics of the R programming language. It discusses data structures, data environment, looping constructs, and packages. If you are already familiar with the basics you can begin with the second part of the book to learn the steps involved in data analysis like loading, cleaning, and transforming data. The second part of the book gives more insight to perform exploratory analysis and modeling.

10. Data Analytics: A Comprehensive Beginner’s Guide to Learn About the Realms of Data Analytics From A-Z by Benjamin Smith

Smith pens down the path to learning data analytics from A to Z in easy-to-understand language. The book offers simplified explanations for challenging topics like sophisticated algorithms, or even the Euclidean Square Estimate. At any point, while reading this book, you will not feel overwhelmed by technical jargon or menacing formulas. 

First, quickly after introducing the topic, the author then explains a real-world use case and then brings forth the technical jargon. Smith demonstrates almost every practical topic with the use of Python, to enable learners to recreate the projects by themselves. The handy tips and practical exercises are a bonus. 

11. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data  by EMC Education Services

With the implementation of Big Data analytics, you explore greater avenues to investigate and generate authentic outcomes to support businesses. It instigates deeper insights that were previously not conveniently doable for everyone. Readers of Data Science and Big Data Analytics perform integration with real-time feeds and queries of structured and unstructured data. As you progress with the chapters in this book, you will open new paths to insight and innovation.

EMC Education Services in this book introduced some of the key techniques and tools suggested by the practitioners for Big Data analytics. Mastering the tools upholds an opportunity of becoming an active contributor to the challenging projects of Big Data analytics. This data science book consists of twelve chapters, crafting a reader’s journey from the Basics of Big Data analytics toward a range of advanced analytical methods, including classification, regression analysis, clustering time series, and text analysis.

All these lessons speak to assist multiple stakeholders which include business and data analysts looking to add Big Data analytics skills to their portfolio; database professionals and managers of business intelligence, analytics, or Big Data groups looking to enrich their analytic skills; and college graduates investigating data science as a career field

12.  An Introduction to Statistical Methods and Data Analysis  by Lyman Ott

Lyman Ott discussed the powerful techniques used in statistical analysis for both advanced undergraduate and graduate students. This book helps students with solutions to solve problems encountered in research projects. Not only does it greatly benefit students in decision making but it also allows them to become critical readers of statistical analyses. The book gained positive feedback from different levels of learners because it presumes the readers to have little or no mathematical background, thus explaining the complex topics in an easy-to-understand way.

Ott extensively covered the introductory statistics in the starting 11 chapters. The book also targets students who struggle to ace their undergraduate capstone courses. Lastly, it provides research studies and examples that connect the statistical concepts to data analysis problems.

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54 Best Data Science Books in 2023 (Vetted by Experts)

First things first: If you want to learn data science, the most important thing you can do is get your hands on some real-world data and start coding. Our learning platform is designed to help you do just that. Even if you’re not using Dataquest, your primary approach to learning data skills should be hands-on, not passive.

But what can you do to keep learning in those moments when you’re not sitting in front of a computer? Read some data science books!

As a student we recently spoke with pointed out, ebooks are a great way to immerse yourself in data science when you can’t actually get hands-on with code. Think of reading during a bus ride, for example, or while waiting in line at the grocery store.

You can also listen to books like you would podcasts. Just use an ebook app with a “read aloud” feature or opt to pay for an audiobook.

There are so many different data science books available, though. Which ones are worth the time? We’ve listed some of the best below. The good news? Many of these books are totally free!

Note: Some of the links below are PDF links. We’ve tried to link to the free versions of books where possible.

Non-Technical Data Science Books

These are books that can help motivate you to start or continue your data science journey. Or they may help you better understand important issues in the data science field. You won’t learn many practical skills from them, but they’re good reads that help show how data and statistics are used in the real world.

1. Weapons of Math Destruction (September 2016)

Rating : 4.5/5 (3,017)

One of the most popular nonfiction works about how “big data” and machine learning are not as unbiased as they might appear. Written by a former Wall Street quantitative analyst.

2. Big Data: A Revolution That Will Transform How We Live, Work, and Think (March  2014)

Rating : 4.3/5 (827)

A good “big picture” read about how data and machine learning are changing lives in the real world — and what else is likely to change in the future. If you’ve heard about the hype, but aren’t really sure how data science can affect things, this is a good place to start.

3. Naked Statistics: Stripping the Dread from Data (January 2014)

Rating : 4.6/5 (2,236)

A good read on statistics and data for the layperson. If you’re interested in learning data science, but it’s been a while since your first math course, this is the book for you. Ideally, it will help you build confidence and intuition about how statistics are useful in the real world.

4. Invisible Women: Data Bias in a World Designed for Men (March 2019)

Rating: 4.7/5 (8,353)

Understanding how biases in data can create inequalities in the real world is critical for anyone working with data. This book details how aspects of gender inequality can be traced to data that treats men as the “default.”

5. Numsense: Data Science for the Layman (March 2017)

Rating : 4.5/5 (299)

A self-described “gentle” introduction to data science and algorithms, with minimal math. This is used as a textbook in some university courses, and it’s a good place to start if you’re interested in data, but a little bit afraid of the math. (By the way, you don’t have to be good at math to learn coding . In fact, it doesn’t even really help).

6. Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are (May 2017)

Rating : 4.4/5 (2,295)

This book is essentially Freakonomics for data science. It’s an interesting read, and it will help you learn how to answer different kinds of questions using data.

7. Algorithms of Oppression: How Search Engines Reinforce Racism (February 2018)

Rating : 4.7/5 (500)

Another book about how algorithms contribute to inequality; this one focuses on search engines. Understanding algorithmic bias, the ways it’s created, and how it can be avoided is really important for anyone who wants to work with data.

8. How to Lead in Data Science (December 2021)

Rating : 4.9/5 (30)

If you’re interested in the soft skills necessary to become a leader in the data science field, this is a great handbook. You’ll learn crucial industry concepts like managing complex data projects, overcoming setbacks, and facilitating diversity amongst teams. 

9. Ace the Data Science Interview (August 2021)

Rating : 4.5/5 (577)

Another great career resource, this book cracks the code of data science interviews. It includes not only hundreds of actual interview questions from data science giants, but also tips for resume and portfolio building. 

10. Data Science for Economics and Finance (June 2021)

Rating : 4.7/5 (39)

Looking for examples of data science success stories? This is the book for you. You’ll get a crash course in data science technologies. Then, you’ll take a deep dive into more than a dozen detailed examples of how data science has changed the fields of economics and finance. 

General Data Science Books

1. the elements of data analytic style (march 2015).

Rating : 3.9/5 (12)

This book by Johns Hopkins professor Jeff Leek is a useful guide for anyone involved with data analysis. It covers a lot of the little details you might miss in statistics lessons and textbooks. Since it's a pay-what-you-want book, you can technically get this one for free. Of course, we recommend making a contribution if you can.

2. The Art of Data Science (June 2016)

Rating : 4.6/5 (44)

This is another pay-what-you-want book. It takes a big-picture view of how to do data science rather than focusing on the technical nitty-gritty of statistical or programming techniques.

3. An Introduction to Data Science (September 2017)

Rating : 4.4/5 (74)

This introductory textbook was written by Syracuse professor Jeffrey Stanton. Not surprisingly, it covers a lot of the fundamentals of data science and statistics. It also covers some R programming. Still, some sections are worthwhile reading even for those who are learning Python.

4. Social Media Mining (April 2014)

Rating : 4.8/5 (14)

This textbook from Cambridge University Press won’t be relevant for every data science project. But if you do have to scrape data from social media platforms, this is a well-rated guidebook. Note that the site also includes links to free slide presentations on related topics.

5. The Data Science Handbook (March 2021)

Rating : 4.4/5 (53)

This book is a collection of interviews with prominent data scientists. It doesn’t offer technical or mathematical insight, but it’s a great read. It’s especially relevant for anyone thinking about data science as a career.

6. Doing Data Science: Straight Talk from the Frontline (October 2013)

Rating : 4.3/5 (170)

This book consists of a collection of talks from data scientists working at a variety of companies. It’s meant to cut through the hype and help you understand how data science works in the real world.

7. Data Science for Dummies (March 2017)

Rating : 4.4/5 (204)

Laugh if you want, but these books provide good, clear introductions to a lot of important concepts. There’s also a Big Data for Dummies that’s worth taking a look at.

8. Data Jujitsu: The Art of Turning Data into Product (November 2012)

Rating : 4.1/5 (141)

Catchy title aside, this book is a good read about general data science processes and the data science problem-solving approach. Plus, it’s written by DJ Patil, arguably the most famous data scientist in the United States.

9. Mining of Massive Datasets (September 2014)

Rating : 4.4/5 (54)

A free textbook on data mining with, as you’d expect from the title, a specific focus on working with huge datasets. Be aware, though, that it’s focused on the math and big-picture theory. Thus, it’s not really a programming tutorial.

10. Designing Data-Intensive Applications (May 2017)

Rating : 4.8/5 (3,098)

This book is more about data engineering than data science. Still, it’s a good read for any aspiring data scientist tasked with creating production-ready models or data engineering work. Note: This is not uncommon in data science roles, particularly at smaller companies.

11. Data Science Job: How to Become a Data Scientist (January 2020)

Rating : 4.0/5 (26)

A book on the non-technical side of learning data science — how to build your data science career. The world of data science changes quickly, but this book was self-published in 2020, so it’s relatively up-to-date. Plus, several reviewers say it’s a good read for beginners. (Dataquest also has a data science job application and career guide if you’re interested in something that’s both shorter and free.)

12. Becoming a Data Head (May 2021)

Rating : 4.6/5 (171)

This book is not just for data scientists, which only adds to its appeal. It’s perfect for newcomers to the field. Why? It discusses data in laymen’s terms while also introducing readers to the lingo and culture of the industry. 

Python for Data Science Books

1. python data science handbook (january 2017).

Rating : 4.6/5 (586)

An O’Reilly text by Jake VanderPlas, this book is also available as a series of Jupyter Notebooks on Github . It’s not for total beginners since it assumes some knowledge of Python programming basics. (But don’t worry – we’ve got an interactive Python course you can take for that).

2. Automate the Boring Stuff with Python (November 2019)

Rating : 4.7/5 (2,494)

This total beginner’s Python book isn’t focused on data science specifically. Still, the introductory concepts it teaches are all relevant in data science. Plus, some of the specific skills later in the book (like web scraping and working with Excel files and CSVs) will also be of use to data scientists.

3. A Byte of Python (September 2013)

Rating : 3.9/5 (9)

Like Automate the Boring Stuff , this is a well-liked Python-from-scratch ebook. It also teaches the basics of the language to total beginners. It’s not data-science-specific, but most of the concepts it covers are relevant to data scientists. It has also been translated into a wide variety of languages, so it’s easily accessible to learners all over the globe.

4. Learn Python, Break Python (February 2014)

Rating : 4.0/5 (11)

Yet another well-liked Python-for-beginners tome! This one encourages readers to learn Python by “breaking” it and watching how it handles errors and mistakes.

5. Data Science from Scratch (May 2019)

Rating : 4.4/5 (589)

This book approaches the task of teaching data science in Python by walking you through how to implement algorithms from scratch. It covers a variety of areas, including deep learning, statistics, NLP, and much more.

6. Python for Beginners (August 2021)

Rating : 4.1/5 (14)

This book aspires to do the impossible – teach you everything you need to know about computer programming from scratch – all in one book. While it may not reach that goal entirely, it will certainly teach you a ton along the way. Note that this guide features Python 3 instruction. 

7. Data Science Projects with Python (July 2021)

Rating : 4.6/5 (49)

A unique find, this book re-creates the experience of working in the field of data science. Readers who immerse themselves in this project-based workbook will come away with newfound skills – not just in Python, but in machine learning, data visualization, logistic regression, and more. 

R for Data Science Books

1. r programming for data science (april 2016).

Rating : 4.2/5 (20)

Roger D. Peng’s text will teach you the basics of R programming from scratch. This is a pay-what-you-want text. Note that for $20 you can get it with all of the mentioned datasets and code files.

2. An Introduction to Data Science (September 2017)

This introductory text was already listed above, but we’re listing it again in the R section because it does cover quite a bit of R programming for data science.

3. Advanced R (May 2019)

Rating : 4.8/5 (143)

This is precisely what it sounds like: a free online text that covers advanced R topics. It’s written by Hadley Wickham, one of the most influential voices in the R community.

4. R Cookbook (July 2019)

Rating : 4.6/5 (102)

Precisely what it sounds like: a collection of R “recipes” for data analysis and data science work.

5. R Graphics Cookbook (November 2018)

Rating : 4.5/5 (77)

Similar to the above, this is a cookbook that’s focused specifically on getting higher-quality graphs and charts out of R.

1. Neural Networks and Deep Learning (August 2018)

Rating :4.5/5 (164)

This free online book aims to teach machine learning principles. It’s not the place to go to learn the technical intricacies of any particular library, and it’s written with the now-outdated Python 2.7 rather than Python 3. Still, there’s a lot of valuable wisdom here.

2. Bayesian Reasoning and Machine Learning (March 2012)

Rating: 4.2/5 (83)

This is a massive 680-page PDF that covers many important machine learning topics. It was written for students who lack a formal background in computer science or advanced mathematics. Total newbies welcome!

3. Understanding Machine Learning: From Theory to Algorithms (May 2014)

Ranking: 4.4/5 (218)

Looking for a thorough review of machine learning, from the fundamentals, all the way through advanced machine learning theory? Look no further.

4. Deep Learning (November 2016)

Rating : 4.3/5 (1850)

This textbook from MIT Press is only available in HTML format. It covers everything – from the basics, through current research, into deep learning.

5. Ng says that courses teaching technical skills can give you a “hammer.” This book aims to teach you how to use that hammer correctly. 6. Natural Language Processing with Python (August 2009)

Rating : 4.4/5 (179)

This is a great text for anyone interested in NLP. The online version has been updated with Python 3.

7. Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow (October 2019)

Rating : 4.8/5 (3,236)

This is a Python-focused machine learning textbook. It uses the scikit-learn and Tensorflow frameworks to explore modeling and build different types of neural nets.

8. Grokking Deep Learning (January 2019)

Rating : 4.4/5 (134)

Grokking means “understanding,” and that’s exactly what this book is focused on. Its goal is to help you understand deep learning well enough to build neural networks from scratch!

9. Deep Learning with Python (December 2017)

Rating : 4.5/5 (1,340)

Another Python-focused deep learning and machine learning text. This one is focused primarily on using the Keras library.

10. Data Science on the Google Cloud Platform (May 2022)

Rating : 5/5 (10)

One of the newest books on our list, this one is a must-read if you want to learn how to build a data pipeline on GCP. We’re particularly drawn to its project-based approach utilizing a real-world business decision. 

Statistics Books

1. rating : 4.5/5 (203) don’t overthink it. this book is precisely what it sounds like: an introductory textbook that teaches probability and statistics. 2. think bayes (june 2021).

Rating : 4.5/5 (41)

An O’Reilly text by Allen Downey that offers an introduction to Bayesian statistics. Note that there is updated Python 3 code for this book available here .

3. Bayesian Methods for Hackers (October 2015)

Rating : 4.3/5 (128)

Here’s another free read on Bayesian statistics and programming. The cool thing about this one is that the chapters are in Jupyter Notebook form, so it’s easy to run, edit, and tinker with all of the code you come across.

4. Statistical Inference for Data Science (May 2016)

A rigorous look at statistical inference. This one is for readers who are already somewhat comfortable with basic statistics topics and programming with R.

5. Rating : 4.7/5 (132) A great introduction to data-science-relevant statistical concepts and R programming. 6. The Elements of Statistical Learning (April 2017)

Rating : 4.6/5 (1,002)

Another valuable statistics text that covers just about everything you might want to know, and then some. (It’s over 750 pages long!) Make sure you get the most updated version of the book here .

7. Data Mining and Machine Learning (January 2020)

Rating : 4.7/5 (18)

This Cambridge University Press text will take you deep into the statistics and algorithms used for various types of data analysis.

8. Think Stats: Exploratory Data Analysis (November 2014)

Rating : 4.2/5 (132)

Another stats text focused on statistics in the context of data analysis work using Python.

Reading (or listening to) books can be a great way to augment your data science learning. But the best way to learn anything, including data science, is to get hands-on and actually do it. Write the code you’re reading about. Collect your own data. Build your own models. Learn by doing.

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20 Best Data Science Books to Know

From textbooks to introductory tomes and mass-market nonfiction.

Data science is a booming field, utilizing technologies like machine learning , algorithms and predictive models to gather everyday insights and make business decisions — which can be difficult to understand if you’re new to the industry.

Best Data Science Books

• Everybody Lies
• Naked Statistics
• Data Science from Scratch
• Think Stats
• An Introduction to Statistical Learning
• Pattern Recognition and Machine Learning

Luckily, we compiled this list of data science books to help you further your knowledge base, ranging from introductory overviews to more advanced content on deep learning , bias in algorithms and more. With recommendations from experts and our own personal picks, here are the data science books to pick up to learn more about the subject.

General Interest Data Science Books

Everybody lies: big data, new data, and what the internet can tell us about who we really are by seth stephens-davidowitz.

This book is like Freakonomics in the age of data science. It’s 100 percent not a technical book. Every chapter tells some peculiar story illustrating a data science concept — like, there’s one chapter about Google searches, another about news, another about image data, etc. It’s a bunch of stories of people being creative and finding patterns in the most random things, because these random things actually reveal a lot. The book has that name because you can lie about what you eat and read, and you can lie about who you’re going to vote for — but if I have access to your search history, I can figure out the truth. It’s a book for people that are curious about what data science is and what it can do — especially when it comes to social data. The author finishes by saying the next Freud will be a  data scientist , the next Foucault will be a data scientist, the next Marx will be a data scientist. I think that’s a bit much perhaps, because data science doesn’t answer every question ever. But it’s a fun book, to be read with a grain of salt. — Chico Camargo, postdoctoral researcher in data science at the  Oxford Internet Institute

Naked Statistics: Stripping the Dread From the Data by Charles Wheelan

This book gives a lot of examples of how statistical concepts apply in the real world. Wheelan does not go into a lot of theory, but he has some pretty interesting examples and a kind of dry sense of humor. This is the only statistics book that’s ever made me laugh, and it’s the book that we recommend our incoming students at the Flatiron School read beforehand. Our students come from a wide variety of statistics backgrounds, but I’ve always gotten really positive feedback on it. It’s ideal for beginners, but I also think that if you’ve never read it and you’re in data science, it’s a great read. — Jeff Herman, lead data science instructor at the  Flatiron School

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil

The author of  this book , Cathy O’Neil, used to be an academic mathematician. Then she went to Wall Street, then she went to Occupy Wall Street and now she’s an activist raising awareness of how algorithms rule our lives, and how they are not as neutral or unbiased as we like to believe. The book is a collection of stories of algorithms’ real-world applications, and a lot of them are about people who were classified as unworthy by an algorithm. Like, someone purchased an item at a particular shop and automatically got their credit card limit lowered, or a college student couldn’t get a job at a local grocery store because the algorithm said so.

She doesn’t just say “boo hoo, bad algorithm, bad machine!” though — she makes an effort to explain the mechanisms that might  make an algorithm racist , for instance. So, why is a policing algorithm sending officers to Black neighborhoods more often? Well, what happened in that case is that the algorithm was fed data on previous police patrols, which were more often in Black neighborhoods. So the algorithm learned that those neighborhoods are the ones that receive more patrols. The algorithm simply reproduced what it was taught. The book makes you think a lot about how you can design algorithms and data science practices to deal with that. — Camargo

​​​​Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble

This book has a few stories, with very simple “data,” which the author explores in depth. I found it a very interesting read, because the author’s background is almost diametrically opposed to mine. She’s 100 percent qualitative, telling stories based on “small data” with a lot of context. 

In one of these stories, the author, Safiya Noble, was organizing a party for her niece and other children, and she searched something like “Black girls” on Google. To her surprise, she didn’t find pictures of children. She found websites like “HOT BLACK SINGLES IN YOUR AREA.” For other search terms, like “Latina girls” and “Asian girls,” she found the same stuff. 

The reason this happened, she explained, is Google’s revenue model. The algorithm will serve whatever ad pays the most. And it becomes a troubling situation, because even though Google is an advertising company, we use it like a public library — like some sort of publicly accessible repository of information. I found it a very sobering read. — Camargo

Related Data Analyst vs. Data Scientist: Similarities and Differences Explained

Data Science Books for Beginners

Data science from scratch: first principles with python by joel grus.

This book is about how to write data science algorithms in  Python . It’s a mix between a textbook and a normal book — a great entryway book, very appropriate for a layperson. So for instance, if I wanted to learn the machine learning algorithm  Naive Bayes , this book says, “We’re going to literally program Naive Bayes as if it doesn’t exist in the world. We’re going to learn the math first and then write the code as part of that. We’ll build this algorithm together with nothing but Python.”

You probably want to know a little bit of Python and a little bit of statistics going in, but this book assumes almost no depth of knowledge. It’s not one of those books that’s like, “This is left to the reader because it’s easy.” And it will teach you all the standard  machine learning algorithms , probably 10 or 15 different ones. — Zach Miller, lead data scientist at  CreditNinja

​ R for Data Science by Hadley Wickham and Garret Grolemund

This book overviews using the  R programming language for data science, with no previous programming experience necessary. Readers are introduced to the basics of R as well as RStudio, an  integrated development environment for R, and Tidyverse, a collection of open-source packages for R. Wickham and Grolemund walk through how to use R and its tools to wrangle, program, explore, model and visualize data, and provide an overall understanding of the data science cycle for beginners. Every section of the book also comes with a data exercise, so readers can practice what they learn along the way. — Built In Staff

The Hundred-Page Machine Learning Book by Andriy Burkov

This book introduces the fundamentals of machine learning and popular algorithms used in the field of data science in a little over 100 pages. Along with covering common machine learning definitions and practices, it illustrates algorithms like  linear and  logistic regression ,  support vector machines and  random forest using Python. The book is suitable for beginners with no prior programming or statistical experience, as well as experienced data science professionals looking for a reference resource. — Built In Staff

Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow by Aurélien Géron

This book will teach you how to run  predictive analytics . In the data science world, there are two main  programming languages : Python and R. There are pros and cons to both, but this book is specifically for Python.  Scikit-Learn ,  Keras and  TensorFlow are all libraries of machine learning and deep learning functions within the Python programming languages. 

You have to be pretty good at these libraries to be a data scientist. When I was starting out, I would reference this book daily. To this day I probably look at it at least monthly as a reference, because he really goes deep into explaining how each algorithm works. A lot of algorithms have a lot of knobs or levers that you can turn — so depending on what the data is doing, you might change the algorithm a little bit. The author explains what those different knobs and levers are in a way that a beginner can understand, but someone with more experience can appreciate the level of detail that he goes into. — Herman

Grokking Deep Learning  by Andrew W. Trask

This book is an introductory textbook for the beginner who wants to go beyond usage and understand a bit of how  deep learning works. People who develop deep learning tools are usually drawing from a lot of mathematics: multivariate calculus,  linear algebra , optimization, often some physics too. But you don’t need all these things to understand what deep learning is doing. In the author’s words, “If you’ve passed high school mathematics and hacked around in Python, you’re ready for this book.” It covers some very general and fundamental bits, such as  gradient descent ,  backpropagation and  regularization , which are used in so many advanced tools that you cannot progress without a decent understanding of them.

I think books like this are important because thanks to online tutorials, you can get to a point where you’re implementing complex stuff without actually understanding how it works — all you need is  Python and an internet connection. And that is troublesome, sometimes. People can waste resources by using deep neural networks where a linear regression would do (using a bazooka to kill a fruit fly, in a sense) or by implementing algorithms that lead to decisions that harm people, without the programmers realizing that’s happening. — Camargo

An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

When I was first learning data science, most statistical textbooks were kind of unreadable. They went in-depth on theory and didn’t really show the application side.  This book doesn’t go as deep statistically as a lot of other books, but it gives you enough knowledge to be successful as a data scientist, and it goes over the key machine learning algorithms. One of the issues people have with data science is that algorithms are these black boxes where you put data in and you get data out and you have no idea what happens in the middle. This book gives you enough statistical knowledge to understand what’s going on in that black box.

It’s geared toward people that don’t have any programming or statistics background. That being said, I’ve actually read this book multiple times. Even if you’re an experienced data scientist, a lot of statistical concepts, you kind of forget about them over time. As you work in a job, you’re not going to be using every single algorithm. You get comfortable. This book allows you to say, okay, maybe I should try this other algorithm. — Herman

Think Stats: Exploratory Data Analysis by Allen B. Downey

Data science is a mix of three different disciplines. One is programming and  computer science ; one is  linear algebra , stats, very math-heavy analytics; and then one is machine learning and algorithms. The ideal data scientist is really good at all of them. But that doesn’t always happen, so  this book is about building out that analytics, math and stats side of your data science knowledge. How do you do testing, how do you determine whether your solutions are working and the distributions are right, and how do you use that math stuff to solve business problems? 

It’s textbook-y, but it isn’t a hardcore textbook. It also merges the statistical analysis with how you would write it in Python. Early in my career, I found statistics fairly easy, but making statistics into a program was more challenging. I found this very helpful for making that connection. — Miller

​​ Linear Algebra Done Right by Sheldon Axler

This book is an undergraduate math textbook. It’s designed for a mid-level linear algebra course, which is something every data scientist can use. It’s not machine learning, it’s not flash programming. But the thing that I use more than anything else is my ability to take a matrix or a high-dimensional space and think about it. This is one of those books that, when you’re done, you will know inside and out how to do matrices and how to handle the vector space and how to do pure math about high-dimensional spaces. I wouldn’t say it’s for everybody, though. If this was your first math book, you would find it daunting. This is for a 200- or 300-level course. — Miller

Related 10 Steps to Become a Data Scientist

Advanced Data Science Books

Pattern recognition and machine learning by christopher m. bishop.

This book is definitely a textbook. It’s also, if you take Data Science From Scratch and then turn up the math level to 11, that’s what this book is. It bases everything on what is known as a Bayesian viewpoint, and it says that it has an intro for Bayesian learning, which it technically does, but any beginner would be mortified by it about two pages in. When I talked to other data scientists who are as nerdy as me, though, this is the book that we always end up talking about.

As far as what pattern recognition means here — any machine learning is pattern recognition, right? Looking at how the stock market used to perform and then projecting how it should perform next, that’s pattern recognition. But similarly looking at a bunch of signs and learning, this pattern means “stop,” that’s a similar thing. Machine learning is a big, fancy, shiny term, which basically just means using the old data to think about the data you haven’t seen before. This is probably the best book I’ve read on the subject, just in terms of just depth and clarity of presentation. He’s not glossing over anything and he’s not making it super beginner-friendly. It’s just, this is how it works, and you can take it or leave it. — Miller

​​​ Deep Learning with Python by François Chollet

The author of this book is the creator of the library called Keras, which makes it a lot easier to build neural networks in Python — and usually, in deep learning, you’re using neural networks on unstructured data. So if you’re trying to predict if there’s a person in an image, or whether a review on Yelp is positive or negative, you would use a deep neural network. I remember when I was reading this, in the second chapter, you build a neural network for the first time. He writes out code in the book, and then you try it out for yourself on your computer, and you get 98 percent accuracy. The data set is a bunch of handwritten numbers and you’re trying to predict what the number is, even though everyone’s handwriting is different. The ones the algorithm gets incorrect are ones that I would probably get incorrect. Being able to do that in the second chapter, I was like, “OK, I’m definitely gonna be finishing this book.” — Herman

Data Science with Python and Dask by Jesse Daniel

The focus of this book is big data — specifically working on it with Dask.

Dask is a library in Python and it’s this buzzword right now. I see it in pretty much every job description my students apply for, and I’m very fond of it. Most companies that work with big data use a library called Spark, but it has a huge learning curve. You have to learn essentially a new language to use it. Dask allows you to interact with massive data sets in libraries that you’re already comfortable with. In this book, I really liked seeing how concepts were applied. The author introduces a data set at the beginning — it’s 42 million parking tickets around New York City — and he’ll explain a concept and then apply it on that data set. — Herman

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppman

This book isn’t a standard pick for a data science book because it’s very much in that data engineering, computer sciences corner of data science’s three pillars. It’s more about designing databases and making sure that your data can flow in and out of your system. If I wanted to build a system to store every Yelp review that’s ever existed, every Yelp user and all of that information — this book is about how you store that. How do you make sure that the data can go in and out? How do you make sure that the data is consistent and reliable? How do you make sure that your system doesn’t break when you get a million users instead of 100,000 users?

It’s not super data science-y, but I think it’s a piece of the puzzle that a lot of data scientists ignore, and it explains why your system should be this way very clearly. It doesn’t assume that you’re a data engineer or an admin. I would say anybody who’s a data scientist owes it to themselves to learn about how the systems they rely on work. But you probably aren’t going to sit down and read this one end to end. It’s more of a reference. — Miller

Related 22 Python Data Science Courses and Bootcamps to Know

Data Science Books for Professionals

Build a career in data science by emily robinson and jacqueline nolis .

This book serves as a guide for landing your first data science role and succeeding as a professional in the field. Rather than focusing on technical knowledge, it outlines how to craft a data science portfolio, ace job interviews and create effective analyses and deploy models at work. Overall, it provides steps for navigating a data science career, from entry-level to managerial roles. — Built In Staff

The Data Science Handbook by Carl Shan, Henry Wang, William Chen and Max Song

This book features in-depth conversations with data scientists from established companies and growing startups alike, including places like Facebook, LinkedIn and Uber. It covers their careers, perspectives, personal stories and general life advice. The data science professionals interviewed come from various backgrounds and industries, too, so it’s a perfect primer for readers curious about the field. — Built In Staff

Data Science for Business by Foster Provost and Tom Fawcett

This book provides a look into the principles of data science and how to apply them for practical and business applications. It walks through how to approach data from an analytical perspective and utilize data-mining techniques, as well. Provost and Fawcett emphasize treating data as a business asset, helping readers understand how to fit data science practices into an organization and use it as a competitive advantage. — Built In Staff

Storytelling with Data  by Cole Nussbaumer Knaflic

This book teaches the foundations of  data visualization , and what practices business professionals (including data scientists) can use for presenting data effectively. It explains how to go beyond conventional tools to truly understand and communicate your data, and goes over the importance of context, audience and storytelling when visualizing data. The book also provides real-world examples for readers to use in their own presentations. — Built In Staff

Frequently Asked Questions

What is a good starter book for data science.

Some good starter books for data science include:

• Data Science from Scratch: First Principles with Python
• Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow
• An Introduction to Statistical Learning: With Applications in R
• Build a Career in Data Science
• The Data Science Handbook

Is data science math-heavy?

Data science can require having mathematical knowledge in linear algebra, calculus and statistics, though the amount of math realistically used will depend on the role and specific task needing to be accomplished.

Is data science hard for beginners?

Data science can be difficult for beginners due to its technical nature, though the subject can be learned with proper training or education.

Rose Velazquez contributed reporting to this story.

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Nonfiction Books » Technology » Data Science Books

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Last updated: September 05, 2023

Data science covers a variety of disciplines and we have expert book recommendations that cover it all. Statistics, data analytics, data vizualisation and the computer language Python .

We spoke to Roger D. Peng , Professor of Biostatistics at Johns Hopkins University to get an overview of data science: "Data science is a pretty big tent. It encompasses a lot of people, and that’s kind of the point. One of the reasons this new concept of ‘data science’ has appeared in recent years is that it covers a wide range of activities that many people have been doing all along."

The best books on Using Data to Understand the World , recommended by Edouard Mathieu

Factfulness: ten reasons we're wrong about the world — and why things are better than you think by hans rosling, the signal and the noise by nate silver, superforecasting: the art and science of prediction by dan gardner & philip e tetlock, thinking in bets: making smarter decisions when you don't have all the facts by annie duke, hello world: how to be human in the age of the machine by hannah fry.

Even as more and more data becomes available, many of us have a view of the world that doesn't correspond to reality. On probabilities in particular, people tend to be completely clueless. Here Edouard Mathieu , Head of Data at Oxford-based research group Our World in Data , recommends books to help readers not only use data to better understand the world, but also make better decisions in daily life.

Even as more and more data becomes available, many of us have a view of the world that doesn’t correspond to reality. On probabilities in particular, people tend to be completely clueless. Here Edouard Mathieu, Head of Data at Oxford-based research group Our World in Data , recommends books to help readers not only use data to better understand the world, but also make better decisions in daily life.

The best books on Data Science , recommended by Roger D. Peng

Statistical evidence: a likelihood paradigm by richard royall, visualize this: the flowingdata guide to design, visualization, and statistics by nathan yau, storytelling with data: a data visualization guide for business professionals by cole nussbaumer knaflic, an introduction to statistical learning: with applications in r by daniela witten, gareth james, robert tibshirani & trevor hastie, design thinking: understanding how designers think and work by nigel cross.

From complex techniques only used by academic statisticians, data science has risen to extreme popularity in only a few years. Roger D. Peng , Professor of Biostatistics at Johns Hopkins University and founder of one of the largest data science online courses, helps us understand this discipline and recommends the five best books to delve into it.

From complex techniques only used by academic statisticians, data science has risen to extreme popularity in only a few years. Roger D. Peng, Professor of Biostatistics at Johns Hopkins University and founder of one of the largest data science online courses, helps us understand this discipline and recommends the five best books to delve into it.

The best books on Learning Python and Data Science , recommended by Vicki Boykis

Learn python the hard way by zed a. shaw, coders at work: reflections on the craft of programming by peter seibel, big data: principles and best practices of scalable realtime data systems nathan marz (with james warren), how to lie with statistics by darrell huff, computer organization and design mips edition: the hardware/software interface by david a. patterson & john l. hennessy.

What do we mean when we talk about 'big data', and how can be become better critical consumers of it? Data scientist Vicki Boykis recommends the best books for learning Python—a language, she says, as versatile as a Swiss Army knife—and shows that it's possible to teach yourself coding and data science.

What do we mean when we talk about ‘big data’, and how can be become better critical consumers of it? Data scientist Vicki Boykis recommends the best books for learning Python—a language, she says, as versatile as a Swiss Army knife—and shows that it’s possible to teach yourself coding and data science.

The best books on Computer Science for Data Scientists , recommended by Hadley Wickham

Structure and interpretation of computer programs by gerald jay sussman, harold abelson & julie sussman, the algorithm design manual by steven s. skiena, the pragmatic programmer: from journeyman to master by andrew hunt & david thomas, the art of readable code by dustin boswell & trevor foucher, style: lessons in clarity and grace by joseph bizup & joseph m. williams.

Data science is often said to be built on three pillars: domain expertise, statistics, and programming. Hadley Wickham , Chief Scientist at RStudio and creator of many packages for the R programming language, chooses the best books to help aspiring data scientists build solid computer science fundamentals.

Data science is often said to be built on three pillars: domain expertise, statistics, and programming. Hadley Wickham, Chief Scientist at RStudio and creator of many packages for the R programming language, chooses the best books to help aspiring data scientists build solid computer science fundamentals.

We ask experts to recommend the five best books in their subject and explain their selection in an interview.

This site has an archive of more than one thousand seven hundred interviews, or eight thousand book recommendations. We publish at least two new interviews per week.

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​​​​The Best Data Books for Aspiring Data Analysts

Even the most tech-savvy data analysts (or aspiring data analysts) can benefit from a digital detox at times. What better way to take a screen break than by curling up with a good book? If the latest fiction best seller isn’t your thing, why not check out a tome that will help you get to grips with a new aspect of data analytics?

In this post, we list a careful selection of our favorite books for data enthusiasts. We’ve grouped these into the following sections, and have deliberately chosen data books we think complement each other well, but you can decide!

• Data books for beginners: Broad introductions to data
• Expressing insights: Data visualization
• Upskilling: Getting to grips with statistics
• Applications: Data analytics in business

From the broad-ranging to the nitty-gritty, here are 12 books for aspiring data analysts:

1. Data books for beginners: Broad introductions to data

Hello world: how to be human in the age of algorithms — hannah fry , 2018.

Amid the hype and potential horrors of sentient machines wiping out humanity, British mathematician Hannah Fry takes readers on a balanced but unflinching tour of the pros and cons of our ever-more algorithm-driven society. With wit and precision, Fry looks at how data and algorithms have the power to transform our world for the better.

She doesn’t hold back on examples—for instance, they have the potential to improve our justice system and advance our healthcare. But Fry doesn’t shy away from exploring areas where our blind faith in algorithms can potentially lead to dystopian horrors. Think the destruction of democracy!

A well-rounded introduction to our data-driven world, this book is a funny and fascinating love letter to data and is suitable for those who are completely new to the field. Highly recommended! 

The Drunkard’s Walk: How Randomness Rules Our Lives — Leonard Mlodinow , 2008

Perhaps the most important skill for any data analyst is the ability to think critically and overcome one’s biases and expectations. The Drunkard’s Walk by U.S. physicist Leonard Mlodinow (who, for the record, was a close friend of the late, great Stephen Hawking) tackles the issue of randomness, chance, and probability in our daily lives.

While this might not initially seem that relevant to the field of data analytics, the book explores how our reliance on statistics for everything—from political polls to student grades and financial markets—is not as infallible as it seems. Irreverent and clear in his explanations, Mlodinow illuminates some of the more complex aspects of probability and statistics, using language that anyone can understand.

This book should help any budding data analyst appreciate the importance of data, while understanding that data analytics goes hand-in-hand with critical thinking skills.

How Smart Machines Think — Sean Gerrish , Kevin Scott , 2018

Fascinated by self-driving cars and computers that can beat humans at chess? Want to know how Netflix figures out what you want to watch with such a high level of accuracy? Look no further. Written by two expert machine learning engineers, How Smart Machines Think is the ideal introduction to artificial intelligence and machine learning for those who know next to nothing about the topic.

The book explores both the theory and the practice of creating machine learning algorithms, explaining both how they work (via reinforced learning, much in the way a dog is trained with treats) as well as the software architecture behind famous deep learning and artificial neural networks, such as DeepMind’s AlphaGo .

The book also gives a voice to the experts behind these cutting-edge technologies, making it an all-round box-ticker for any data analyst interested in the topic—and if you aren’t interested yet, you certainly will be once you’ve finished reading!

2. Expressing insights: Data visualization

Cartographies of time: a history of the timeline —daniel rosenberg and anthony grafton, 2010.

About a decade ago, there was a huge hype around infographics—where exactly it came from, who knows, but suddenly companies everywhere were representing their histories (usually badly) on some kind of graphical timeline. The impression was that this was somehow a new idea.

This book puts an end to that notion, taking the reader on a historical journey through one of the first types of data representation—the timeline. A history of graphic representations of time in Europe and the United States, Cartographies of Time highlights that the timelines are not the preserve of 21st-century marketers, but have been around for centuries.

From representing the genealogies of Christ using human body parts, to charting ships at points in time (rather than geographic location) this book is a fascinating visual treat. It’s stuffed with great illustrations, too, making it a lush addition to our list!

The Functional Art: An Introduction to Information Graphics and Visualization — Alberto Cairo , 2008

From the history of data visualization to a practical guide, The Functional Art offers tips for using data viz to represent important insights. Written by data journalist Alberto Cairo, the book leans towards data viz for public consumption but the principles can be broadly applied.

A practical introduction, it explores how turning figures into graphics can help the human brain better comprehend information. Cairo introduces everything from statistical charts, maps, and explanatory diagrams and how these are commonly used across industries.

The important thing about this book is that it relies on core underlying principles, namely driving home how data viz best practice and beautiful representation should go hand in hand—neither be prioritized at the expense of the other. A must-read for any newbie data viz enthusiast.

Knowledge Is Beautiful: A Visual Miscellaneum of Compelling Information — David McCandless , 2014

 If you want a book that’s a little less didactic and isn’t back-to-back text, then this is the one for you. Writer and designer, David McCandless, has published several books on data visualization, and it’s hard to choose between them! However, we’ve selected this one as this book is a true piece of artwork—a visual libation to data viz.

McCandless’ genius eye shows how to represent data that are too complex or abstract to be understood in any other way. This inspirational piece demonstrates many ways in which we can blend data points, representing their relationships to one another in beautiful but meaningful ways.

The author doesn’t only focus on visuals, though, but highlights ways of connecting datasets that many might not think to compare. A book you’ll want to take your time over, and a future coffee table favorite, it’s well worth checking out.

3. Upskilling: Getting to grips with statistics

The art of statistics: how to learn from data — david spiegelhalter , 2019.

Statistics is a fundamental skill for any data analyst. But before adopting the tools necessary for carrying out statistical analyses in a workplace setting, you need to get the basics down. In The Art of Statistics , renowned statistician David Spiegelhalter is on-hand to help, specifically aiming to improve the reader’s statistical literacy.

After covering the ‘basics’ (we use quotes, since you’ll need a solid foundation in math to grasp the concepts), Spiegelhalter gets behind the theory to explain how you can use different models to pull accurate insights from raw data. Using lots of real-world examples to bring the concepts to life, the book introduces all the statistical techniques you’ll need to start your journey in data analytics. It’s also a great reference book for returning to. 

Python for Data Analysis — Wes McKinney , 2011

Once you’ve nailed the basic statistical models, start learning the tools you’ll need to apply them. Enter Python for Data Analysis . Written by the software developer behind the pandas Python library for data analysis, this book will cover everything you need to know about the most common programming language in the field.

McKinney looks at the process of manipulating, cleaning, collating, and analyzing data using Python, adopting hands-on tasks so you can play around with Python and its features, using the book as a guide. From Python’s basic numerical features, to creating scatterplots and using the language for problem-solving in areas like social sciences and economics, the book is packed with examples and case studies. A great one for introducing what could otherwise be a tough topic.

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python —Andrew Bruce, Peter C. Bruce, and Peter Gedeck, 2020

Bridging the gap between programming and statistics, this book—co-written by three renowned data experts—will expand your knowledge of statistics using both the Python and R programming languages. Acknowledging that most data analysts aren’t formally trained in statistical programming, Practical Statistics for Data Scientists takes a data-analysis-specific look at statistical problem-solving.

The great thing about this book is that it’s not just a ‘how-to’ statistics guide. It also links the concepts to fundamental data analytics theory, such as why exploratory data analysis is so important (and how to carry it out). From concepts such as random sampling and experimental design to techniques like regression and classification, this book covers it all, while acting as a useful training guide for Python and R.

4. Applications: Data analytics in business

Data science for business: what you need to know about data mining and data-analytic thinking — foster provost , tom fawcett.

Feel comfortable with the basic techniques and tools we’ve covered so far? Then perhaps it’s time to build on the concepts you’ve learned in a business intelligence context. More of a technical guide than any of the other data books we’ve listed so far, Data Science for Business includes both the math you’ll need to grasp and apply various statistical models, as well as the wider contexts in which you’ll use them.

The book is based on an MBA course taught for over a decade by Foster Provost at New York University. Although not ideal for beginners, it’s definitely comprehensive and uses excellent real-world business examples to lift the concepts off the page. Perfect if you want to dive a bit deeper and test your intellect.

Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions — Matt Taddy , 2019

If you’re looking for tools you’re likely to use, rather than an encyclopedia of concepts, Matt Taddy delivers. With hands-on experience at companies like eBay, Microsoft, and Amazon, his expertise—in the fields of economics, big data, and machine learning—is at the cutting edge of technologies being used within data analytics today.

You’ll need some statistics know-how before diving in with this tome, but for the most part, the book is written in an appealing, chatty manner that should appeal to everyone from business leaders to data engineers. What makes this book stand out though, is that it goes beyond just listing applications and techniques. Rather, using real-world examples, Taddy shares his personal insights on the use of data science in business, which makes it feel like a real treasure trove of hidden secrets.

Invisible Women: Data Bias in a World Designed for Men — Caroline Criado Pérez , 2019

Creating successful businesses means more than having the right practical data skills. It also means understanding the systems that underpin our work. In data science, this means becoming aware of our built-in biases. Invisible Women shines a light on this issue, exploring how vast amounts of data fail to account for gender, treating men as ‘the norm’ and women as atypical.

Without apportioning blame or shame, the book simply states the facts, showing that baked-in biases shape everything from how our technology is designed for men, how our healthcare is built on the male anatomy, and how the way our society is subsequently shaped impacts negatively on women.

While this book focuses on gender inequality, it’s a must-read for any data analyst looking to expand their awareness of how all different minority groups are represented (or not represented) in big data. We hold a great responsibility for others in our hands, and we must take that responsibility seriously.  

There we have it—12 carefully curated data books catering to aspiring data analysts of all experience levels. Whether you’re still learning the basics, or are ready to dive in with tools like Python and R, we hope you’ll find something on our list to enjoy!

Brand new to data analytics and want to test the water before splashing the cash on a book? Why not check out this free, 5-day data analytics short course ? You can also explore the following introductory data analytics posts:

• 15 Data Science Podcasts for Data Enthusiasts
• What Does a Data Analyst Actually Do?
• What Is Data Visualization and Why Is It Important? A Complete Introduction

The 14 Best Statistics Books for Data Science

Statistics and data science are among the most challenging subjects to self-learn. If you have never had experience in any of these two fields, you will need to be ready to put time and commitment to learning these always-evolving disciplines. However, in this process, picking the right volumes and textbooks is essential. 

The best statistics books for Data Science include Naked Statistics: Stripping the Dread from the Data by Charles Wheelan and Practical Statistics for Data Scientists – Peter Bruce. To learn more about stats in R, read Discovering Statistics Using R – A. Field, J. Miles, and Z. Field.

Find out more about the best books to learn statistics from scratch and become a skilled data scientist .

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

Table of Contents

Are Books Effective to Learn Statistics for Data Science?

Textbooks and specialized training sessions have been used in university courses to improve the quality of the teaching. However, if you are trying to learn statistics from scratch to become a data scientist , be aware that there are significant limitations presented by textbooks. 

• Data Science, as stated in several Forbes articles, is a relatively new field , in which innovations happen every day and developments are carried out continuously. Consequently, only a few staple books can be useful to understand the basic concepts of this discipline. Therefore, if you are looking for some recent research or innovation, you are better off consulting the internet or journals on the field. 
• If you are not sure about what data science entails , there is always the danger of getting lost in the myriad of information that composes the field of statistics. While the majority of concepts are also the pillars at the core of the field of Data Science, some other concepts might not be so relevant when you are looking for a job in data science. 
• Statistics are considered among the most challenging subjects to self-learn using only volumes and textbooks. Therefore, you will need to be well-equipped with patience, commitment, constancy, and willingness to go over some more complicated subjects a few times. 

While it is easy to get discouraged, keep in mind that it is normal to find some challenges when studying a field so complicated and in evolution like data science or statistics can be. Additionally, using other learning methods and tools such as online videos and training can help you understand some concepts easier and faster.

Statistics – Robert S. Witte and John S. Witte

If you wish to approach the field of statistics and you have no previous experience in the field, this is a suitable book for you. 

The 11th edition of this volume has been released, and you can find updated information and latest innovation alongside staple principles and concepts of statistics.

In terms of knowledge level, you can expect to grow from a beginner level to an undergraduate level. The journey is assisted by the organized chapter, easy-to-understand text, and clear graphs. 

While this book is perfect if you are just starting your studies, many professionals opt to use it as a backup reference for certain projects.

Among the most important features of this book is the fact that every jargon and obscure terms are explained in detail. Some of the concepts covered include variations of coefficient and correlation, interpretation, and hypothesis.

• Accessibility: available online, the price varies from over $170 to $21 (for the eBook)
• Experience level: Beginner
• Best for: learners interested in the basics of statistics. It focuses on basic principles and essential concepts.
• Find it here in the eBook format: Statistics, 11th Edition  

Barron’s AP Statistics, 8th Edition – Martin Sternstein, PhD

Written by the head of various math departments in Universities, the Barron’s AP Statistics volume focuses primarily on the connection between math and statistics. 

Of course, mathematical algorithms and calculations are at the core of this field as well as data science. However, other books only focus on one aspect, excluding some of the basics of math. 

This affordable book is also easy to read and highly accessible. Inside, you will find 15 chapters – one for each basic concept of statistics. While some might not be covered particularly in-depth, you can get an all-around knowledge of a subject.

If you would like to practice, this book includes a CD to watch and tests that you should be able to pass at the end of every chapter. Answers to the questions are also included to enable self-learning.

• Accessibility: available online at the cost of around $9. On eBay, you can find cheaper second-hand versions.
• Experience level: beginners and experts looking at specializing 
• Best for: beginner statisticians interested in the link between math and statistics

Statistics for Business and Economics – James T. McClave, P. George Benson and Terry T. Sincich

This book is the brainchild of a series of experts in the fields of math, finances, market trends, and statistics. Unlike the option seen above, this book primarily focuses on the applications that statistics find in the world of business and economics. 

The fact that the authors have brought their own experience into the making of this book offers students the opportunity to work with real-world examples and truthful reports. You can find traces of these stories in the example used, as well as in tests and exercises. 

Another aspect of Statistics for Business and Economics worth mentioning is the fact that this book is organized in easy-to-read chapters that revolve around a relevant case study. These real-world instances are used to explain a new concept of statistics to the students.

One of the main advantages of this type of learning technique is that you are likely to find the content more motivating and engaging. This is not always true in the case of statistics books that don’t refer so much to real-life scenarios and practical applications.

• Accessibility: available in a range of formats, with prices varying from $10 to $150
• Experience level: beginner and intermediate
• Best for: statistic students interested in business application and real-world data

Naked Statistics: Stripping the Dread From the Data – Charles Wheelan

If you have been waiting to find a book that would make you fall in love with statistics, at first sight, you have found it. This book is a little irreverent, and it has a unique point of view over the always-considered serious and monotone field.

Funny and accessible, this book is created to be an optimal choice for everybody, whether you are a navigated student, amateur statistician, or just curious about a field that can open so many career opportunities.

While using real-world examples and easy-to-read chapters, this relatively small volume works perfectly for everybody who is looking for an alternative introduction to statistics. 

Of course, you might need to complement this book with another, more in-depth volume that can explain in more detail some main topics. However, if you were not sure whether statistics is the field for you or not, Naked Statistics can give you an immediate answer!

• Accessibility: it is available online, with a cost ranging between $7 and $9. You can also opt for the free Audible version.
• Experience level: beginners, curious
• Best for: students interested in the real-world application of statistics with a fun twist.

Practical Statistics for Data Scientists – Peter Bruce

The complete title of this book is Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. This headline says a lot about how useful this modern volume could be when starting out your career in data science .

While focusing on the notions of data science and the use of R, this book brings the attention of the readers on the fact that not many data scientists have formal training in statistics . Nonetheless, this whole discipline is founded in the field of statistics.

Instead, this book starts with the statistical concepts and tells you what the best use you could make of them in the field of data science. The chapters cover:

• Importance of exploratory data in data science
• Random sampling
• Experimental design’s principles
• Detection of anomalies
• Statistical machine learning methods 
• Unsupervised learning methods

These are just among the concepts you will learn in this book, but several of the chapters explore in-depth other techniques that can be used in Data Science.

• Accessibility: around $40 if bought online. There is also a free version available in PDF format if you don’t feel like committing to a significant expense.
• Experience level: beginner-intermediate. Knowledge of R preferred
• Best for: Statisticians who are looking at using Python and R
• Free PDF: Practical Statistics for Data Scientists  

Head First Statistics: A Brain-Friendly Guide – Dawn Griffiths

One of the selling points of this accessible volume is the fact that it tries to make fun and to entertain a subject such as statistics – and it succeeds in it. Firstly, you will be able to find simplified concepts and explanations of jargon and acronyms. 

Alone, these two characteristics would be enough for you to move onto your studies further. However, this book does not stop here. Indeed, reading the different chapters, you will explore all the major concepts of statistics, including the ones that are the most suitable for data science projects.

The puzzles, visual aids, case studies, and real-world examples included in this book make sure it fits in the top more interesting books to learn statistics for data science. 

• Accessibility: online cost varying between $7 and $23
• Experience level: beginners
• Best for: students interested in concepts but not in terms and jargon

Introduction to Statistical Learning – Gareth James

If you are looking for a complete, all-encompassing introduction to the field of Statistical Learning, this volume is the right one for you. However, the book focuses on the explanations of how to use large data sets to allow a pattern to emerge. 

Therefore, if you want to launch a career in data science, this book should already be in your shopping cart.

Inside, you will be able to find real-world examples, graphs, charts, and case studies that can help simplify even the most complex concept. R – the preferred programming language by data scientists – is used for the analysis of certain situations, so you have a complete toolkit to start practicing in the field.

• Accessibility: the cost varies depending on the format and can be as high as $50. The volume is also available on Springer.
• Experience level: beginner, but linear regression knowledge is assumed
• Best for: students with a basic level of mathematical knowledge

Think Stats – Allen Downey

Think Stats is a modern, easy-to-read book that can help you refine your skill as a statistician and data scientist. This book focuses on the use of programming languages such as R and Python to perform tasks such as statistical analysis instead of completing the process mathematically.

To have an all-encompassing knowledge of the process. This book uses a single case study throughout the book. This case study will show you how to gather the data, analyze them, and draw conclusions from them.

Since you will be using real-world data during your training, you will also acquire some statistical knowledge that is useful in data science.

• Accessibility: between $20 and $40
• Experience level: beginner statisticians with experience in computing sciences or programming. Knowledge of coding and programming is assumed.
• Best for: students who want to upgrade their skills and use statistics within their current project.

All of Statistics: A Concise Course in Statistical Inference – Larry A. Wasserman

It is not exactly as the title of the book says – it does not cover all of the statistics. It is fair to say that this statistical book helps you discover a much greater range of concepts than most other introductory books, but it might not show you an in-depth look of all the characteristics of certain models and notions.

If you are already familiar with statistical aspects, reading this book can broaden your career-related horizon. Moreover, unlike other more traditional books about stats, this volume includes the latest innovations and the most modern upgrades on staple concepts of statistics.

• Accessibility: parts available on SpringerLink. The whole volume is accessible for minimal cost.
• Experience level: introductory book on mathematical statistics
• Best for: beginners 

Statistics – David A. Freedman

While not among the most recent books on statistics, this volume contains basic notions and staple concepts that are useful in many fields. 

Whether you wish to take your education further and specialize in data science or you wish to pursue a project’s research, this book will give you all the fundamentals you need to face most tasks.

If you are worried about the lack of new concepts and innovations, keep in mind that new editions are released regularly for the benefits of students and professionals alike.

• Accessibility: free PDF version available. Otherwise, it can cost between $50 and $100.
• Best for: beginners who are looking to cover all the main concepts of statistics

Innumeracy: Mathematical Illiteracy and Its Consequences – John Allen Paulos

First published in 1988, this bestseller asks why it is important to understand mathematical and statistical sciences. 

In the pages of Innumeracy, you will be able to find out about the consequences of innumeracy and the benefits of having control over it. Mathematics and statistics are indeed used in many aspects of societies, including lotteries and insurance firms. 

Understanding how probability and trends are functioning can offer you better control over what is happening in your life.

If you know that you have always been interested in the field of statistics, but you are not sure what you will do with the knowledge acquired, go ahead and purchase this book. 

• Accessibility: available online for $4 to $7
• Experience level: beginner/curious
• Best for: someone who wants to know more about the importance of learning more about math and stats – and, of course, data science.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction – Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

It is essential to understand what is the concept of statistics that you are bound to use in data science. Indeed, statistics is an extremely broad field that might include concepts not useful in other disciplines.

However, if you are looking for a book that can help you refine the skills needed for data science, the knowledge presented in this book is what you need. Indeed, many companies rely on processes such as data mining, prediction, and inference to create analytical models that can be used in real life.

Unfortunately, there is a limited number of books on the market that will be as clear as this one when explaining such complex processes. Luckily, though, a free PDF version is available for you to grab.

• Accessibility: the free PDF gives you accessibility to this resource every time you need it.
• Experience level: intermediate
• Best for: learners looking at deepening their knowledge in data mining and prediction models
• Free version: The Elements of Statistical Learning  

Discovering Statistics Using R – Andy Field, Jeremy Miles, and Zoë Field

While you won’t need to have an exhaustive knowledge of statistics to enjoy this book, it is recommendable to get to know better the functions of R. this statistical language often used by data scientists is based on a statistical language that enables programmers to leverage the speed and efficiency of a programming language and the ingenious statistical models.

Unlike many other structured books on the market today, this volume is written in a witty, irreverent tone that can help you get involved in the field more. You can also find self-assessment tests and quizzes to test your knowledge as you continue reading. 

Don’t underestimate the importance of a book written in an engaging tone, especially if the book in question is about statistics. 

• Accessibility: from $18 to $190 (for hardback cover)
• Experience level: intermediate – experience in programming and knowledge of basic concepts of stats is assumed
• Best for: using R in your career

A Probabilistic Theory of Pattern Recognition – Luc Devroye

The last book on our list is the self-contained volume written by Luc Devroye. The chapters of this book cover a huge range of techniques and statistical processes that you will be able to use when working in data science. 

Among the most important ones, you will find nearest neighbor rules, parametric classification, and feature extraction. Just like the previous book, you will be able to find tests and quizzes at the end of every section.

• Accessibility: from $70 to $180
• Experience Level: intermediate
• Best For statisticians and data scientists looking at refining their knowledge

Considerations and Features of the Best Statistics Books for Data Science

As mentioned, statistics are among the most difficult subjects to learn just by reading a book. When it comes down to applying the notion learned in such a practical and evolving field like data science, it is essential to couple up your theoretical knowledge with practical skills. 

However, if you would like to start your journey in this industry from a book, there are some critical characteristics to keep in mind. Even if you have opted for a book different from the ones mentioned above, make sure it boasts the following characteristics – you can do so by checking out the reviews on these books on platforms such as Amazon.

Easy to Understand

Firstly, a book about statistics should be easy to understand. Statistics and data science , just like other fields, use abbreviations and jargon that can make learning more about the field much more challenging. 

However, there are books that avoid such terms at first, just to explain the meaning of certain phrases, abbreviations, or common terms later on. 

Such a learning method can help you arrive at the phase in which you need to apply the notions learned fully prepared. And, when you are applying for a data science job , you will sound like a pro.

Telltale signs of the intelligibility of the book can be found in the volume’s reviews or in the introduction.

Practical Applications Opportunities

Some books are purely theoretical, which are excellent if you are looking at learning statistics for research. However, this field found its foundation on user-generated and real-world data. And these are everything aside from theoretical values.

When you need to apply such notions to data science, the need for practical uses becomes paramount. Indeed, data science is an interdisciplinary field in which data gathered by companies is used to study past trends and foresee future developments.

Making sure that your book encourages you to try the notions learned in real-life scenarios is crucial if you are looking to work for a company or business in the field of data science .

Include Calculation Tips

There is no doubt about the fact that statistics is a field based on calculations, algorithms, and math in general. But some tips can help.

As an example, you could find a book that offers a satisfactory introduction about some statistical or predictive models, without actually teaching you how to extract measurable results. 

While these books might be easy to understand at first, they might leave you without the substantial knowledge needed to put such notions into practice. 

To check whether a textbook has everything you need, look for exercises and problems to solve at the end of each chapter. And of course, it should include some tips on how to use your calculator properly. 

It Is Easily Accessible

Depending on your budget and commitment to learning more about data science , you might be willing to spend more or less on volumes, books, and resources.

However, luckily, some resources are available to all students at all times. So, instead of spending money on buying just one volume and taking a chance on it, you can have a collection of various works that you can use as a reference while entering this field as a professional.

Avoid renting or borrowing these books as having a physical reference to go back to when you have a seemingly insurmountable problem can be time- and energy-saving.

Can Be Used in Combination With Other Learning Methods

Some of the books seen above come with DVDs or CDs that can help you get some of the insights explained in the book in other forms. These methods are particularly useful for visual or auditory learners who need a reference other than a textbook. 

If the volume you have picked does not come with another learning channel, there is no need to discard it altogether. However, in this case, you might consider subscribing to platforms such as Udemy and SkillShare to deepen your knowledge and apply the notions learned.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

• DataCamp: If you are a beginner focused towards building the foundational skills in data science , there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
• IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here )
• MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today ! (To learn more: Check out my full review of the MIT MicroMasters program here )
• Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started : read my article – 6 Proven Ways To Becoming a Data Scientist . In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.

The books mentioned above are the ones you can use to start learning statistics for data science. Every learner might prefer different methods to acquire and retain information about this ever-changing field. 

While these amazing books are well-crafted for you to get a head start in the field, don’t forget to increase your practical knowledge by subscribing to online courses or specialized training. For example, you might like to start applying the notions learned in R or increase your knowledge of useful programming languages like Python. 

Ultimately, a lot depends on the career you would like to build for yourself in this field.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide] . We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

• Calculator tips and tricks. (n.d.). Department of Statistics. https://statweb.stanford.edu/~dlsun/60/calc.html
• Different types of learners: What college students should know. (n.d.). Regionally Accredited College Online and on Campus | Rasmussen College.  https://www.rasmussen.edu/student-experience/college-life/most-common-types-of-learners/
• Press, G. (2014, October 15). A very short history of data science. Forbes.  https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#7ca774f255cf
• Statistics, 11th edition. (2017, January 5). Wiley.com. https://www.wiley.com/en-us/Statistics%2C+11th+Edition-p-9781119254515
• (n.d.). Tilastokeskus. https://www.stat.fi/isi99/proceedings/arkisto/varasto/rams0070.pdf

Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.

Daisy is the founder of DataScienceNerd.com. Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

10 Best Research Books For Qualitative And Quantitative Research

Research books are designed to help you improve your research skills and conduct studies more effectively. So, if you’re on the hunt for research materials, check out our collection of the best research books!

Research methodology comes in many shapes and forms. Regardless of whether you are interested in qualitative or quantitative research, it is essential to find a book that can help you plan your research project adequately.

Research design can vary from hard sciences to social sciences, but data analysis following a case study is usually similar. Therefore, you need a practical guide that can help you complete a research project and finish your research paper—interested in this field of topic? You’ll love our round-up of the best books for quantum physics !

1. Qualitative Research: A Guide To Design And Implementation, 4th Edition

2. research design: qualitative, quantitative, and mixed methods approaches, 4th edition, 3. the research methods knowledge base, 3rd edition, 4. the craft of research, 5. doing your research project: open up study skills, 5th edition, 6. qualitative inquiry and research design: choosing among five approaches, 3rd edition, 7. the essential guide to doing your research project, 2nd edition, 8. introducing research methodology: a beginner’s guide to doing a research project, 2nd edition, 9. the sage handbook of qualitative research, 5th edition, 10. research methods in education, 7th edition, the final word on the best research books, what is the difference between quantitative research and qualitative research, how do i figure out which academic journal to publish my research in.

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Popular Research Books to Read

When someone talks about qualitative research in academia, they refer to research that focuses on overall concepts and takeaways instead of complex numbers. For those conducting academic research, understanding the basics of this process is critical. Qualitative Research: A Guide to Design and Implementation , by Sharan B. Merriam and Elizabeth J. Tisdell, 4th edition, is one of the best books available because it focuses on action research, mixed methods, online data sources, and some of the latest technology that people can use to complete their projects.

A significant portion of this book focuses on data analysis software packages, which have become critically important in an era where publishing in the best academic journals is critical for every successful researcher. Finally, this book explains topics so that nearly everyone can understand.

Suppose you are looking for a book that can teach you the best research methodology. In that case, you will want to check out Research Design: Qualitative, Quantitative, and Mixed Methods Approaches by John W. Creswell, 4th edition. John Creswell is one of the most well-respected writers in case study research.

As books on how to research go, this one on quantitative and qualitative research methods is a great tool that will help you learn the basics of forming a research project in every field. This book covers philosophical assumptions and research projects, theory and research approaches, and conducts an effective literature review. These elements are also crucial in helping you form a step-by-step guide for your upcoming research project, and this book will teach you the basics of data analysis.

The first two editions were already solid, but the third edition of The Research Methods Knowledge Base , by William M.K. Trochim and James P. Donnelly, features many updates to quantitative and qualitative research methods, teaching graduate students the basics of data collection before diving into the details for more advanced learners.

One of the significant advantages of this text is that it is a comprehensive tool that can be used for both undergraduate and graduate-level courses. It has a relatively informal style and conversational feel, which means readers will not be intimidated by walls of text. The research methods it teaches are straightforward, applicable, and relevant to anyone looking to complete a research project in the current era.

The Craft of Research , by Wayne C. Booth, Gregory C. Colomb, and Joseph M. Williams, covers various research approaches that teach everyone the basics of forming a solid research project. In particular, this book focuses on what to do with the data after it has been collected.

People need to think about how their readers will interpret the structure of the paper, proactively anticipating questions they might have. By answering the reader’s questions in the initial version of the paper, it is easier to hold their attention from start to finish.

Of course, one of the most critical questions that must be asked when writing a research paper is, “so, what? Why does this information matter?” Researchers can keep this in mind while writing the introduction and conclusion of the paper so they will have an easier time constructing a powerful academic manuscript that is more likely to be accepted into the top academic journals.

Doing Your Research Project: Open Up Study Skills , by Judith Bell, is a must-read for new researchers looking to make their way in academic research. This book is helpful because it teaches people how to conduct a research project using step-by-step advice. A research project can be daunting for new learners because it’s easy to focus on the final project and feel intimidated before taking the first step.

This book is indispensable because it teaches people everything they need to know to develop a research project, draft a hypothesis, carry out the project, and finalize a research paper after conducting detailed data analysis. Furthermore, this text will dive into common mistakes, pitfalls, and obstacles researchers need to overcome. Time is your most valuable resource, and nobody wants to spend time on trials that will not be relevant to the final project.

Qualitative Inquiry And Research Design: Choose Among Five Approaches , 3rd Edition, Is The Latest In A Line Of Best-Selling Research Books From Creswell. This Book Ties Into People’s Philosophical Underpinnings When Developing A Research Project. It Also Looks At The History Of Various Research Projects, Which Serve As An Example For The Reader.

Overall, there are five traditions in qualitative research; grounded theory, phenomenology, narrative research, case study, and ethnography. Creswell uses an accessible writing style to help the reader understand when to use each of these narratives. Then, he dives into strategies for writing research papers using each of these approaches.

The Essential Guide To Doing Your Research Project by Zina O’Leary is geared more toward young learners. As books on how to research, it focuses on how to develop a research project, analyze data, and write up the results. Every stage of the book is clearly explained, with the author specifying why it is essential to carry out that step correctly.

It also focuses on practical tips and tricks that learners can use to successfully carry out their research projects. The book includes helpful chapter summaries, a complete glossary, and boxed definitions for essential terms that should not be overlooked. The author also has a variety of suggestions for further reading, which is helpful for more advanced learners who may want to pick up a text that is a bit more detailed. Finally, the book also comes with access to a companion website. The website includes journal articles, real projects, worksheets, and podcasts.

Introducing Research Methodology: A Beginner’s Guide to doing a research project, by Uwe Flick is ideal for new researchers. the author guides readers through the fundamentals that underpin a strong research project. He focuses on essential steps, common mistakes, and ways to expedite the research process.

Then, the author dives into some of the most critical skills readers need to have if they want to collect and analyze data properly. he goes into basic organizational tactics that make data easier to interpret, explains how to shorten the analytical process, and dives into real-life quantitative and qualitative research methods. He uses his research as an example, explaining to people how to pull out the essential parts of the research project before writing them up.

The SAGE Handbook of Qualitative Research by Norman K. Denzin and Yvonna S. Lincoln focuses on global research. This text teaches readers how to synthesize existing literature, identify current research, and focus on caps that can be filled. the authors gather contributions from some of the most well-renowned researchers, addressing issues in research projects today. This text focuses more on research regarding social justice therefore, this is better for people in the social sciences.

Research Methods in Education , by Louis Cohen, Lawrence Manon, and Keith Morrison, is essential for students and professional researchers who want to learn how to create a comprehensive research project. It’s broken up into helpful chapters wrapped up by a convenient summary at the end, explaining to readers how to hit the high points.

Research Methods in Education also comes with a helpful companion website that contains PowerPoint slides for every chapter. This book can be read independently and discussed with a classroom full of students. The book has been written at a level that is accessible even to high school students, but the basics can be a helpful review for graduate researchers.

Academic research comes in many shapes and forms, with qualitative and quantitative research having high points; however, the basics are the same across all fields. Researchers need to learn how to develop a hypothesis, put together a research methodology, collect their data, interpret it, and write up their findings.

It can be helpful to use the books about research above to refine your research methods . Each book focuses on a slightly different facet of academic research, so readers need to find the right book to meet their needs. With a substantial text, readers can avoid common mistakes, follow in the footsteps of successful researchers, and increase their chances of writing a solid research paper for school or getting their paper accepted into an academic journal.

Books About Research FAQs

Quantitative research focuses more on numbers and statistics. This type of research is more common in hard sciences such as biology, chemistry, and physics.

Qualitative research focuses more on overall meanings and concepts. This type of research is more common in social sciences such as anthropology, archaeology, and research topics focusing on social justice.

It would help compare prior articles in that academic journal to the article you have written. Most academic journals focus on a specific field, and you need to submit your article to a publication that shares research articles similar to your own. Be sure to consider the prestige of the journal before submitting your paper.

If you enjoyed this round-up of the best research books, you might also like our top 11 essay writing tips for students .

Table of Contents

Top data analytics books of 2024, preparation tips for data analytics , more ways to learn data analytics, best data analytics books 2024: must-read books.

From bestsellers to must-reads, books exemplify the benefits of reading, where you find comfort, knowledge, challenge, and inspiration. Let’s revisit what the famous author Margaret Fuller once said about reading– "Today a reader, tomorrow a leader." Good reads in any field are the roads to a successful journey toward one’s desired destination and beyond.

Today, the field of Data Analytics is surging as high as the U.S. Bureau of Labor Statistics predicts over 23% job growth for data analysts between 2020 and 2030. Nevertheless, this level of fast job growth can be brought upon when data professionals are supplemented with the best resources.

Let’s skim through one of the best resources for success in Data– the must-read Data Analyst books for beginners and experienced professionals. 

Books for data analysts are great ways for professionals who aspire to work in data analysis to learn about subjects, developments, and useful skills. 

Here is a collection of the best data analytics books, from fundamentals to specifics, such as big data, AI, statistical programming languages, etc. 

Storytelling with Data: A Data Visualization Guide for Business Professionals - Cole Nussbaum Knaflic, 2015 

Cole Nussbaummer Knaflic, the CEO and founder of Storytelling With Data, wrote this remarkable data analyst book.

SWD is a book that emphasizes the importance of data storytelling in data analysis. Instead of just placing charts on report pages, data analysts should carefully choose the right chart and create a compelling story to engage their audience.

This piece is one of the must-read data analytics books for beginners, and it provides six useful steps for data storytelling.

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Big Data: A Revolution That Will Transform How We Live, Work, and Think - Viktor Mayer-Schönberger, 2013 

Viktor Mayer and Schönberger, domain experts, discuss the impact of big data on our world. Their book also focuses on the potential positive or negative changes in big data.

This book offers a good understanding of data analytics and its impact on various industries. It prepares readers for the big data revolution that is about to come. The book digs into the broader consequences of big data on societal aspects. It highlights the potential risks associated with digital technology . The book also provides a theoretical overview of big data's importance in various life stages.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython - Wes McKinney, 2011 

The author of the Pandas library's comprehensive book Python for Data Analysis teaches learners the fundamentals of using Python for data manipulation, processing, cleaning, and crunching. Real-world case studies are covered, along with an introduction to data science tools and instructions on how to use Matplotlib to build useful visualizations . Other techniques include loading, cleaning, manipulating, combining, and reshaping data. 

Naked Statistics: Stripping the Dread from the Data - Charles Wheelan, 2012 

The field of statistics is rapidly evolving into a "sexy" discipline, with applications in various fields such as politics, game shows, and medical research. Charles Wheelan's book, Naked Statistics, focuses on the intuition behind statistical analysis, explaining key concepts like inference, correlation, and regression analysis. The book also highlights how biased parties can manipulate data and how creative researchers use natural experiment data to tackle complex questions. It is a valuable resource for those who missed Stats 101.

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking - Tom Fawcett, 2013 

This book, written by Foster Provost and Tom Fawcett, introduces the fundamental concepts of data science and data-analytic thinking. This data analytics book enables readers to extract valuable knowledge and business value from data. It educates readers on how to use data science techniques to help business decision-making and how to think analytically about data.

Business UnIntelligence: Insight and Innovation Beyond Analytics and Big Data - Barry Devlin, 2013 

This book examines business intelligence's past, present, and future while stressing the advantages and disadvantages of conventional methods. Dr. Devlin discusses how big data and analytics have revolutionized business intelligence today, highlighting tried-and-true methods and providing insights into how people, processes, and information interact to create competitive advantage and propel company success. Additionally, he suggests new frameworks and models for companies to enhance their future.

The Hundred-page Machine Learning Book - Andriy Burkov, 2019 

This book offers a succinct introduction to machine learning in just 140 pages, making it appropriate for readers with no prior programming or statistical knowledge. Neural networks, cluster analysis, and supervised and unsupervised learning are among the important ideas covered. The book is short enough to read in one sitting, and the companion wiki provides resources and suggestions for further reading. 

Artificial Intelligence: A Guide for Thinking Humans - Melanie Mitchell, 2019 

Melanie Mitchell, a computer scientist, wrote this book to help us explore the historical background and people behind artificial intelligence. The book specifically draws attention to difficult ideas like neural networks, computer vision models, and NLP. It helps readers who do not require a thorough understanding of AI understand how AI affects data analytics.

Developing Analytic Talent: Becoming a Data Scientist - Vincent Granville, 2014 

With his background in big data, business analytics, and predictive modeling, Granville provides helpful information in his handbook on data science and data scientists. The book discusses the significance of key information for data scientists in big data organizations. It is divided into three sections that address technological applications, case studies, tutorials, career opportunities, and the relationship between data science and other fields. 

Educating decision-makers about specialized solutions and their applications also aids in the development of stronger analytics teams. Granville's more than two decades of industrial experience offer quick suggestions for those wishing to build a data science firm.

Learning R: A Step-by-Step Function Guide to Data Analysis - Richard Cotton, 2013 

This book offers a step-by-step introduction to the R language, making it an invaluable tool for non-technical learners. It covers environments, looping constructions, packages, and data structures. The book then covers the data analysis processes, including loading, cleaning, and converting data. The second section is a priceless resource for individuals unfamiliar with programming languages, as it offers further insight into exploratory analysis and modeling. 

Weapons of Math Destruction - Cathy O'Neil, 2016 

Cathy O'Neil's book on data bias highlights the importance of using big data responsibly. It also discusses the consequences of machines making decisions about our lives and how algorithms often reinforce discrimination. Despite disagreements, the insights are crucial for those new to data science, ensuring future data is used for the benefit of all, not just the privileged.

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 2014 

Big Data analytics offers deeper insights and supports businesses by integrating real-time data feeds and queries. This book, by EMC Education Services, introduces key techniques and tools for Big Data analytics, guiding readers from basic methods to advanced methods like classification, regression analysis, clustering time series, and text analysis. It is suitable for business analysts, database professionals, and college graduates interested in data science or data analysis as a career field .

Too Big to Ignore: The Business Case for Big Data - Phil Simon, 2013 

Phil Simon's book Too Big to Ignore: The Business Case for Big Data explores businesses' and local governments' use of big data. It features case studies and quotes from professionals worldwide, providing valuable insights on turning data into intelligence and making it actionable.

The Elements of Statistical Learning - Trevor Hastie, 2001 

This book thoroughly introduces statistical ideas in various industries, including marketing, biology, finance, and medicine. It employs color pictures for examples and prioritizes concepts over mathematical formulas. Classification trees, neural networks, support vector machines, boosting, and other subjects related to supervised and unsupervised learning are covered in this book, which is an invaluable tool for statisticians and data mining players.

Numsense! Data Science for the Layman: No Math Added - Kenneth Soo, 2017 

This book offers a comprehensive introduction to data science, suitable for non-technical individuals. It provides clear language and visual explanations for algorithms, avoiding complex math. It is valuable for data scientists and beginners as a refresher for communicating work to business partners. The book's algorithm explanations are useful for field communication.

Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Good Decisions - Michael Milton, 2009 

Head First Data Analysis is a book that teaches how to manage and analyze various types of data, including product development, marketing, sales, and entrepreneurship. It provides a unique approach to learning how to convert raw data into a vital business tool. The book uses the latest research in cognitive science and learning theory to create a visually rich format that caters to the brain's workings, making it an efficient way to convert raw data into a valuable business tool.

SQL QuickStart Guide: The Simplified Beginner's Guide to Managing, Analyzing, and Manipulating Data With SQL - Walter Shields, 2015

This book includes a thorough introduction to Structured Query Language (SQL), digital resources such as workbooks and reference guides, and an example database and SQL browser software. It addresses subjects like relational database communication, database structures, important SQL queries, and marketing SQL expertise to prospective employers. The book also offers suggestions on marketing newly acquired SQL abilities to possible employers.

Microsoft Excel Data Analysis and Business Modeling - Wayne L. Winston, 2004 

Wayne Winston, a renowned consultant and business professor, has been teaching clients in the corporate sector and MBA students how to use Microsoft Excel for data analysis, modeling, and decision-making for over a decade. This practical guide offers real-world examples and learn-by-doing exercises to enhance data analysis and modeling expertise. The book is available as a searchable eBook and CD file for download.

The following tips are proven and effective for preparing for data analytics, regardless of the learning method chosen by a data analyst.

• Study the basic concepts
• Pick up one or two programming languages.
• Make time for learning and education via a schedule
• Develop your data analyst skills
• Discover the common tools
• Acquire real-world experience
• Get a mentor

Apart from the preparation tips for Data Analytics, there are some more smart ways to learn data analytics:

• Registering for seminars and courses online
• Taking part in competitions and hackathons
• Going to conferences and networking activities
• Participating in cooperative projects
• Making contributions to open-source projects
• Pursuing advanced coursework and specialization
• Constant education and skill growth via webinars, workshops, and Internet resources
• Participating in mentoring initiatives
• Grabbing hands-on projects and internships

Build your career in Data Analytics with our Data Analyst Master's Program ! Cover core topics and important concepts to help you get started the right way!

Since data professionals are in great demand, there is a dire need for developing a strong foundation. Books were and will always be one of the most authentic sources of acquiring knowledge. The above-mentioned books for data analysts are must-reads and serve as the best picks for 2024 and beyond.

In addition to data analyst books, another strong source is acquiring a good Data Analyst course program from reputable platforms like Simplilearn. Such resources are all-in-one places to check industry trends through expert-led classes. So, stop wasting your precious time and embrace the best of knowledge in the vastness of data!

1. What are the 4 pillars of data analytics?

The four pillars of Data Analytics are descriptive, diagnostic, predictive, and prescriptive. Each pillar enhances a business's understanding of data and ability to drive objectives through insights.

2. Is data analytics a tough job?

Working in data analytics can be tough, particularly if you're not experienced in programming, statistics, or data processing. But if you make conscious efforts, it can also be enjoyable.

With dedication, the right resources (Data analyst books, courses from good learning platforms), stress management, and a strategic approach, data analysts can have a fruitful and rewarding career.

3. What is the salary of a data analyst in India?

India's average annual salary for Data Analysts is ₹6.4 Lakhs, based on 97.1k salary estimates, with a range of ₹1.8 Lakhs to ₹12.8 Lakhs, with 0-6 years of experience.

4. Will AI replace data analysts?

Although AI provides useful tools, it can only support the efforts of a data analyst. Businesses will keep spending money on data analysts who can implement artificial intelligence technologies securely and confidently in the future. Rather than completely replacing data analysts, AI will advance, enhance, and make them more efficient.

5. Is data analyst still in demand?

Data analysts are in high demand in India, with 97,000 unfilled jobs annually. Demand has led to a 45% increase in Indian data analyst jobs. The U.S. Bureau of Labor Statistics predicts over 23% job growth globally for analyst professionals between 2022 and 2032.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Recommended Reads

Data Analyst Resume Guide

Data Analyst Job Description: What Does a Data Analyst Do?

How to Become a Data Analyst? Navigating the Data Landscape

Business Intelligence Career Guide: Your Complete Guide to Becoming a Business Analyst

How to Become a Data Analyst?

Data Analyst vs. Data Scientist: The Ultimate Comparison

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The Top 16 Best Data Management Books to Read in 2023

• Best Practices ,

A compilation of the best data management books on technique and methodology written by some of the most prominent experts in the field.

With this in mind, our editors have done the research for you, having reviewed many of these books. We’ve carefully selected the top data management books based on relevance, popularity, review ratings, publish date, and ability to add business value.

Below you will find a library of books from recognized leaders, experts, and technology professionals in the field. From data governance to master data management, these publications have something to offer even the most tenured data and analytics professional.

The Best Data Management Books

Dama-dmbok: data management body of knowledge (2nd edition), our take: this udemy power bi training has more than 46,000 ratings and 4.6 stars. by the end, you will be able to analyze data from different data sources and create their own datasets..

Data Management: Databases and Organizations

Data Management for Researchers: Organize, maintain and share your data for research success (Research Skills)

Master Data Management and Data Governance, 2/E

The DAMA Guide to the Data Management Body of Knowledge

Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program (The Morgan Kaufmann Series on Business Intelligence)

Data Management Using Stata: A Practical Handbook

Non-Invasive Data Governance

Data Stewardship: An Actionable Guide to Effective Data Management and Data Governance

Data Mining: Concepts and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems)

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling

Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems

Enterprise Master Data Management: An SOA Approach to Managing Core Information

Multi-Domain Master Data Management: Advanced MDM and Data Governance in Practice

Master Data Management MDM: An Integration Blueprint

The Data Management Toolkit: A Step-by-Step Implementation Guide for the Pioneers of Data Management

NOW READ: The Best Data Management Courses and Online Training

This article was written by Tim King on October 13, 2022

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Tim is Solutions Review's Executive Editor and leads coverage on data management and analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in Data Management, Tim is a recognized industry thought leader and changemaker. Story? Reach him via email at tking@solutionsreview dot com.

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The Data Book

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The Data Book: Collection and Management of Research Data is the first practical book written for researchers and research team members covering how to collect and manage data for research. The book covers basic types of data and fundamentals of how data grow, move and change over time. Focusing on pre-publication data collection and handling, the text illustrates use of these key concepts to match data collection and management methods to a particular study, in essence, making good decisions about data.

The first section of the book defines data, introduces fundamental types of data that bear on methodology to collect and manage them, and covers data management planning and research reproducibility. The second section covers basic principles of and options for data collection and processing emphasizing error resistance and traceability. The third section focuses on managing the data collection and processing stages of research such that quality is consistent and ultimately capable of supporting conclusions drawn from data. The final section of the book covers principles of data security, sharing, and archival. This book will help graduate students and researchers systematically identify and implement appropriate data collection and handling methods.

TABLE OF CONTENTS

Chapter 1 | 22  pages, collecting and managing research data, chapter 2 | 23  pages, defining data and information, chapter 3 | 19  pages, deciding, defining, and documenting data to be collected, chapter 4 | 15  pages, data management planning, chapter 5 | 20  pages, fundamental dynamic aspects of data, chapter 6 | 21  pages, data observation and recording, chapter 7 | 12  pages, good data recording practices applicable to man and machine, chapter 8 | 20  pages, getting data into electronic format, chapter 9 | 21  pages, data structures, chapter 10 | 18  pages, data processing, chapter 11 | 21  pages, designing and documenting data flow and workflow, chapter 12 | 20  pages, selecting software for collecting and managing data, chapter 13 | 19  pages, the data management quality system, chapter 14 | 13  pages, calculating the time and cost for data collection and processing, chapter 15 | 18  pages, research data security, chapter 16 | 8  pages, data ownership, stewardship, and sharing, chapter 17 | 13  pages, data archival.

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The Data Book: Collection and Management of Research Data (Chapman & Hall/CRC Interdisciplinary Statistics) 1st Edition

The Data Book: Collection and Management of Research Data is the first practical book written for researchers and research team members covering how to collect and manage data for research. The book covers basic types of data and fundamentals of how data grow, move and change over time. Focusing on pre-publication data collection and handling, the text illustrates use of these key concepts to match data collection and management methods to a particular study, in essence, making good decisions about data.

The first section of the book defines data, introduces fundamental types of data that bear on methodology to collect and manage them, and covers data management planning and research reproducibility. The second section covers basic principles of and options for data collection and processing emphasizing error resistance and traceability. The third section focuses on managing the data collection and processing stages of research such that quality is consistent and ultimately capable of supporting conclusions drawn from data. The final section of the book covers principles of data security, sharing, and archival. This book will help graduate students and researchers systematically identify and implement appropriate data collection and handling methods.

• ISBN-10 1498742246
• ISBN-13 978-1498742245
• Edition 1st
• Publisher Chapman and Hall/CRC
• Publication date July 12, 2017
• Part of series Chapman & Hall/CRC Interdisciplinary Statistics
• Language English
• Dimensions 7 x 0.75 x 10 inches
• Print length 354 pages
• See all details

Editorial Reviews

About the author.

Meredith Zozus is an Associate Professor in the Department of Biomedical Informatics at the University of Arkansas for Medical Sciences. She has spent more than two decades managing data for research and studying methodology for doing so. She has worked in data management for life sciences, engineering, and biomedical research. She received her undergraduate and master’s degrees in Nuclear Engineering at North Carolina State University, led data management operations at the Duke Clinical Research Institute, and later Clinical Research Informatics at the Duke Translational Medicine Institute. Dr. Zozus received her PhD in Health Informatics at the University of Texas at Houston School of Biomedical Informatics.

Product details

• Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (July 12, 2017)
• Language ‏ : ‎ English
• Hardcover ‏ : ‎ 354 pages
• ISBN-10 ‏ : ‎ 1498742246
• ISBN-13 ‏ : ‎ 978-1498742245
• Item Weight ‏ : ‎ 1.44 pounds
• Dimensions ‏ : ‎ 7 x 0.75 x 10 inches
• #2,263 in Business Statistics
• #2,391 in Data Mining (Books)
• #2,675 in Database Storage & Design

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Managing Your Research Data and Documentation

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In the behavioral sciences today, there is increasing emphasis on transparency, and the need for research studies to be made replicable.

In this book, Kathy R. Berenson presents a straightforward approach to managing and documenting one's data so that other researchers can repeat the study.

While data management may seem intimidating to new researchers, this guide shows how easy it can (and should) be. The first chapter presents a basic structure of folders and subfolders for organizing data files, and then each subsequent chapter delves into details for a specific folder.

Step by step, readers learn to label and archive different kinds of project documents and data files, including original, processed, and working data. Readers also learn to write command codes showing exactly how the original data are analyzed.

Examples illustrate how to document the most common types of research, and since major research funders now require recipients to meet strict data handling standards, this resource will foster a vital career skill for students.

Series Foreword

• Introduction
• Your Project Files
• Your Data Files
• Your Command Files
• Your Replication Documentation
• Appendix A. Source Data and Metadata
• Appendix B. Working With "Tall" Data Files
• Appendix C. Data Entry
• Appendix D. Labeling and Renaming Many Variables Efficiently
• Appendix E. Importing Data Files
• Appendix F. Merging Data Files
• Appendix G. Estimating Missing Values

About the Author

About the Series Editor

Kathy R. Berenson, PhD, is an assistant professor of psychology at Gettysburg College and a clinical psychologist who conducts research focused on cognitive/affective and interpersonal factors in psychological difficulties. She has many years of experience managing data for large, grant-funded studies and mentoring undergraduate research in the fields of personality and psychopathology.

• Choice Outstanding Academic Title

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Advancing Data Science Education in K-12 Research and Theory for Teaching and Learning

Description.

Advancing Data Science Education in K-12 offers a highly accessible, research-based treatment of the foundations of data science education and its increasingly vital role in K-12 instructional content.

As federal education initiatives and developers of technology-enriched curricula attempt to incorporate the study of data science—the generation, capture, and computational analysis of data at large scale—into schooling, a new slate of skills, literacies, and approaches is needed to ensure an informed, effective, and unproblematic deployment for young learners. Friendly to novices and experts alike, this book provides an authoritative synthesis of the most important research and theory behind data science education, its implementation into K-12 curricula, and clarity to the distinctions between data literacy and data science. Learning with and about data hold equal and interdependent importance across these chapters, conveying the variety of issues, situations, and decision-making integral to a well-rounded, critically minded perspective on data science education.

Students and faculty in teaching, leadership, curriculum development, and educational technology programs will come away with essential insights into the breadth of our current and future engagements with data; the real-world opportunities and challenges data holds when taught in conjunction with other subject matter in formal schooling; and the nature of data as a human and societal construct that demands new competencies of today’s learners.

Table of Contents

1. Data Everywhere  2. Data Literacy, Data Science, and Terms that Trip Us Up  3. Humans Thinking About Data  4. Teaching Data Science in Schools  5. Learning Data Science Outside of Schools  6. Expansive Views for Data Science Education  7. Onward - A Data Science Education Research Ecosystem

Victor R. Lee is Associate Professor in the Graduate School of Education at Stanford University, USA. He is Past-President of the International Society of the Learning Sciences and was Chair of the Advanced Technologies for Learning SIG for the American Educational Research Association. His previous books are Reconceptualizing Libraries: Perspectives from the Information and Learning Sciences and Learning Technologies and the Body: Integration and Implementation in Formal and Informal Learning Environments . He received his PhD in Learning Sciences from Northwestern University and holds bachelor's degrees in Cognitive Science and Mathematics from the University of California San Diego.

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• iSchool Connect

Jiangping Chen

Jiangping Chen CV

Interim Executive Associate Dean and Visiting Professor

PhD, Information Transfer, Syracuse University

Room 112E, 501 E. Daniel St.

(217) 333-3280

[email protected]

• https://idealabunt.github.io/home/index.html

Other professional appointments

2023-2024 Co-Chairs, ALISE Council of Deans, Directors, and Chairs

Research focus

Honors and awards.

• Regents Professor, University of North Texas, 2024
• Outstanding Department Award, University of North Texas, 2022
• LIBINF Top 10 Cited Article (IF 2019-2020), ELSEVIER, 2022
• Star Performer Award, University of North Texas, 2021
• Recognition of Service Award, Association for Computing Machinery (ACM), 2018
• ISI/ASIS&T Doctoral Dissertation Proposal Award, 2003

Dr. Chen is currently the interim executive associate dean and visiting professor at the iSchool. Before joining UIUC in August 2024, she was Regents professor and the chair of the Department of Information Science in the College of Information at the University of North Texas (UNT). She conducts interdisciplinary research, spanning information science, data science, and health informatics. She is the founder of UNT's Intelligent Information Access (IIA) Lab, which explores methods for access, interaction, and analysis of large, distributed, heterogeneous, multimedia, and multilingual information. 

Her professional contributions include authoring numerous publications, including a monograph on multilingual digital libraries,  journal articles, book chapters, and conference proceedings as well as giving invited presentations and talks. She served as the editor-in-chief for The Electronic Library for seven years and as chair of the Joint Conference on Digital Libraries (JCDL) in 2018. Dr. Chen holds a PhD in information transfer from Syracuse University, a master's degree in information science from the Library of Chinese Academy of Sciences, and a bachelor's degree in information science from Wuhan University.

Publications & Papers

Selected publications

Ogbadu-Oladapo, L., Chung, H., Li, J., & Chen, J. (2023). An investigation of the use of theories in misinformation studies. Proceedings of 2023 annual conference of the American Society for Information Science and Technology , London, UK, October 27-31. https://doi.org/10.1002/pra2.790 . Nguyen, H., Ogbadu-Oladapo, L., Irhamni, A., Chen, H., & Chen, J. (2023). Fighting misinformation: where are we and where to go? Proceedings of iConference 2023 , Barcelona, Spain, March 27-29. https://doi.org/10.1007/978-3-031-28035-1_27 . Wu, A. & Chen, J. (2022). Sustaining multilinguality: case studies of two multilingual digital libraries. The Electronic Library , 40(6), 625-645. https://doi.org/10.1108/EL-03-2022-0061 . Chen, H., Wu, L., Lu, W., Chen, J., & Ding, J. (2022). A comparative study of automated legal text classification using random forests and deep learning. Information Processing and Management , 59(2). https://doi.org/10.1016/j.ipm.2021.102798 . Chen, H., Nguyen, H., & Chen, J. (2021). Demystifying COVID-19 publications: researchers, topics, diseases, and therapeutics. Journal of the Medical Library Association , 109(3), 395-405. https://doi.org/10.5195/jmla.2021.1141 . Chen, J. (2020). Beyond information organization and evaluation: how can information scientists contribute to independent thinking. Data and Information Management , 4(3), 171-176. https://doi.org/10.2478/dim-2020-0017 . Wang, C., Huang, R., Li, J., & Chen, J. (2020). Towards better information services: A framework for immigrant information needs and library services. Library and Information Science Research , 42(1), https://doi.org/10.1016/j.lisr.2019.101000 . Brenda, R., Knudson, R., Chen, J., Cao, G., & Wang, X. (2018). Metadata records machine translation combining multi-engine outputs with limited parallel data. Journal of the Association for Information Science and Technology . 69(1), 47-59, 2018. https://doi.org/10.1002/asi.23925 . Chen, J. 2016. Multilingual Access And Services For Digital Collections . Santa Barbara, CA: Libraries Unlimited. https://www.amazon.com/Multilingual-Access-Services-Digital-Collections/dp/1440839549 .   

• Data Analytics and Human Centered Data
• Digital Libraries
• Information Retrieval
• Natural Language Processing, Text Mining, Text Analysis, Computational Linguistics
• Privacy, Security, and Trust

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• Full List »

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September 2, 2024

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The dark side of egg donation: Racial discrimination in fees and unspoken health risks

by Taylor & Francis

White women are paid up to eight times more for their eggs than Black women in the U.S., according to data uncovered by the author of a new book which exposes the lesser-known ramifications of egg donation.

Eggonomics by Diane M. Tober reveals statistics and stories which illuminate the rarely-told, complex realities of egg donation, and airs personal accounts of disturbing power imbalances within the industry. She takes a microscope to the industry across the globe, but in particular in the U.S. and Spain.

Donor disparities

Tober finds shocking disparities between the top rates of pay for donors by race. In her study of over 900 egg donors, the top rate for White donors in the U.S. was around $100k, relative to Black donors who received a top rate of only $12k. In one case, she found, eggs from a "perfect donor" were valued by prospective parents as worth $250,000. This donor was Chinese American and had a master's degree from Massachusetts Institute of Technology.

Exposing the perspective of the industry, Tober says, "Donor profiles are the marketing tools used to attract intended parents, and those women who possess desired social and physical traits will sell faster to a wide range of intended parents than others."

Eggonomics identifies the precarious financial position of many donors and uncovers how many have been motivated to donate eggs following the 'cost of living' crisis, or due to high student debt.

In the context of the pursuit of the ideal genetics, and the market value attached to them, the word "eugenics" crops up repeatedly. One donor said, "…from me they wanted really, really specific things, and I was feeling like, 'Oh my god. This is eugenics.'"

Writing in Eggonomics, Tober summarizes the situation for many donors, "Donors are not industry-serving machines producing products by the double or triple dozens. Sometimes they donate eggs out of financial need, or because they want to help others, and most have mixed motivations. Wherever they are in the world, they are human beings with their own lives, emotions, and physical bodies they put on the line—for a whole range of complicated reasons—to help create families for the more affluent.

"Many come to find that the abundance of eggs they counted on in their youth are not available to them later when they are ready to start their own families, and the only biological children they will ever have are being raised by someone else. Until extensive longitudinal research into egg donor health and well-being is conducted, and care for donors is prioritized, there will be no justice for egg donors or the families they help create."

Tober also raises questions about whether donors may be at an increased risk of health problems following medication and subsequent donation—especially those who donate repeatedly. She includes accounts of young, healthy donors developing health problems soon after donating.

The author notes that a disproportionate number seem to have no other relevant risk factors, and that some of them have physicians who suspected a causal link between egg donation and subsequent illness, and she calls for more research to be done to fully understand all the risks.

Weaving between the perils and the joys egg donors experience, Tober recognizes the complexities involved in being a donor. She calls for sweeping changes in policy and to the industry to both improve transparency and enhance egg donor rights and safety.

To that end, in Eggonomics, Tober sets out a list of recommendations for the industry:

• Recognize donors' rights to truthful, thorough, and standardized informed consent, including how any pre-existing conditions (e.g., PCOS, endometriosis) may be aggravated by the hormones used;
• Reduce potential for donor undue inducement by eliminating financial incentives from donor recruitment advertisements and materials;
• Implement independent legal counsel for donors with enforceable terms when donor contracts are violated or when they experience medical harm;
• Recognize the rights of donors as primary patients by implementing best practices and providing donors with their medical records ;
• Recognize "no means no" when a donor expresses even once that she is not interested in doing another cycle and enforce ASRM six-cycle limits;
• Rescind taxation on donor compensation;
• Implement practices to recognize all donors and intended parents have a right to be treated with respect regardless of race, gender identity , sexual orientation , and patient status;
• Recognize the rights of donor-conceived people to have access to medical information and information about their identities;
• Establish a three-pronged donor registry to track donor cycles, enforce live birth limits, and enable immediate and long-term follow-up on donor health.

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