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The world is being transformed by data and data-driven analysis is rapidly becoming an integral part of science and society. Stanford Data Science is a collaborative effort across many departments in all seven schools. We strive to unite existing data science research initiatives and create interdisciplinary collaborations, connecting the data science and related methodologists with disciplines that are being transformed by data science and computation.

Our work supports research in a variety of fields where incredible advances are being made through the facilitation of meaningful collaborations between domain researchers, with deep expertise in societal and fundamental research challenges, and methods researchers that are developing next-generation computational tools and techniques, including:

Data Science for Wildland Fire Research

In recent years, wildfire has gone from an infrequent and distant news item to a centerstage isssue spanning many consecutive weeks for urban and suburban communities. Frequent wildfires are changing everyday lives for California in numerous ways -- from public safety power shutoffs to hazardous air quality -- that seemed inconceivable as recently as 2015. Moreover, elevated wildfire risk in the western United States (and similar climates globally) is here to stay into the foreseeable future. There is a plethora of problems that need solutions in the wildland fire arena; many of them are well suited to a data-driven approach.

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Data Science for Economics

Many of the most pressing questions in empirical economics concern causal questions, such as the impact, both short and long run, of educational choices on labor market outcomes, and of economic policies on distributions of outcomes. This makes them conceptually quite different from the predictive type of questions that many of the recently developed methods in machine learning are primarily designed for.

Data Science for Education

Educational data spans K-12 school and district records, digital archives of instructional materials and gradebooks, as well as student responses on course surveys. Data science of actual classroom interaction is also of increasing interest and reality.

Data Science for Human Health

It is clear that data science will be a driving force in transitioning the world’s healthcare systems from reactive “sick-based” care to proactive, preventive care.

Data Science for Humanity

Our modern era is characterized by massive amounts of data documenting the behaviors of individuals, groups, organizations, cultures, and indeed entire societies. This wealth of data on modern humanity is accompanied by massive digitization of historical data, both textual and numeric, in the form of historic newspapers, literary and linguistic corpora, economic data, censuses, and other government data, gathered and preserved over centuries, and newly digitized, acquired, and provisioned by libraries, scholars, and commercial entities.

Data Science for Linguistics

The impact of data science on linguistics has been profound. All areas of the field depend on having a rich picture of the true range of variation, within dialects, across dialects, and among different languages. The subfield of corpus linguistics is arguably as old as the field itself and, with the advent of computers, gave rise to many core techniques in data science.

Data Science for Nature and Sustainability

Many key sustainability issues translate into decision and optimization problems and could greatly benefit from data-driven decision making tools. In fact, the impact of modern information technology has been highly uneven, mainly benefiting large firms in profitable sectors, with little or no benefit in terms of the environment. Our vision is that data-driven methods can — and should — play a key role in increasing the efficiency and effectiveness of the way we manage and allocate our natural resources.

Ethics and Data Science

With the emergence of new techniques of machine learning, and the possibility of using algorithms to perform tasks previously done by human beings, as well as to generate new knowledge, we again face a set of new ethical questions.

The Science of Data Science

The practice of data analysis has changed enormously. Data science needs to find new inferential paradigms that allow data exploration prior to the formulation of hypotheses.

Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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Ten Research Challenge Areas in Data Science

To drive progress in the field of data science, the authors propose 10 challenge areas for the research community to pursue. because data science is broad, with methods drawing from computer science, statistics, and other disciplines, these challenge areas speak to the breadth of issues..

To drive progress in the field of data science, the authors propose 10 challenge areas for the research community to pursue. Because data science is broad, with methods drawing from computer science, statistics, and other disciplines and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. The authors preface their enumeration with metaquestions about whether data science is a discipline. They then describe each of the 10 challenge areas. The goal of this article is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving.

Although data science builds on knowledge from computer science, engineering, mathematics, statistics, and other disciplines, data science is a unique field with many mysteries to unlock: fundamental scientific questions and pressing problems of societal importance.

This article enumerates 10 areas of research in which to make progress to advance the field of data science. The goal is to start a discussion on what could constitute a basis for a research agenda in data science, while recognizing that the field of data science is still evolving.

Ten Research Areas

What are the research challenge areas that drive the study of data science? Here is a list of 10. They are not in any priority order, and some of them are related to each other. They are phrased as challenge areas, not challenge questions; each area suggests many questions. They are not necessarily the top 10, but they are a good 10 to start the community discussing what a broad research agenda for data science might look like.

1. Scientific Understanding of Learning, Especially Deep Learning Algorithms

As much as we admire the astonishing successes of deep learning, we still lack a scientific understanding of why deep learning works so well, though we are making headway.

2. Causal Reasoning

Machine learning is a powerful tool to find patterns and to examine associations and correlations, particularly in large data sets. While the adoption of machine learning has opened many fruitful areas of research in economics, social science, public health, and medicine, these fields require methods that move beyond correlational analyses and can tackle causal questions. A rich and growing area of current study is revisiting causal inference in the presence of large amounts of data.

3. Precious Data

Data can be precious for one of three reasons: the data set is expensive to collect; the data set contains a rare event (low signal-to-noise ratio); or the data set is artisanal—small, task-specific, or targets a limited audience.

4. Multiple, Heterogeneous Data Sources

For some problems, we can collect lots of data from different data sources to improve our models and to increase knowledge.

5. Inferring From Noisy or Incomplete Data

The real world is messy, and we often do not have complete information about every data point. Yet, data scientists want to build models from such data to do prediction and inference. This long-standing problem in statistics comes to the fore as (1) the volume of data, especially about people, that we can generate and collect grows unboundedly; (2) the means of generating and collecting data is not under our control, for example, data from mobile phone and web apps vary—by design—across different users and across different populations; and (3) many sectors, from finance to retail to transportation, embrace the desire to do real-time personalization.

6. Trustworthy AI

We have seen rapid deployment of systems using artificial intelligence (AI) and machine learning in critical domains such as autonomous vehicles, criminal justice, health care, hiring, housing, human resource management, law enforcement, and public safety, where decisions taken by AI agents directly impact human lives. Consequently, there is an increasing concern if these decisions can be trusted to be correct, fair, ethical, interpretable, private, reliable, robust, safe, and secure, especially under adversarial attacks.

7. Computing Systems for Data-Intensive Applications

Traditional designs of computing systems have focused on computational speed and power: the more cycles, the faster the application can run. Today, the primary focus of applications, especially in the sciences, is data. Novel special-purpose processors are now commonly found in large data centers.

8. Automating Front-End Stages of the Data Life Cycle

While the excitement in data science is due largely to the successes of machine learning, and more specifically deep learning, before we get to use machine learning algorithms, we need to prepare the data for analysis. The early stages in the data life cycle are still labor intensive and tedious. Data scientists, drawing on both computational and statistical tools, need to devise automated methods that address data collection, data cleaning, and data wrangling, without losing other desired properties.

For many applications, the more data we have, the better the model we can build. One way to get more data is to share data, for example, multiple parties pool their individual data sets to build collectively a better model than any one party can build. However, in many cases, due to regulation or privacy concerns, we need to preserve the confidentiality of each party’s data set.

Data science raises new ethical issues. They can be framed along three axes: (1) the ethics of data: how data are generated, recorded, and shared; (2) the ethics of algorithms: how artificial intelligence, machine learning, and robots interpret data; and (3) the ethics of practices: devising responsible innovation and professional codes to guide this emerging science and to define institutional review board criteria and processes specific for data.

Read the full story here.

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Data Science and Engineering: Research Areas

Author: guest contributor.

Data science has emerged as an independent domain in the decade starting 2010 with the explosive growth in big data analytics, cloud, and IoT technology capabilities. A data scientist requires fundamental knowledge in the areas of computer science, statistics, and machine learning, which he may use to solve problems in a variety of domains. We may define data science as a study of scientific principles that describe data and their inter-relationship. Some of the current areas of research in Data Science and Engineering are categorized and enumerated below : 

1. Artificial Intelligence / Machine Learning :

While human beings learn from experience, machines learn from data and improve their accuracy over time. AI applications attempt to mimic human intelligence by a computer, robot, or other machines. AI/ML has brought disruptive innovations in business and social life. One of the emerging areas in AI is generative artificial intelligence algorithms that use reinforcement learning for content creation such as text, code, audio, images, and videos. The AI based chatbot ‘ChatGPT’ from Open AI is a product in this line. ChatGPT can code computer programs, compose music, write short stories and essays, and much more!

2. Automation: 

Some of the research areas in automation include public ride-share services (e.g., uber platform), self-driving vehicles, and automation of the manufacturing industry. AI/ML techniques are widely used in industries for the identification of unusual patterns in sensor readings from machinery and equipment for the detection or prevention of malfunction.

3. Business:

As we know, social media provide opportunities for people to interact, share, and participate in numerous activities in a massive way. A marketing researcher may analyze this data to gain an understanding of human sentiments and behavior unobtrusively, at a scale unheard of in traditional marketing. We come across personalized product recommender systems almost every day. Content-based recommender systems guess user’s intentions based on the history of their previous activities. Collaborative recommender systems use data mining techniques to make personalized product recommendations, during live customer transactions, based on the opinions of customers with similar profile. 

Data science finds numerous applications in finance like stock market analysis; targeted marketing; and detection of unusual transaction patterns, fraudulent credit card transactions, and money laundering. Financial markets are complex and chaotic. However, AI technologies make it possible to process massive amounts of real-time data, leading to accurate forecast and trade. Stock Hero, Scanz, Tickeron, Impertive execution, and Algoriz are some of the AI based products for stock market prediction. 

4. Computer Vision and NLP:

AI/ML models are extensively used in digital image processing, computer vision, speech recognition, and natural language processing (NLP). In image processing, we use mathematical transformations to enhance an image. These transformations typically include smoothing, sharpening, contrasting, and stretching. From the transformed images we can extract various types of features - edges, corners, ridges, and blobs/regions. The objective of computer vision is to identify objects (or images). To achieve this, the input image is processed, features are extracted, and using the features the object is classified (or identified).

Natural language processing techniques are used to understand human language in written or spoken form and translate it to another language or respond to commands. Voice-operated GPS systems, translation tools, speech-to-text dictation, and customer service chatbots are all applications of NLP. Siri, and Alexa are popular NLP products. 

5. Data Mining

Data mining is the process of cleaning and analyzing data to identify hidden patterns and trends that are not readily discernible from a conventional spread sheet. Building models for classification and clustering in high dimensional, streaming, and/or big data space is an area that receives much attention from researchers. Network-graph based algorithms are being developed for representing and analyzing the interactions in social media such as facebook, twitter, linkedin, instagram, and web sites. 

6. Data Management:

Information storage and retrieval is area that is concerned with effective and efficient storage and retrieval of digital documents in multiple data formats, using their semantic content. Government regulations and individual privacy concerns necessitate cryptographic methods for storing and sharing data such as secure multi-party computation, homomorphic encryption, and differential privacy. 

Data-stream processing needs specialized algorithms and techniques for doing computations on huge data that arrive fast and require immediate processing – e.g., satellite images, data from sensors, internet traffic, and web searches. Some of the other areas of research in data management include big data databases, cloud computing architectures, crowd sourcing, human-machine interaction, and data governance. 

7. Data visualization

Visualizing complex, big, and / or streaming data, such as the onset of a storm or a cosmic event, demands advanced techniques. In data visualization, the user usually follows a three-step process - get an overview of the data, identify interesting patterns, and drill-down for final details. In most cases, the input data is subjected to mathematical transformations and statistical summarizations. The visualization of the real physical world may be further enhanced using audio-visual techniques or other sensory stimuli delivered by technology. This technique is called augmented reality. Virtual reality provides a computer-generated virtual environment giving an immersive experience to the users. For example, ‘Pokémon GO’ that allows you play the game Pokémon is an AR product released in 2016; Google Earth VR is VR product that ‘puts the whole world within your reach’.

8. Genetic Studies:

Genetic studies are path breaking investigation of the biological basis of inherited and acquired genetic variation using advanced statistical methods. The human genome project (1990 – 2003) produced a genome sequence that accounted for over 90% of the human genome. The project cost was about USD 3 billion. The data underlying a single human genome sequence is about 200 gigabytes. The digital revolution has made astounding possibilities to pinpoint human evolution with marked accuracy. Note that the cost of sequencing the entire genome of a human cell has fallen from USD 100,000,000 in the year 2000 to USD 800 in 2020! 

9. Government:

Governments need smart and effective platforms for interacting with citizens, data collection, validation, and analysis. Data driven tools and AI/ML techniques are used for fighting terrorism, intervention in street crimes, and tackling cyber-attack. Data science also provides support in rendering public services, national and social security, and emergency responses.

10. Healthcare:

The most important contribution of data science in the pharmaceutical industry is to provide computational support for cost effective drug discovery using AI/ML techniques. AI/ML supports medical diagnosis, preventive care, and prediction of failures based on historical data. Study of genetic data helps in the identification of anomalies, prediction of possible failures and personalized drug suggestions, e.g., in cancer treatment. Medical image processing use data science techniques to visualize, interrogate, identify, and treat deformities in the internal organs and systems.

Electronic health records (EHR) are concerned with the storage of data arriving in multiple formats, data privacy (e.g., conformance with HIPAA privacy regulations), and data sharing between stakeholders. Wearable technology provides electronic devices and platforms for collecting and analyzing data related to personal health and exercise – for example, Fitbit and smartwatches. The Covid-19 pandemic demonstrated the power of data science in monitoring and controlling an epidemic as well as developing drugs in record time. 

11. Responsible AI : 

AI systems support complex decision making in various domains such as autonomous vehicles, healthcare, public safety, HR practices etc. To trust the AI systems, their decisions must be reliable, explainable, accountable, and ethical. There is ongoing research on how these facets can be built into AI algorithms.

This book appears in the book series Transactions on Computer Systems and Networks . If you are interested in writing a book in the series,  then please click here to complete and submit the relevant form.

Srikrishnan Sundararajan, PhD in Computer Applications, is a retired senior professor of business analytics, Loyola institute of business administration, Chennai, India. He has held various tenured and visiting professorships in Business Analytics, and Computer Science for over 10 years. He has 25 years of experience as a consultant in the information technology industry in India and the USA, in information systems development and technology support. 

He is the author of the forthcoming book ‘Multivariate Analysis and Machine Learning Techniques - Feature Analysis in Data Science using Python’ published by Springer Nature (ISBN.9789819903528). This book offers a comprehensive first-level introduction to data science including python programming, probability and statistics, multivariate analysis, survival analysis, AI/ML, and other computational techniques.

Guest Contributors include Springer Nature staff and authors, industry experts, society partners, and many others. If you are interested in being a Guest Contributor, please contact us via email: [email protected] .

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37 Research Topics In Data Science To Stay On Top Of

• February 22, 2024

As a data scientist, staying on top of the latest research in your field is essential.

The data science landscape changes rapidly, and new techniques and tools are constantly being developed.

To keep up with the competition, you need to be aware of the latest trends and topics in data science research.

In this article, we will provide an overview of 37 hot research topics in data science.

We will discuss each topic in detail, including its significance and potential applications.

These topics could be an idea for a thesis or simply topics you can research independently.

Stay tuned – this is one blog post you don’t want to miss!

37 Research Topics in Data Science

1.) predictive modeling.

Predictive modeling is a significant portion of data science and a topic you must be aware of.

Simply put, it is the process of using historical data to build models that can predict future outcomes.

Predictive modeling has many applications, from marketing and sales to financial forecasting and risk management.

As businesses increasingly rely on data to make decisions, predictive modeling is becoming more and more important.

While it can be complex, predictive modeling is a powerful tool that gives businesses a competitive advantage.

2.) Big Data Analytics

These days, it seems like everyone is talking about big data.

And with good reason – organizations of all sizes are sitting on mountains of data, and they’re increasingly turning to data scientists to help them make sense of it all.

But what exactly is big data? And what does it mean for data science?

Simply put, big data is a term used to describe datasets that are too large and complex for traditional data processing techniques.

Big data typically refers to datasets of a few terabytes or more.

But size isn’t the only defining characteristic – big data is also characterized by its high Velocity (the speed at which data is generated), Variety (the different types of data), and Volume (the amount of the information).

Given the enormity of big data, it’s not surprising that organizations are struggling to make sense of it all.

That’s where data science comes in.

Data scientists use various methods to wrangle big data, including distributed computing and other decentralized technologies.

With the help of data science, organizations are beginning to unlock the hidden value in their big data.

By harnessing the power of big data analytics, they can improve their decision-making, better understand their customers, and develop new products and services.

3.) Auto Machine Learning

Auto machine learning is a research topic in data science concerned with developing algorithms that can automatically learn from data without intervention.

This area of research is vital because it allows data scientists to automate the process of writing code for every dataset.

This allows us to focus on other tasks, such as model selection and validation.

Auto machine learning algorithms can learn from data in a hands-off way for the data scientist – while still providing incredible insights.

This makes them a valuable tool for data scientists who either don’t have the skills to do their own analysis or are struggling.

4.) Text Mining

Text mining is a research topic in data science that deals with text data extraction.

This area of research is important because it allows us to get as much information as possible from the vast amount of text data available today.

Text mining techniques can extract information from text data, such as keywords, sentiments, and relationships.

This information can be used for various purposes, such as model building and predictive analytics.

5.) Natural Language Processing

Natural language processing is a data science research topic that analyzes human language data.

This area of research is important because it allows us to understand and make sense of the vast amount of text data available today.

Natural language processing techniques can build predictive and interactive models from any language data.

Natural Language processing is pretty broad, and recent advances like GPT-3 have pushed this topic to the forefront.

6.) Recommender Systems

Recommender systems are an exciting topic in data science because they allow us to make better products, services, and content recommendations.

Businesses can better understand their customers and their needs by using recommender systems.

This, in turn, allows them to develop better products and services that meet the needs of their customers.

Recommender systems are also used to recommend content to users.

This can be done on an individual level or at a group level.

Think about Netflix, for example, always knowing what you want to watch!

Recommender systems are a valuable tool for businesses and users alike.

7.) Deep Learning

Deep learning is a research topic in data science that deals with artificial neural networks.

These networks are composed of multiple layers, and each layer is formed from various nodes.

Deep learning networks can learn from data similarly to how humans learn, irrespective of the data distribution.

This makes them a valuable tool for data scientists looking to build models that can learn from data independently.

The deep learning network has become very popular in recent years because of its ability to achieve state-of-the-art results on various tasks.

There seems to be a new SOTA deep learning algorithm research paper on  https://arxiv.org/  every single day!

8.) Reinforcement Learning

Reinforcement learning is a research topic in data science that deals with algorithms that can learn on multiple levels from interactions with their environment.

This area of research is essential because it allows us to develop algorithms that can learn non-greedy approaches to decision-making, allowing businesses and companies to win in the long term compared to the short.

9.) Data Visualization

Data visualization is an excellent research topic in data science because it allows us to see our data in a way that is easy to understand.

Data visualization techniques can be used to create charts, graphs, and other visual representations of data.

This allows us to see the patterns and trends hidden in our data.

Data visualization is also used to communicate results to others.

This allows us to share our findings with others in a way that is easy to understand.

There are many ways to contribute to and learn about data visualization.

Some ways include attending conferences, reading papers, and contributing to open-source projects.

10.) Predictive Maintenance

Predictive maintenance is a hot topic in data science because it allows us to prevent failures before they happen.

This is done using data analytics to predict when a failure will occur.

This allows us to take corrective action before the failure actually happens.

While this sounds simple, avoiding false positives while keeping recall is challenging and an area wide open for advancement.

11.) Financial Analysis

Financial analysis is an older topic that has been around for a while but is still a great field where contributions can be felt.

Current researchers are focused on analyzing macroeconomic data to make better financial decisions.

This is done by analyzing the data to identify trends and patterns.

Financial analysts can use this information to make informed decisions about where to invest their money.

Financial analysis is also used to predict future economic trends.

This allows businesses and individuals to prepare for potential financial hardships and enable companies to be cash-heavy during good economic conditions.

Overall, financial analysis is a valuable tool for anyone looking to make better financial decisions.

12.) Image Recognition

Image recognition is one of the hottest topics in data science because it allows us to identify objects in images.

This is done using artificial intelligence algorithms that can learn from data and understand what objects you’re looking for.

This allows us to build models that can accurately recognize objects in images and video.

This is a valuable tool for businesses and individuals who want to be able to identify objects in images.

Think about security, identification, routing, traffic, etc.

Image Recognition has gained a ton of momentum recently – for a good reason.

13.) Fraud Detection

Fraud detection is a great topic in data science because it allows us to identify fraudulent activity before it happens.

This is done by analyzing data to look for patterns and trends that may be associated with the fraud.

Once our machine learning model recognizes some of these patterns in real time, it immediately detects fraud.

This allows us to take corrective action before the fraud actually happens.

Fraud detection is a valuable tool for anyone who wants to protect themselves from potential fraudulent activity.

14.) Web Scraping

Web scraping is a controversial topic in data science because it allows us to collect data from the web, which is usually data you do not own.

This is done by extracting data from websites using scraping tools that are usually custom-programmed.

This allows us to collect data that would otherwise be inaccessible.

For obvious reasons, web scraping is a unique tool – giving you data your competitors would have no chance of getting.

I think there is an excellent opportunity to create new and innovative ways to make scraping accessible for everyone, not just those who understand Selenium and Beautiful Soup.

15.) Social Media Analysis

Social media analysis is not new; many people have already created exciting and innovative algorithms to study this.

However, it is still a great data science research topic because it allows us to understand how people interact on social media.

This is done by analyzing data from social media platforms to look for insights, bots, and recent societal trends.

Once we understand these practices, we can use this information to improve our marketing efforts.

For example, if we know that a particular demographic prefers a specific type of content, we can create more content that appeals to them.

Social media analysis is also used to understand how people interact with brands on social media.

This allows businesses to understand better what their customers want and need.

Overall, social media analysis is valuable for anyone who wants to improve their marketing efforts or understand how customers interact with brands.

16.) GPU Computing

GPU computing is a fun new research topic in data science because it allows us to process data much faster than traditional CPUs .

Due to how GPUs are made, they’re incredibly proficient at intense matrix operations, outperforming traditional CPUs by very high margins.

While the computation is fast, the coding is still tricky.

There is an excellent research opportunity to bring these innovations to non-traditional modules, allowing data science to take advantage of GPU computing outside of deep learning.

17.) Quantum Computing

Quantum computing is a new research topic in data science and physics because it allows us to process data much faster than traditional computers.

It also opens the door to new types of data.

There are just some problems that can’t be solved utilizing outside of the classical computer.

For example, if you wanted to understand how a single atom moved around, a classical computer couldn’t handle this problem.

You’ll need to utilize a quantum computer to handle quantum mechanics problems.

This may be the “hottest” research topic on the planet right now, with some of the top researchers in computer science and physics worldwide working on it.

You could be too.

18.) Genomics

Genomics may be the only research topic that can compete with quantum computing regarding the “number of top researchers working on it.”

Genomics is a fantastic intersection of data science because it allows us to understand how genes work.

This is done by sequencing the DNA of different organisms to look for insights into our and other species.

Once we understand these patterns, we can use this information to improve our understanding of diseases and create new and innovative treatments for them.

Genomics is also used to study the evolution of different species.

Genomics is the future and a field begging for new and exciting research professionals to take it to the next step.

19.) Location-based services

Location-based services are an old and time-tested research topic in data science.

Since GPS and 4g cell phone reception became a thing, we’ve been trying to stay informed about how humans interact with their environment.

This is done by analyzing data from GPS tracking devices, cell phone towers, and Wi-Fi routers to look for insights into how humans interact.

Once we understand these practices, we can use this information to improve our geotargeting efforts, improve maps, find faster routes, and improve cohesion throughout a community.

Location-based services are used to understand the user, something every business could always use a little bit more of.

While a seemingly “stale” field, location-based services have seen a revival period with self-driving cars.

20.) Smart City Applications

Smart city applications are all the rage in data science research right now.

By harnessing the power of data, cities can become more efficient and sustainable.

But what exactly are smart city applications?

In short, they are systems that use data to improve city infrastructure and services.

This can include anything from traffic management and energy use to waste management and public safety.

Data is collected from various sources, including sensors, cameras, and social media.

It is then analyzed to identify tendencies and habits.

This information can make predictions about future needs and optimize city resources.

As more and more cities strive to become “smart,” the demand for data scientists with expertise in smart city applications is only growing.

21.) Internet Of Things (IoT)

The Internet of Things, or IoT, is exciting and new data science and sustainability research topic.

IoT is a network of physical objects embedded with sensors and connected to the internet.

These objects can include everything from alarm clocks to refrigerators; they’re all connected to the internet.

That means that they can share data with computers.

And that’s where data science comes in.

Data scientists are using IoT data to learn everything from how people use energy to how traffic flows through a city.

They’re also using IoT data to predict when an appliance will break down or when a road will be congested.

Really, the possibilities are endless.

With such a wide-open field, it’s easy to see why IoT is being researched by some of the top professionals in the world.

22.) Cybersecurity

Cybersecurity is a relatively new research topic in data science and in general, but it’s already garnering a lot of attention from businesses and organizations.

After all, with the increasing number of cyber attacks in recent years, it’s clear that we need to find better ways to protect our data.

While most of cybersecurity focuses on infrastructure, data scientists can leverage historical events to find potential exploits to protect their companies.

Sometimes, looking at a problem from a different angle helps, and that’s what data science brings to cybersecurity.

Also, data science can help to develop new security technologies and protocols.

As a result, cybersecurity is a crucial data science research area and one that will only become more important in the years to come.

23.) Blockchain

Blockchain is an incredible new research topic in data science for several reasons.

First, it is a distributed database technology that enables secure, transparent, and tamper-proof transactions.

Did someone say transmitting data?

This makes it an ideal platform for tracking data and transactions in various industries.

Second, blockchain is powered by cryptography, which not only makes it highly secure – but is a familiar foe for data scientists.

Finally, blockchain is still in its early stages of development, so there is much room for research and innovation.

As a result, blockchain is a great new research topic in data science that vows to revolutionize how we store, transmit and manage data.

24.) Sustainability

Sustainability is a relatively new research topic in data science, but it is gaining traction quickly.

To keep up with this demand, The Wharton School of the University of Pennsylvania has  started to offer an MBA in Sustainability .

This demand isn’t shocking, and some of the reasons include the following:

Sustainability is an important issue that is relevant to everyone.

Datasets on sustainability are constantly growing and changing, making it an exciting challenge for data scientists.

There hasn’t been a “set way” to approach sustainability from a data perspective, making it an excellent opportunity for interdisciplinary research.

As data science grows, sustainability will likely become an increasingly important research topic.

25.) Educational Data

Education has always been a great topic for research, and with the advent of big data, educational data has become an even richer source of information.

By studying educational data, researchers can gain insights into how students learn, what motivates them, and what barriers these students may face.

Besides, data science can be used to develop educational interventions tailored to individual students’ needs.

Imagine being the researcher that helps that high schooler pass mathematics; what an incredible feeling.

With the increasing availability of educational data, data science has enormous potential to improve the quality of education.

26.) Politics

As data science continues to evolve, so does the scope of its applications.

Originally used primarily for business intelligence and marketing, data science is now applied to various fields, including politics.

By analyzing large data sets, political scientists (data scientists with a cooler name) can gain valuable insights into voting patterns, campaign strategies, and more.

Further, data science can be used to forecast election results and understand the effects of political events on public opinion.

With the wealth of data available, there is no shortage of research opportunities in this field.

As data science evolves, so does our understanding of politics and its role in our world.

27.) Cloud Technologies

Cloud technologies are a great research topic.

It allows for the outsourcing and sharing of computer resources and applications all over the internet.

This lets organizations save money on hardware and maintenance costs while providing employees access to the latest and greatest software and applications.

I believe there is an argument that AWS could be the greatest and most technologically advanced business ever built (Yes, I know it’s only part of the company).

Besides, cloud technologies can help improve team members’ collaboration by allowing them to share files and work on projects together in real-time.

As more businesses adopt cloud technologies, data scientists must stay up-to-date on the latest trends in this area.

By researching cloud technologies, data scientists can help organizations to make the most of this new and exciting technology.

28.) Robotics

Robotics has recently become a household name, and it’s for a good reason.

First, robotics deals with controlling and planning physical systems, an inherently complex problem.

Second, robotics requires various sensors and actuators to interact with the world, making it an ideal application for machine learning techniques.

Finally, robotics is an interdisciplinary field that draws on various disciplines, such as computer science, mechanical engineering, and electrical engineering.

As a result, robotics is a rich source of research problems for data scientists.

29.) HealthCare

Healthcare is an industry that is ripe for data-driven innovation.

Hospitals, clinics, and health insurance companies generate a tremendous amount of data daily.

This data can be used to improve the quality of care and outcomes for patients.

This is perfect timing, as the healthcare industry is undergoing a significant shift towards value-based care, which means there is a greater need than ever for data-driven decision-making.

As a result, healthcare is an exciting new research topic for data scientists.

There are many different ways in which data can be used to improve healthcare, and there is a ton of room for newcomers to make discoveries.

30.) Remote Work

There’s no doubt that remote work is on the rise.

In today’s global economy, more and more businesses are allowing their employees to work from home or anywhere else they can get a stable internet connection.

But what does this mean for data science? Well, for one thing, it opens up a whole new field of research.

For example, how does remote work impact employee productivity?

What are the best ways to manage and collaborate on data science projects when team members are spread across the globe?

And what are the cybersecurity risks associated with working remotely?

These are just a few of the questions that data scientists will be able to answer with further research.

So if you’re looking for a new topic to sink your teeth into, remote work in data science is a great option.

31.) Data-Driven Journalism

Data-driven journalism is an exciting new field of research that combines the best of both worlds: the rigor of data science with the creativity of journalism.

By applying data analytics to large datasets, journalists can uncover stories that would otherwise be hidden.

And telling these stories compellingly can help people better understand the world around them.

Data-driven journalism is still in its infancy, but it has already had a major impact on how news is reported.

In the future, it will only become more important as data becomes increasingly fluid among journalists.

It is an exciting new topic and research field for data scientists to explore.

32.) Data Engineering

Data engineering is a staple in data science, focusing on efficiently managing data.

Data engineers are responsible for developing and maintaining the systems that collect, process, and store data.

In recent years, there has been an increasing demand for data engineers as the volume of data generated by businesses and organizations has grown exponentially.

Data engineers must be able to design and implement efficient data-processing pipelines and have the skills to optimize and troubleshoot existing systems.

If you are looking for a challenging research topic that would immediately impact you worldwide, then improving or innovating a new approach in data engineering would be a good start.

33.) Data Curation

Data curation has been a hot topic in the data science community for some time now.

Curating data involves organizing, managing, and preserving data so researchers can use it.

Data curation can help to ensure that data is accurate, reliable, and accessible.

It can also help to prevent research duplication and to facilitate the sharing of data between researchers.

Data curation is a vital part of data science. In recent years, there has been an increasing focus on data curation, as it has become clear that it is essential for ensuring data quality.

As a result, data curation is now a major research topic in data science.

There are numerous books and articles on the subject, and many universities offer courses on data curation.

Data curation is an integral part of data science and will only become more important in the future.

34.) Meta-Learning

Meta-learning is gaining a ton of steam in data science. It’s learning how to learn.

So, if you can learn how to learn, you can learn anything much faster.

Meta-learning is mainly used in deep learning, as applications outside of this are generally pretty hard.

In deep learning, many parameters need to be tuned for a good model, and there’s usually a lot of data.

You can save time and effort if you can automatically and quickly do this tuning.

In machine learning, meta-learning can improve models’ performance by sharing knowledge between different models.

For example, if you have a bunch of different models that all solve the same problem, then you can use meta-learning to share the knowledge between them to improve the cluster (groups) overall performance.

I don’t know how anyone looking for a research topic could stay away from this field; it’s what the  Terminator  warned us about!

35.) Data Warehousing

A data warehouse is a system used for data analysis and reporting.

It is a central data repository created by combining data from multiple sources.

Data warehouses are often used to store historical data, such as sales data, financial data, and customer data.

This data type can be used to create reports and perform statistical analysis.

Data warehouses also store data that the organization is not currently using.

This type of data can be used for future research projects.

Data warehousing is an incredible research topic in data science because it offers a variety of benefits.

Data warehouses help organizations to save time and money by reducing the need for manual data entry.

They also help to improve the accuracy of reports and provide a complete picture of the organization’s performance.

Data warehousing feels like one of the weakest parts of the Data Science Technology Stack; if you want a research topic that could have a monumental impact – data warehousing is an excellent place to look.

36.) Business Intelligence

Business intelligence aims to collect, process, and analyze data to help businesses make better decisions.

Business intelligence can improve marketing, sales, customer service, and operations.

It can also be used to identify new business opportunities and track competition.

BI is business and another tool in your company’s toolbox to continue dominating your area.

Data science is the perfect tool for business intelligence because it combines statistics, computer science, and machine learning.

Data scientists can use business intelligence to answer questions like, “What are our customers buying?” or “What are our competitors doing?” or “How can we increase sales?”

Business intelligence is a great way to improve your business’s bottom line and an excellent opportunity to dive deep into a well-respected research topic.

37.) Crowdsourcing

One of the newest areas of research in data science is crowdsourcing.

Crowdsourcing is a process of sourcing tasks or projects to a large group of people, typically via the internet.

This can be done for various purposes, such as gathering data, developing new algorithms, or even just for fun (think: online quizzes and surveys).

But what makes crowdsourcing so powerful is that it allows businesses and organizations to tap into a vast pool of talent and resources they wouldn’t otherwise have access to.

And with the rise of social media, it’s easier than ever to connect with potential crowdsource workers worldwide.

Imagine if you could effect that, finding innovative ways to improve how people work together.

That would have a huge effect.

Final Thoughts, Are These Research Topics In Data Science For You?

Thirty-seven different research topics in data science are a lot to take in, but we hope you found a research topic that interests you.

If not, don’t worry – there are plenty of other great topics to explore.

The important thing is to get started with your research and find ways to apply what you learn to real-world problems.

We wish you the best of luck as you begin your data science journey!

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Home / Data Science / What Is Data Science?

What is Data Science?

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. To uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

The Data Science Life Cycle

The term “data scientist” was coined when companies first realized the need for data professionals skilled in organizing and analyzing massive amounts of data. Ten years after the widespread business adoption of the internet, Hal Varian, Google’s chief economist, first dean of the UC Berkeley School of Information (I School), and UC Berkeley emeritus professor of information sciences, business, and economics, predicted the importance of adapting to technology’s influence and reconfiguration of different industries.

“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”

– Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics 1

Today, effective data scientists masterfully identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are now required in almost all industries, which means data scientists have become increasingly valuable to companies.

Develop Specialized Data Science Skills Online

Get your master’s in information and data science and earn a certificate from the UC Berkeley School of Information (I School).

What Does a Data Scientist Do?

Data scientists have become assets across the globe and are present in almost all organizations. These professionals are well-rounded, analytical individuals with high-level technical skills who can build complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organizations. They also have the communication and leadership experience to deliver tangible results to various stakeholders across an organization or business.

Data scientists are typically curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

They also use key technical tools and skills, including:

Apache Hadoop

Apache Spark

NoSQL databases

Cloud computing

iPython notebooks

Why Become a Data Scientist?

As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. There’s now a demand for qualified data science professionals across organizations, big and small.

With the power to shape decisions, solve real-world challenges, and make a meaningful impact in diverse sectors, data science professionals have the opportunity to pursue various career paths.

Work from the comfort of your home

Gain new skills as data uses continue to grow

Request More Information

Where do you fit in data science.

Data is everywhere and expansive. Various terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but the roles typically involve different skill sets. The complexity of the data analyzed also differs.

Data Scientist

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Data scientists also leverage machine learning techniques to model information and interpret results effectively, a skill that differentiates them from data analysts. Results are then synthesized and communicated to key stakeholders to drive strategic decision making in the organization.

Skills needed:  Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They’re provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed:  Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer

Data engineers manage exponentially growing and rapidly changing data. They focus on developing, deploying, managing, and optimizing data pipelines and infrastructure to transform and transfer data to data scientists and data analysts for querying.

Skills needed:  Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data Science Career Outlook and Salary Opportunities

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. Data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.

Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts use high-level programming skills to create algorithms that continuously gather data and adjust their learning to improve prediction performance.

Learn how a Master of Information and Data Science from UC Berkeley can prepare you for a successful career in data science.

1 Hal Varian on How the Web Challenges Managers . (2009). Mckinsey . Retrieved December 2023. arrow_upward

How to Become a Data Scientist

Liz Simmons

Contributing Writer

Learn about our editorial process .

Updated October 31, 2023

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Are you ready to discover your college program?

A subfield of computer science, data science is the study of large quantities of data.

A relatively new and quickly growing field, data science offers excellent career opportunities. Glassdoor ranks data scientist as the third best job in the U.S. for 2022, citing high job satisfaction, top salaries, and abundant job openings.

This page explains how to become a data scientist. We cover experience and education requirements, look at certifications and job search strategies, and outline the steps to become a data scientist.

What Does a Data Scientist Do?

A data scientist's primary goal is to use data to answer questions, make predictions, and solve problems. Data science professionals collect, clean, and analyze data. They use computer science techniques and tools to create algorithms, find patterns, ask questions, and launch experiments. Data scientists also write reports and deliver presentations based on their findings.

Organizations of all kinds collect increasingly large sets of data and can face difficulty deciphering its meaning or how to use it effectively. Data scientists use their advanced knowledge and skills to help companies make informed business decisions.

A successful career in data science requires excellent problem-solving, analytical, and communication skills. Data scientists need experience with big data analytics, SQL, R, and data mining. The following section explores the most important soft and hard skills for data scientists.

What Education Do Data Scientists Need?

Education requirements for data science professionals vary by position, employer, and industry. Data scientists typically need at least a bachelor's degree in computer science , data science, or a related field. However, many employers in this field prefer a master's degree in data science or a related discipline.

Data analysts and data engineers usually need a bachelor's degree. Becoming a data scientist or computer and information research scientist usually requires a master's.

Some data science professionals hold a mix of education levels. For example, someone might earn a bachelor's in computer science and complete a data science bootcamp . Or, they might complete a bachelor's in an unrelated field and then earn a master's in data science.

In general, career opportunities and salaries increase as people earn higher degree levels. A graduate degree can also help job applicants stand out from candidates with just a bachelor's.

Data-based insights have revolutionized many industries while creating new avenues for profit. To realize these benefits, companies must look at the right data sets and ask the right questions. The specialized training that data scientists undergo develops advanced skills in these areas.

Knowing which data to look at and which questions to ask is one of the data science profession's major challenges. Professionals with strong aptitudes for statistical analysis, logical and mathematical thinking, creativity, and technology tend to succeed in the role.

The following section analyzes major job duties in more detail.

Data Scientist Responsibilities

• Identifying Organizational Challenges : Organizations face internal and external challenges in areas like employee productivity and pressure from competitors. Advanced data analysis can often identify patterns that indicate potential solutions. Data scientists identify organizational challenges that targeted statistical analysis can address.
• Assembling Data : First, data scientists identify an organizational issue to explore through data analysis. Next, they collect relevant statistical information. This process involves harvesting data, usually from many sources. Data scientists then check the collected information for completeness and accuracy.
• Analyzing Data : Data scientists sometimes analyze data by hand. However, in most cases they use software-based tools such as algorithms to filter data strategically. Data scientists may take part in developing these algorithms. They also devise the modeling methods used to generate data-based insights.
• Extracting Insights : Data scientists apply algorithms, mathematical models, and filters to large data sets. Then, they analyze the resulting statistics to extract actionable insights.
• Communicating Findings : Capable data scientists often uncover observations worth reporting to organizational decision-makers. They communicate these findings in writing, with charts and graphs, or through a combination of both. This aspect of the job may also involve giving oral presentations or reports.

Requirements to Become a Data Scientist

There is more than one way to enter the data science field. Data scientist requirements differ by employer, industry, and location. Aspiring professionals can meet data scientist job requirements in various ways.

The typical process to become a data scientist includes at least a four-year college degree in computer science, data science, or a related field. Many data scientists also pursue graduate education, professional certifications, and bootcamps.

Most data scientist jobs require some relevant professional experience. Students can gain experience while still in school through internships, capstone projects, and fellowships.

Below, we outline in detail some of the steps to become a data scientist.

Steps to Become a Data Scientist

Several paths can lead to a career as a data scientist . Below, we list the steps to become a data scientist based on different paths.

Bachelor's Degree Path

• Earn a bachelor's degree. Most data science jobs require at least a four-year bachelor's degree. Consider majoring in data science, computer science, or mathematics. Take classes in computer science, business, and statistics.
• Complete an internship. Getting internship experience develops career-relevant skills and can lead to job offers.
• Pursue professional certifications. Earning a professional certification is not required for becoming a data scientist, but it can help you prove your skills to potential employers.
• Get entry-level professional experience. Apply for jobs like data analyst, data engineer, and market research analyst. Spending several years developing your skills can lead to better job opportunities in the future.

Data Science Bootcamp Path

• Earn a bachelor's degree in any field.
• Complete a data science bootcamp. Data science bootcamps provide intensive career training in less time than most traditional college programs. You may need to complete prerequisites before starting a bootcamp.
• Complete an internship.
• Pursue professional certifications.

Match me with a bootcamp.

Find programs with your skills, schedule, and goals in mind.

Master's Degree Path

• Earn a master's degree in data science. Data science master's programs usually take 1-2 full-time years to complete.

What Is a Data Scientist?

New technologies help organizations easily collect large amounts of data. But, they often do not know what to do with this information. Data scientists use advanced methods to help bring value to data. They collect, organize, visualize, and analyze data to find patterns, make decisions, and solve problems.

Data scientists need strong skills in programming, data visualization, communication, and mathematics. Typical job responsibilities include gathering data, creating algorithms, cleaning and validating data, and drafting reports. Nearly any organization can benefit from the contributions of a trained data scientist. Potential work sectors include healthcare, logistics, and banking and finance.

How Much Experience Do Data Scientists Need?

Data scientist requirements for professional experience depend on the role and workplace. In general, data scientist positions are not entry-level jobs. Successful candidates need some relevant experience.

Before becoming a data scientist, some people start out in related information technology positions. These stepping-stone roles may include data analyst, market research analyst, or data engineer.

Data science degree programs and bootcamps often include internships, fellowships, and capstone projects. These experiences provide hands-on practice that can help graduates land a job offer. Some employers let job applicants substitute education for experience, or vice versa.

Professional Certifications

Professional certifications are not a requirement for becoming a data scientist. However, a certification can help you stand out and show your expertise to potential employers. Getting certified can help you earn a higher entry-level salary and open the door to more career advancement opportunities.

In some cases, a data science certification may persuade an employer to hire someone who does not meet all of a job's stated education or experience requirements.

SAS Data Science Certification

This certification demonstrates the ability to use SAS and open source tools to manipulate big data. Certified professionals know how to use machine learning models to make business recommendations. Applicants complete the SAS data curation professional, advanced analytics professional, and AI & machine learning credentials to earn the data science certification.

Azure Data Scientist Associate

Microsoft's data scientist associate certification shows knowledge and experience with Azure machine learning and Azure databricks. Applicants must pass the Designing and Implementing a Data Science Solution on Azure exam. The credential demonstrates skill in implementing machine learning, managing Azure resources, and deploying machine learning solutions.

Senior Data Scientist

The senior data scientist certification signifies data science knowledge and data leadership potential. Candidates pay a $775 fee that covers exam prep resources, a digital badge, and a credential kit. The certification requires 3-5 years of analytics and research experience and a bachelor's degree or higher.

Principal Data Scientist

The principal data scientist credential offers four tracks for different applicants. It indicates high-level knowledge of data science and analytics technologies. Depending on the track they qualify for, candidates must pursue various exams and assessments to earn the credential. Once awarded, the designation never expires.

Key Soft Skills for Data Scientists

• Analytics: Understanding and interpreting data are at the heart of data science, so business and data analytics skills are essential to data scientists. Some employers seek skills in machine learning, a branch of analytics that creates performance prediction systems.
• Business Knowledge: Many companies seek data scientists who can interpret data and use it to inform business strategies for improving efficiency, productivity, and sales. Data scientists can specialize in the data science field of business analytics, in which old performance data guides present and future business moves.
• Communication: Most data scientists must communicate their findings, interpretations, and ideas to employers and colleagues. Consequently, clear writing and public speaking skills make data scientists more effective in collaborating with team members and superiors.
• Problem-Solving: Most companies, governments, and nonprofits turn to data scientists for their ability to solve problems with data-driven insights. These professionals can help organizations clarify which aspects of issues can be solved using data sets.
• Organization: Data scientists need strong organizational skills to address projects involving complex parts and data sets. Professionals who understand how to organize large repositories of raw data, for example, may know how to improve company outcomes by sourcing more representative amounts of data.

Key Hard Skills for Data Scientists

• Big Data Analytics: Big data analytics involves the analysis and application of large data sets to help companies understand consumer trends and market patterns. The term "big data" refers to data sets so complex that normal data processing software could not work with them.
• Data Mining/Data Warehouse: Data mining is the process of looking within a large set of data for previously unrecognized patterns or insights. A data warehouse is a system created specifically for data analytics.
• SAS: SAS is a suite of software products created specifically for data management and analysis for business insights. Use and mastery of SAS are foundational skills for data science professionals, helping them create value for their employers by addressing real-world problems.
• SQL: Short for Structured Query Language, this language works specifically with database management. All prospective data scientists should maintain a firm grasp on the latest trends and advances in SQL.
• R: R, a programming language, applies to statistics and graphics. Many computer science professionals consider R to be foundational knowledge.

Data Science Degree Programs

Online Data Science Bachelor's Programs

Data Science Bootcamps

Certifications for Computer Science Professionals

Professional spotlight: interview with a data scientist.

Why did you choose to become a data scientist?

Prior to data science, I was a professor. But I (and many of my fellow young Ph.D.s) gradually realized that the academic job market has serious problems that prevent it from absorbing and properly utilizing all of the talented candidates who are getting their doctorates. Data science offers a way for people with strong mathematical and statistical backgrounds to apply their industry knowledge and research acumen to problems in the private sector in a much livelier job market (also for substantially better pay than is offered in academia). I also felt that data science, as a fast-growing, dynamic field, would allow me to expand my skills and insights faster than in academia.

Can you describe your path to a career in data science?

My first problem once I decided to switch careers was how, exactly, to transition. While I was highly educated, I had no specific certifications or qualifications that many jobs were looking for. That is why I chose to enroll in The Data Incubator. The Data Incubator specializes in taking candidates with strong academic backgrounds and helping them to learn how to conduct and communicate data science effectively in the private sector. They also help to match their students with prospective employers, which enabled me to get my first job in data science at Cova Strategies (which later transitioned to a role at NNData as senior data scientist).

What are some high and low points for this career? What challenges might a data scientist face?

While I am likely not in a good position to comment on career highs and lows (I have not been in data science for that long), I can say that the biggest challenge I faced in data science was believing myself to actually be qualified. Even after getting my first data science role, I felt much of the same imposter syndrome that plagues many people, especially those coming from academia.

What type of person does well in this role?

People who have a strong grasp of mathematics and statistics and can learn and apply new techniques rapidly. Data science is a rapidly evolving field; methods change, new techniques develop, and there is always something relevant to discover, understand, and integrate into new or even existing projects. No one can stay informed on every topic, so there will inevitably be times when you have to learn on the fly to use the latest or best techniques to solve a problem.

What advice do you have for students considering a career in data science?

First, as I mentioned earlier, data science is much more an exercise in mathematical and statistical reasoning than anything else, so don't neglect your mathematics! Second, be prepared to be a pioneer. While many people have attempted to solve almost any problem (Stack Overflow is proof of that), few have likely tried to solve the problems you will be facing with the exact intention that you have. Be prepared to combine solutions together, modify code, or apply technologies in ways they may not have been initially intended. That's what makes data science into a "data art," and that's what makes it fun! Third, especially if the student is coming from a graduate program, know your value. If you have (or are about to have) a Ph.D., you probably know something! You are more qualified than you likely give yourself credit for, and should not let yourself forget that. Finally, getting your foot in the door in any industry can be hard. Try and find some certification program, course, or something that signals to companies that you are serious and able to apply your skills to meet their needs. Beyond that, just remember that, like any career, a career in data science is a journey. Be prepared for the unexpected and to find your ideal niche in a company (and the wider industry) where you may not initially expect.

Andrew Graczyk, Ph.D.

Dr. Andrew Graczyk is a graduate of The Data Incubator . He also earned his Ph.D. in economics from the University of North Carolina at Chapel Hill in December 2017. His research specialties in game theoretic modeling, Bayesian statistics, and time series analysis allowed him to synthesize novel models to capture adverse incentives responsible for behavior that other models struggle to explain. Prior to his career in data science, he developed experience working with a wide variety of data and topics from asset bubble formation to housing markets to environmental regulation and agriculture. As a senior data scientist at NNData, Dr. Graczyk applies his multifaceted experience with data and theory to create robust, flexible, and holistic solutions to problems using cutting-edge machine learning and statistical techniques.

Where Do Data Scientists Work?

The U.S. Bureau of Labor Statistics (BLS) tracks salary, industry, and location information for data scientists. As of May 2021, the BLS reports the following as the five top-employing states for data science professionals:

• North Carolina

Job opportunities tend to cluster in urban areas. However, some data scientists work mainly or exclusively online. The position translates well to remote work.

The private sector dominates the data science employment landscape. As of May 2021, the BLS reports the following as the five leading industries for data scientist employment:

• Computer systems design
• Enterprise management
• Technical consulting
• Scientific research services
• Credit mediation

A day in the life of a data scientist may differ depending on the industry of employment and whether a professional works on-site or remotely.

Career Outlook for Data Scientists

Data scientists might worry that emerging artificial intelligence technologies could reduce the need for their expertise. However, the complexities of modern business require human solutions.

The BLS projects data scientist employment to increase by 31.4% from 2020-2030. In addition, LinkedIn's 2020 Emerging Jobs Report states that data scientists are replacing statisticians in some industries to prepare for a more advanced future in tech.

The Job Hunt

Places to look for data scientist positions include professional organizations, job fairs, and networking opportunities at annual conferences. Ask for job leads from mentors, alumni associations, and former supervisors and colleagues. Current students and recent graduates can apply for paid and unpaid internships to gain professional experience.

You can also search for openings on job boards. Below, we highlight five of the top job boards for the data science industry.

• DataJobs : This job site posts openings in data science, data analysis, and data engineering. It matches companies with big data talent.
• Open Data Science Job Portal : Job-seekers can find thousands of data science jobs here at over 300 companies. Candidates can submit their resumes and get matched automatically with relevant positions.
• Ai-jobs.net : Find artificial intelligence, machine learning, and big data jobs around the world.
• Digital Analytics Association : Browse for analytics openings by industry, job type, location, and experience level.
• icrunchdata : This website posts analytics, technology, and data-related jobs worldwide. It also provides industry insights and career advancement information.

How Much Can You Earn as a Data Scientist?

According to the Bureau of Labor Statistics (BLS), the median data scientist salary for 2021 was $108,660. However, salaries vary significantly in the data science career path based on experience, education, and specialization.

For instance, a data scientist with certifications and training in several tools, like Apache Spark or Python, might earn more than one without the same credentials.

Similarly, some industries pay higher salaries. The BLS lists computer and peripheral equipment manufacturing as the top-paying data scientist career sector. Data compiled by Fortune indicates that data scientists earn higher salaries than in previous years, regardless of industry and other factors.

Median Annual Salary of Data Scientists, 2021

Source: BLS

Data Scientist Salary by Experience

The average data scientist salary increases with experience level, according to Payscale data from July 2022 . With less than one year of experience, these professionals earn an average of $85,730. After 20 years or more, their salary increases to an average of $134,980.

This shift in salary potential typically occurs because data scientists become more knowledgeable with experience. Employers may have more interest in highly skilled data scientists and offer them higher pay.

Data Scientist Salary by Education

The tables below list the average salaries for data science professionals based on their education level and degree type.

Statistics, informatics, and mathematics degrees provide foundational information for students unsure about pursuing data science or other related fields. In contrast, a computer science degree provides a more direct pathway.

Although Payscale doesn't report salaries for data science degree-holders, a bachelor's or master's data science degree are additional options. Master's degree graduates could see higher average starting salaries than bachelor's degree-holders due to their advanced skills.

However, a bachelor's degree in computer science offers a similar average wage to a non-computer science master's degree because of its relevant content for data scientists.

Discover which education path is right for you:

Data scientist salary by location.

How much does a data scientist make in different locations? BLS data shows that Washington, California, and Delaware are the highest-paying states for data science. Additionally, Payscale lists three California cities — Mountain View, San Francisco, and Santa Clara — as the top-paying cities .

The demand for data scientists can vary significantly nationally. The areas with the highest salaries tend to have strong technology industries that seek skilled professionals. For instance, California's tech sector is the largest in the country, with close to two million jobs .

A data science career salary may also relate to an area's cost of living. California currently has the third-highest cost of living index . Washington, Delaware, New York, and New Jersey also have a higher-than-average cost of living index.

Questions About Data Science Careers

Is data science a good career.

Yes, data science offers promising career paths. It is a fast-growing and relatively new field with higher-than-average salaries. The Bureau of Labor Statistics projects above-average job growth for many data science careers, including operations research analysts and computer and information research scientists .

Do data science jobs pay well?

Yes. Data scientists can earn significantly more than the average worker. The Bureau of Labor Statistics reports that data scientists earned a mean annual salary of $108,660 as of May 2021.

What are the education requirements for a data scientist?

Minimum education requirements for data science professionals usually include a bachelor's degree. Many data scientist positions require or prefer applicants with a master's degree in data science, computer science, or a relevant field.

How can I become a data scientist?

Various paths can prepare students for a data science career. The steps to become a data scientist may include earning a bachelor's degree, gaining work experience, and completing a professional certification. Many data scientists earn a master's degree or attend a bootcamp.

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The Role of Data Science in Research

The training was very relevant. I am about to start a project that aims to predict phenotype based on genetic data, which I plan to approach using machine learning. I really enjoyed the discussions on pitfalls of machine learning, what makes them effective, what can be expected of them and what can’t be expected of them.

Eric Lucas, Post Doctoral Research Associate, Liverpool School of Tropical Medicine.

In academia, new applications of Machine Learning are emerging that improve the accuracy and efficiency of processes, and open the way for disruptive data-driven solutions. For example, the implementation of Data Science in Biomedicine is helping to accelerate patient diagnoses and create personalised medicine based on biomarkers.

Aligned with these advancements, we have received growing interest from professionals in academic disciplines outside of computer science, regarding what are the Data Science tools and techniques they need to know to prepare for the future, and what are the relevant applications in their area of specialisation.

Working with Liverpool School of Tropical Medicine (LSTM), we set out to address these questions and upskill their Department of Vector Biology in Data Science using Python. Our goal was to provide PhD’s and Post Doctoral Researchers with transferable knowledge and Data Science skills they can apply to their research in Epidemiology and Bioinformatics.

In this article we will provide an overview of:

• Essential Data Science techniques researchers need to know

Applications of Data Science in Epidemiology

• Case study: A training plan for Liverpool School of Tropical Medicine

It’s worth noting that this Data Science training strategy can be applied in any field. Cambridge Spark Data Science and Machine Learning training programmes are designed to equip individuals with the skills to gather, analyse and interpret structured and unstructured data, in just two days.

Get in touch with us to learn more about the course!

An Introduction to Data Science in Python

The essential Data Science techniques researchers need to know about

To build data science capabilities, the first step is to upskill researchers and subject-matter experts in the foundations of Data Science using Python. Widely-used techniques to start learning are:

Data Science   Essentials

• Working with Jupyter notebooks
• The Numpy library for array manipulation
• The Pandas library for data manipulation
• Data cleaning and pre-processing
• Data visualisation with Matplotlib and Seaborn
• Applying Principal Component Analysis (PCA) in Python with SKLearn

Unsupervised Learning and Supervised Learning

Unsupervised Learning

• The Scikit-learn library for Machine Learning and Scikit-learn pipelines
• k-means clustering
• Hierarchical cluster analysis
• Density-based clustering (DBScan)

Supervised Learning

• The k-Nearest Neighbour algorithm
• Overfitting, underfitting, bias-variance tradeoff
• Cross-Validation and hyperparameter tuning

Ensemble Models

• Decision Trees
• The intuition behind Bagging and Bootstrapping, Concept, Algorithm, Random Forests in scikit-learn
• The intuition behind Boosting classifiers, visualisation, Boosting methods in scikit-learn
• Adaboost, XGBoost, LightGBM
• Stacking in scikit-learn

How researchers can make use of Machine Learning

Current research initiatives are using Machine Learning to detect health threats and improve diagnosis accuracy /efficiency to have a positive impact on patient outcomes. Examples include:

• Using Feature Engineering and Feature Selection in order to identify biomarkers capable of distinguishing between diseases and group samples with shared characteristics.
• Applying regression models to examine the cause-and-effect relationship between disease risk factors.
• Using random forests to make highly informative predictions for more targeted drug prescriptions.
• Using CNN’s for image analysis to detect diseases such as Malaria.

A training plan for researchers at the   Liverpool School of Tropical Medicine

“The course was intended to improve the data science capability of our department, though each student had their own motivation for signing up. Personally, I was looking for an overview of machine learning tools, the necessary considerations when applying them, and indications about how to implement them,” said Eric Lucas, Post Doctoral Research Associate, Liverpool School of Tropical Medicine. Aligned with these technical specifications and learning objectives Cambridge Spark delivered a three-day Introduction to Data Science using Python training session, on-site, at the Department of Vector Biology.

The training was very relevant. I am about to start a project that aims to predict phenotype based on genetic data, which I plan to approach using machine learning.  I really enjoyed the discussions on pitfalls of machine learning, what makes them effective, what can be expected of them and what can’t be expected of them. Eric Lucas, Post Doctoral Research Associate, Liverpool School of Tropical Medicine.

“I enjoyed learning about how the different machine learning tools work, their strengths and weaknesses. I do a lot of data analysis already (using a lot of tools that overlap strongly with machine learning, such as logistic regressions, PCA, clustering analysis) and I generally get a kick out of thinking about data,” said Eric Lucas, Post Doctoral Research Associate, Liverpool School of Tropical Medicine. “I was actively searching for organisations that could provide in-house machine learning courses, and the course which Raoul proposed matched very closely with what I envisaged.”

Interested in training for your teams?

Whether you're looking to train 5 people or 100 people, we have a variety of scalable training solutions to help you address a wide spectrum of training needs within the fields of Data Science, Artificial Intelligence, or Software Engineering.

Please contact us with your details and any known requirements. We'll then get in touch and guide you through every step of the way.

Get in touch with a Cambridge Spark

Get Involved in Research

Finding research opportunities .

Many of our students are eager to participate in research opportunities, and a common question we get is how to get started. 

• Start by reading this helpful guide from Stanford Academic Advising: How Do I Get Started in Research?
• Opportunities for Data Science & MCS Students - These come from people and organizations who reach out to our program asking us to advertise an opportunity to our students. We update this document weekly with new opportunities we hear about. 
• Stanford On & Off Campus Learning Opportunities (SOLO)
• Academic Advising Newsletter
• Handshake  
• Stanford Impact Labs’ list of labs that often have research opportunities available to undergraduates 
• Meet with a Data Science & MCS Peer Advisor - They often have first hand experience with getting involved in research on campus. 
• Your faculty advisor

Summer Research Programs

• Summer Undergraduate Research Program through the Statistics Department (SURP-Stats)
• Data Science for Social Good (DSSG)
• Don’t be afraid to apply for a program outside of the Data Science major! Many other disciplines look for students with technical skills, making Data Science students a great fit. For example, political science, psychology, and economics often have projects that interest our students and involve some data science. 

Two Other Paths to Research 

Path 1: assist a faculty member with their research project.

This is an opportunity to assist with a research project that a faculty member is already leading.

Reach out to a professor to inquire and/or apply for a research position in a professor’s lab (see the section above about where to look for these opportunities). Don’t be afraid to consider professors or labs outside of the Data Science major! As stated above, many other disciplines look for students with technical skills, making Data Science students a great fit.

If the position is unpaid and you’d like to receive credit, ask your faculty mentor if this is possible. If it is, you will probably enroll in the appropriate Independent Study/Research course under the sponsored faculty, which is often numbered 199. For example, if the faculty leading the research is a CS professor, you’d likely enroll in their CS 199 class; if the faculty member is a Sociology professor, you’d likely enroll in SOC 191. Contact the faculty member or that department’s student services officer for a permission number, if needed.

• If the position is related to the data science program’s learning outcomes, students pursuing the Data Science B.S. Mathematics and Computation subplan can ask for it to be approved as a data science elective (maximum of 3 units). 
• If the position is paid, then Stanford does not allow you to also receive credit for the experience.

Path 2: Independent Research Project

This is an opportunity for students to engage in undergraduate research for credit as an independent study. If students wish to pursue this option, the student must elicit sponsorship from a faculty member.

Identify a topic of interest that you want to explore more in-depth, along with a hypothesis. First, consider directed reading . 

Before proceeding, consider whether you have the time to commit to this research/project. If you do, choose which quarter would work, keeping course enrollment deadlines in mind. 

Find a faculty member to sponsor your research. See Developing a Mentor Relationship . Faculty can be outside of Data Science but if you’d like to use the experience as part of the major, the topic should relate to the data science program’s learning outcomes. This requires approval through the program. 

Enroll in the appropriate Independent Study/Research course with the faculty mentor, which is often numbered 199. For example, if the faculty mentor is a CS professor, you’d likely enroll in their CS 199 class. If the faculty mentor is a Sociology professor, you’d likely enroll in SOC 191. Contact the faculty member or that department’s student services team for a permission number, if needed.

• If the position is related to the data science program’s learning outcomes, students pursuing the Data Science B.S. Mathematics and Computation subplan can ask for it to be approved as a data science elective (maximum of 3 units). For Students pursuing the Data Science & Social Systems B.A., if the research aligns with their program, they can ask for it to be approved as a pathway course. 
• Students cannot receive university credit and funding for the same research experience. 

Resources for Research

• Research and Independent Projects  
• Assemble Your Research Toolbox
• Funding Your Project
• Research Support
• Stanford Digital Repository - Literature Reviews
• Library Data Services

Image credit: Andrew Brodhead

Departmental Research Areas

Performing research at the foundations of Data Science or in Data Science applications.

Department of Biological Sciences

Structural and Computational Biology and Biophysics

Research includes topics such as: determination of protein and nucleic acid structures, the structure and mechanism of protein and RNA enzymes (including proteins involved in cancer), structures of macromolecular complexes, study of the structure and mechanism of viruses (including emerging pathogens such as West Nile and Dengue viruses), genomics, transcriptomics, proteomics, systematics, and computational systems biology, molecular dynamics, machine learning and other topics at the interface of experiment and computation.

Department of Computer Science

Bioinformatics and Computational Biology

Faculty in the area of bioinformatics and computational biology apply computational methodologies such as databases, machine learning, discrete, probabailistic, and numerical algorithms, and methods of statistical inference to problems in molecular biology, systems biology, strucutral biology, and molecular biophysics.

Databases and Data Mining

The data revolution is having a transformational impact on society and computing technology by making it easier to measure, collect, and store data. Our databases and data mining (big data) research group develops models, algorithms, and systems to facilitate and support data analytics in large-scale, complex domains.  Application areas include database privacy and security, web search, spatial data, information retrieval, and natural language processing.

Machine Learning and Artificial Intelligence

Recent increases in data collection and large-scale computing have facilitated successful application of machine learning and artificial intelligence methods across a wide range of fields, including healthcare, education and industrial systems. The Machine Learning and Information Retrieval group develops statistical methods and algorithms to learn models of the world from observations of past behavior, and evaluates these methods in real world applications in complex domains.

Theory of Computing and Algorithms

Members of the group work in areas that include analysis of algorithms, parallel computation, computational algebra and geometry, computational complexity theory, digital watermarking, data structures, graph algorithms, network algorithms, distributed computation, information theory, analytic combinatorics, random structures, external memory algorithms, and approximation algorithms.

Department of Statistics

Big Data Theory Group

The group is focused on research that includes topics on big data, machine learning, deep/reinforcement learning, semi-nonparametric inferences, and high dimensional statistical inferences.

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Pew Research Center’s  Data Labs  team uses computational methods to complement and expand on the Center’s existing research agenda. The team collects text, audiovisual and behavioral datasets; uses innovative computational techniques and empirical strategies for analysis; and generates original research. Data Labs also explores the limitations of these data and methods and works toward establishing standards for use and analysis.

The Data Labs project both produces its own reports and collaborates with other research groups at the Center, applying new computational approaches to existing research questions. Past research has explored  congressional communication , looked at the ways Americans use  social media , and analyzed everything from  video s and  images  to  algorithmic bias  and  religious rhetoric . The Data Labs team also writes about the process of computational social science research on  Decoded , the Center’s behind-the-scenes blog about research methods.

In addition, Data Labs manages the Center’s computing infrastructure. That includes building high-performance computing systems and databases that facilitate web data collection and processing; deploying platforms that facilitate collaborative, replicable analysis in R and Python; and developing systems to automate research tasks such as content classification for machine learning.

As is true for Pew Research Center as a whole, Data Labs is nonpartisan and nonadvocacy. The team values independence, objectivity, accuracy, rigor, humility, transparency and innovation.

[ View the latest research from Data Labs ]

Why did Pew Research Center create Data Labs?

Data Labs was created as a response to the changing nature of data on human behaviors and attitudes. The public is expressing views online and leaving behind electronic trails of behavior in unprecedented ways. We can now learn about whom people connect with on social networks, what they search for, and what content they post. At the same time, institutions and groups are using the internet to convey information to diverse audiences, inviting researchers to observe what they post and how people react.

While some of these digital traces of communication and behavior are unstructured and not amenable to analysis in raw form, a number of new technologies are making it easier to collect and process these data. These technologies include:

• Internet data collection : This includes harvesting web page content and parsing out fields (e.g., dates, names, links and tables) for analysis as well as querying APIs online to obtain formatted data.
• Natural language processing (NLP): This includes processing text to measure concepts and extract patterns.
• Machine vision : This refers to analyzing images using computational models that estimate what the images depict.
• Online distributed labor platforms : These platforms allow major data collection efforts to be divided into a series of small tasks that can then be completed by external individuals. This is sometimes referred to as “crowdsourcing.”

Data Labs is a testing ground for these data sources and the different approaches to analyzing them, with the goal of extracting meaning from the data through creative design, innovative methods, thoughtful measurement and sound deployment.

The Data Labs team also employs methodologies honed across the Center, such as  content analysis ,  survey experiments , and the analysis of  open-ended survey responses .

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• A biostatistics breakthrough: Using data to improve teen driving

May 16, 2024

By Jenni Laidman

In Michael Elliott’s line of work, numbers save lives. Elliott, professor of Biostatistics at the University of Michigan School of Public Health , is an expert in the art and science of collecting, interpreting and analyzing data. His specialty, biostatistics, solves problems in public health, medicine and science, sifting through sand piles of data to unearth the gold nuggets of meaning.

One of his most recent projects could help save lives of a particularly accident-prone population: young drivers.

It’s part of a two-decade-long, traffic-safety-research collaboration with a team from the University of Pennsylvania, led by Dr. Flaura K. Winston , a pediatrician and an engineer. Winston, the Distinguished Chair of Pediatrics at Children’s Hospital of Philadelphia and professor of Pediatrics at the University of Pennsylvania, was the first to uncover that children were dying from front-seat airbags.

Elliott began working with Winston while an assistant professor at Penn from 2000 to 2005, focusing first on children in motor vehicle accidents. At the time, Elliott’s two daughters were preschoolers, adding special significance to the undertaking. One early study led the auto industry to reexamine pickup truck design when it found that children in the rear seats of extended-cab pickups were at an almost five times greater risk of injury than those in the rear seats of other vehicles.

“Mike was with us almost from the beginning,” Winston said. “His contributions were critical at every phase of our work because he’s an expert in so many things. He co-designed sampling and surveys and oversaw our statistical analysis. The quality of our work and its impact is because of Mike’s expertise and contributions.”

Targeting teen driving safety

As the collaboration turned to the urgent need for teen driving safety, Elliott’s two daughters were in their teenage years. “So, there was a personal connection for me,” he said, as there was for so many parents.

In 2021, 2,116 drivers ages 15-20 died in motor vehicle accidents, according to the most recent data from the National Highway Traffic Safety Administration. And deaths aren’t all of it: More than 203,000 teen drivers were injured in 2021, creating $40.7 billion in medical costs, the Insurance Institute for Highway Safety reported.

It’s a problem out of proportion to the size of the teen driving population. Although teens account for just 5% of licensed drivers, they’re responsible for 8.4% of fatal crashes and 12% of all police-reported crashes. Automobile accidents are the No. 1 cause of death for teen girls, and even though teen boys die in far greater numbers—1,866 boys died in 2020 compared to 864 girls—motor vehicle accidents are the No. 3 cause of death for boys, behind homicide and suicide.

In the months it takes new drivers to develop the judgment and skill of experienced drivers, they’re at their most vulnerable.

“Crash risk goes exponentially higher from the day before you get your license to the day you get it,” said Liz Walshe , a research scientist at the Center for Injury Research and Prevention, Children’s Hospital of Philadelphia, who co-leads the Neuroscience of Driving research program. “Teens start driving independently and traveling new routes with no supervision.”

And that’s what the team’s intervention targets, Elliott said, with savvier training.

“There’s no reason you couldn’t train people to drive the way we train pilots,” he said.

Identifying risky teen drivers

A driving simulator developed by Diagnostic Driving Inc., a spin-off from Children’s Hospital of Philadelphia research—Winston is a co-founder—offers the hair-raising experiences no mom supervising her son’s road practice wants to be part of and no driver’s examiner would risk.

Using a computer monitor, a steering wheel and brake and gas pedals, the assessment system records how drivers respond when pedestrians run into traffic, the car in front brakes suddenly, and other dicey moments behind the wheel.

But creating a program laden with high-risk scenarios wasn’t enough. Researchers had to figure out which behaviors contributed to bad outcomes. That was Elliott’s job.

“I was trying to figure out, first of all, what are the pieces that are important?” he said. “We had an enormous amount of data from these trial driving situations. How do we summarize it to pull as much information out of that data as possible, fashioned in a way that’s useful from a scientific perspective?”

What emerged from his efforts was a list of 20 driving behaviors featuring things like jerky braking, throttle control, jackrabbit starts, tailgating, aggression, recklessness, rule-breaking and steering control.

The team took the list of 20 behaviors and organized them into four risk clusters. From best performance to worst, the categories were:

• “No Issues” for cautious drivers with good steering control and good braking
• “Minor Issues” for those with generally good control but minor problems with jerky breaking, tailgating, speeding
• “Major Issues” for skilled rule-breakers or those extremely slow and with poor control
• “Major Issues with Dangerous Behaviors” encompassed drivers with poor control and reckless, rule-breaking behavior

 The research team proved the accuracy of these categories when the state of Ohio gave the virtual driving test as part of its licensure process—although the virtual driving test results did not change whether the teen received a license. In more than 33,000 driver assessments, the researchers found that drivers in both “Major Issues” categories were most likely to fail their on-road test. Those in the first two categories were least likely to fail the exams. The research was published in 2022 in Transportation Research Part F: Traffic Psychology and Behaviour.

Predicting teen crashes

Test results are one thing; real-world performance could prove entirely different. The team’s next study tackled that question, tracking the accident records of nearly 17,000 drivers ages 16-24 who took the virtual driving assessment at licensure.

Again, the driving assessment proved to be a powerful tool. In an October paper in the journal Pediatrics , Elliott and the team revealed that teens rated as having “No Issues” had a 10% lower risk of an accident than the average young driver. At the other end of the spectrum, teens rated “Major Issues with Dangerous Behaviors” had an 11% higher risk of an accident than the average young driver.

Mike was with us almost from the beginning. His contributions were critical at every phase of our work because he’s an expert in so many things. He co-designed sampling and surveys and oversaw our statistical analysis. The quality of our work and its impact is because of Mike’s expertise and contributions.”

— Dr. Flaura K. Winston on Michael Elliott

Several questions remain, however, including how much actual driving did the young drivers do? Were some drivers more accident-prone simply because they were on the road more often, increasing the probability of a mistake?

To address this and other questions, the team will follow 1,000 young Pennsylvania drivers who will take their first virtual driving assessment as a learner-driver and receive feedback about what they need to improve. A second assessment with feedback will take place when they go in to take a driver’s exam.

The 1,000 study participants will receive an app that keeps track of how much driving they do and how well they do it. Finally, each will be randomly assigned to participate in one of three groups: One-third will receive behind-the-wheel driver’s training, the kind of course that’s routine in some states, but not in Pennsylvania; one-third will receive a state-of-the-art online driver’s training module; and one third—the control group—will receive no training. The issue of training is particularly important for older teens in many states, including, for instance, Ohio and Michigan, which require driver’s ed for teenagers, but not for those 18 or older.

The Pediatrics paper showed that 18-year-olds are at grave risk. They were 16% more likely to crash than younger drivers.

“The 16-year-olds are actually the best drivers,” Walshe, lead author on the paper, said. One reason could be that the 18-year-olds never had to take driver training.

For Elliott, the work with the Children’s Hospital/Penn team has done more than give him interesting problems to solve and the funding to do it.

“These collaborations are important to my own methodological work at Michigan Public Health,” he said of the kind of research papers that other biostatisticians read to sharpen their own statistical methods.

And it led to work that has saved lives. 

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Astrophysics > Instrumentation and Methods for Astrophysics

Title: citizen science in european research infrastructures.

Abstract: Major European Union-funded research infrastructure and open science projects have traditionally included dissemination work, for mostly one-way communication of the research activities. Here we present and review our radical re-envisioning of this work, by directly engaging citizen science volunteers into the research. We summarise the citizen science in the Horizon-funded projects ASTERICS (Astronomy ESFRI and Research Infrastructure Clusters) and ESCAPE (European Science Cluster of Astronomy and Particle Physics ESFRI Research Infrastructures), engaging hundreds of thousands of volunteers in providing millions of data mining classifications. Not only does this have enormously more scientific and societal impact than conventional dissemination, but it facilitates the direct research involvement of what is often arguably the most neglected stakeholder group in Horizon projects, the science-inclined public. We conclude with recommendations and opportunities for deploying crowdsourced data mining in the physical sciences, noting that the primary goal is always the fundamental research question; if public engagement is the primary goal to optimise, then other, more targeted approaches may be more effective.

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Analytics Insight. “Big Data Analytics and Data Scientist Recruitment Landscape in India, https://www.analyticsinsight.net/big-data-analysts-and-data-scientists-recruitment-landscape-in-india/ .”  Accessed April 26, 2024.

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Brief research report article, recent records of thermohaline profiles and water depth in the taam ja’ blue hole (chetumal bay, mexico).

• 1 Department of Observation and Study of the Land, the Atmosphere and the Ocean, Consejo Nacional de Humanidades, Ciencias y Tecnologías-El Colegio de la Frontera Sur (CONAHCYT-ECOSUR), Chetumal, Mexico
• 2 Department of Sustainability Sciences, El Colegio de la Frontera Sur, Chetumal, Mexico
• 3 Department of Observation and Study of the Land, the Atmosphere and the Ocean, El Colegio de la Frontera Sur, Chetumal, Mexico

Coastal karst structures have been recently explored and documented in Chetumal Bay, Mexico, at the southeast of the Yucatan Peninsula. These structures, recognized as blue holes, stand out for their remarkable dimensions within a shallow estuarine environment. Particularly the Taam Ja’ Blue Hole (TJBH), revealed a depth of ~274 mbsl based on echo sounder mapping, momentarily positioning it as the world's second-deepest blue hole. However, echo sounding methods face challenges in complex environments like blue holes or inland sinkholes arising from frequency-dependent detection and range limitations due to water density vertical gradients, cross-sectional depth variations, or morphometric deviations in non-strictly vertical caves. Initial exploration could not reach the bottom and confirm its position, prompting ongoing investigation into the geomorphological features of TJBH. Recent CTD profiler records in TJBH surpassed 420 mbsl with no bottom yet reached, establishing the TJBH as the deepest-known blue hole globally. Hydrographic data delineated multiple water layers within TJBH. Comparison with Caribbean water conditions at the Mesoamerican Barrier Reef System, reef lagoons, and estuaries suggests potential subterranean connections. Further research and implementation of underwater navigation technologies are essential to decipher its maximum depth and the possibilities of forming part of an interconnected system of caves and tunnels.

Introduction

Anchialine systems stand out as impressive and exciting environments to be explored across different disciplines. These systems provide a vast research field, from microbiology ( Benítez et al., 2019 ; Little et al., 2021 ; Sha et al., 2021 ), to sea-level dynamics or paleoclimate ( van Hengstum et al., 2011 ; Husson et al., 2018 ; van Hengstum et al., 2020 ; Wallace et al., 2021 ), stratigraphy ( Vimpere, 2017 ), physicochemical water properties ( Perry et al., 2002 , Perry et al., 2009 ), as well as groundwater hydrology ( Gondwe et al., 2010 ; Björnerås et al., 2020 ). However, a common basis across all disciplines is the need to understand the geomorphology and dimensions of the karst structures.

The Yucatan Peninsula, part of Central America's Maya block, lacks Paleozoic folds ( Weber et al., 2012 ). With dynamic diagenesis and gradual Pliocene emergence, it exhibits significant geological structures in vadose ( Perry et al., 2003 , Perry et al., 2009 ) and phreatic settings ( van Hengstum et al., 2010 , van Hengstum et al., 2011 ), as well as in coastal submarine environments ( Bauer-Gottwein et al., 2011a ). Moreover, the Yucatan Peninsula's northern side hosts the Ring of Cenotes Fault, a regional-scale structure formed by sinkholes, related to the Chicxulub meteorite impact 65 million years ago ( Bauer-Gottwein et al., 2011a ). Simultaneously, the world's most extensive subterranean cave system, shaped by glacio-eustatic sea-level changes, is found on the western side ( Supper et al., 2009 ; Kambesis and Coke, 2013 ). Across the eastern margin, parallel to the Caribbean coast, the Yucatan Peninsula features two regional fracture zones—the Holbox Fracture Zone to the north and the Rio Hondo Fault Zone to the south ( Bauer-Gottwein et al., 2011a ) with possible intersections and water exchange ( Gondwe et al., 2011 ). To the southeast, inland sinkholes and lagoons aligned with the Rio Hondo Fault Zone have been extensively studied (e.g. Gischler et al., 2011 ; Perry et al., 2021 ). Also, recent exploration in Chetumal Bay reported large coastal karstic formations recognized as blue holes ( Carrillo et al., 2009b ; Alcérreca-Huerta et al., 2023 ; Flórez-Franco et al., 2023 ). These blue holes represented an outstanding revelation, particularly that of the Taam-ja’ Blue Hole (TJBH), preliminarily recognized as the world's second-deepest, surpassing the depths of the Dean’s Blue Hole in the Bahamas (~202 mbsl) ( Vimpere, 2017 ), the Dahab Blue Hole in Egypt (~130 mbsl) ( Li et al., 2018 ), or the Great Blue Hole in Belize (~125 mbsl) ( Schmitt et al., 2021 ).

The TJBH, first documented by Alcérreca-Huerta et al. (2023) , stands as a noteworthy geological feature. Bathymetric mapping employing echo sounder technology indicated an impressive maximum depth of 274.4 meters below sea level (mbsl). Echo sounding, serving as an indirect method, allowed a comprehensive 3D spatial coverage of the TJBH morphology. However, this method could grapple with constraints arising from frequency-dependent detection and range limitations ( Colbo et al., 2014 ). These challenges are usually accentuated in blue holes and inland sinkholes due to fluctuations in water density ( Cejudo et al., 2022 ) and cross-sectional variations in depth ( Li et al., 2018 ), particularly in non-strictly vertical caves where the blue hole structure deviates from their entrance position. Direct methods for depth measurement employed in TJBH relied on CTD profiling but encountered limitations with measurements being restricted to a maximum depth of 200 mbsl to safeguard against potential instrument damage ( Alcérreca-Huerta et al., 2023 ; Flórez-Franco et al., 2023 ). Notably, the measurements could not reach the bottom and confirm its position, leaving the depths of TJBH and the vertical thermohaline structure partially unresolved.

Therefore, recent direct methods for water depth measurement gathered with a SWiFT CTD Profiler reveal water depths within the TJBH that surpassed the previous reported records, but also the maximum water depth record held by the Sansha Yongle Blue Hole (SYBH) at ~301 mbsl in the South China Sea ( Li et al., 2018 ). This groundbreaking finding establishes the TJBH as the recently confirmed deepest-known blue hole globally. Additionally, the hydrographic data collected is also described to delineate the water temperature and salinity variations along the recent depths reached, the formation of previously unknown pycnoclines, and comparison of the thermohaline conditions in TJBH with those found in the literature for waters in the Caribbean at the Mesoamerican Barrier Reef System and coastal reef lagoons, as a proxy of possible hydraulic connectivity between them and the blue hole.

Cenotes, underground springs, freshwater inlets, and a complex lagoon and anchialine system develop at the southeastern region of the Yucatán Peninsula ( Figure 1A ). The system connects with Chetumal Bay, a semi-closed mesohaline tropical estuary developed over carbonated sedimentary deposits of the Miocene, Mio-Pliocene and Holocene ( Gondwe et al., 2010 ; Domínguez-Herrera et al., 2023 ), which hydrographic conditions are described in Carrillo et al (2009a ), Carrillo et al (2009b) and Ruíz-Pineda et al. (2016) .

Figure 1 (A) Location of the Taam ja’ Blue Hole (TJBH) in Chetumal Bay, Mexico, is presented alongside the CC and CSW data regions for further comparison of water temperature and salinity conditions. Regional fracture zones and geological faults in the Yucatán Peninsula are indicated ( INEGI, 2002 ), along with the locations of documented blue holes within Chetumal Bay. CB data was measured at sampling stations positioned at cardinal positions ~500 m apart of the TJBH (TJBH N , TJBH S , TJBH E and TJBH W ). Images from scuba explorations of the TJBH at depths (B) 5.0 mbsl, (C) 20 mbsl, and (D) 30 mbsl are also presented.

The TJBH (378823 m E, 2059390 m N, UTM 16Q) is located in the central portion of Chetumal Bay, within the Mexican State Reserve “Chetumal Bay-Manatee Sanctuary” (RESMBCH). It is ~4.5 km from Tamalcab island, and ~19.2 km from Chetumal, the most urbanized area. TJBH, Lool ja’ Blue Hole (LJBH), and Ch’och-ja’ Blue Hole (CJBH) are among the blue holes recently documented in Chetumal Bay ( Carrillo et al., 2009b ; Alcérreca-Huerta et al., 2023 ; Flórez-Franco et al., 2023 ) ( Figure 1A ), for which preliminary insights into their geomorphological features, and temporal variability of physicochemical properties have been provided.

Field work and data analysis

On December 6 th , 2023, a scuba diving expedition was conducted to identify the environmental conditions prevailing at the TJBH and related to factors such as visibility, substrate characteristics, and wall coverage within a depth range extending from 0 to 30 mbsl. Additionally, on December 6 th and 13 th , 2023, measurement of new CTD profiles was conducted within the TJBH aiming to reach its bottom and confirm the echo-sounding results described in Alcérreca-Huerta et al. (2023) . Employing a SWiFT CTD Profiler (Valeport UK), single profiles at each campaign with simultaneous measurements of water pressure, temperature, and conductivity were acquired throughout the water column of TJBH. The coordinates for the CTD profiles were 378830.7 m E and 2059383.6 m N (UTM 16Q), selected based on preliminary echo sounding measurements that indicated water depths surpassing 250 mbsl. The vessel was anchored to prevent drifting caused by waves and currents. In this specific location, the CTD instrument was lowered, utilizing ~500 m of cable down to the bottom, adhering to the maximum depth supported by the instrument.

Salinity and density values from CTD casts are computed employing the Chen and Millero/UNESCO international algorithm ( Chen and Millero, 1977 ; Fofonoff and Millard, 1983 ), leading to an accuracy of ±0.01 PSU and ±0.01 kg/m³, respectively. Temperature data from SWiFT CTD Profiler measurements has an accuracy of ±0.01 °C. Data was resampled to achieve a fixed depth resolution of 0.5 m for the calculation of temperature (∂T/∂z), salinity (∂S/∂z), and density (∂ρ/∂z) vertical gradients, to delineate variations in these parameters with depth. The vertical gradient resulted from the absolute difference in a variable quantity over the vertical distance between their resampled measurement locations. Pycnoclines, indicative of density variations, were estimated by considering the maximum vertical density gradient surpassing a defined threshold of δ 1  = 0.5 kg·m 4 ( Read et al., 2011 ; Flórez-Franco et al., 2023 ). Building upon the findings by Flórez-Franco et al. (2023) , density transition zones are identified assuming a density gradient of δ 2  ≥ 0.05 kg·m 4 .

A temperature-salinity diagram was also devised to identify a potential relationship between the waters of the TJBH and those in coastal and open-sea waters in the Caribbean. For this purpose, existing hydrographic data from the Caribbean Surface Water (CSW data) at the Mesoamerican Barrier Reef (0-150 mbsl) delineated in Carrillo et al. (2016) was employed. Insights derived from data detailed in Tovar et al. (2009) , encompassing coastal reef lagoons within the Mexican Caribbean, were considered (CC data). Additionally, existing quarterly data measurements at stations ~500 m apart from the TJBH (i.e., TJBH N , TJBH S , TJBH E , TJBH W ) between March 2021 to December 2023, were used to describe the observed conditions within Chetumal Bay and in the vicinity of the TJBH (CB data). Location of the different comparative study areas (CB, CSW and CC) is depicted in Figure 1A .

The boundary of TJBH, clearly defined around 5.0 mbsl, features a soft substrate covered by biofilms, which extends across the upper walls of the blue hole ( Figure 1B ). The turbidity of Chetumal Bay's waters conceals this border from being visible at the surface. However, the border becomes clearly seen after a depth >4.0 mbsl The TJBH wall exhibits speleothem-like formations covered by biofilms, yet they are soft, fragile, and prone to collapse ( Figure 1C ). Beyond 25-30 mbsl, the wall steepens and develops a firm substrate. This substrate occasionally forms a tilted roof largely free of biofilms (i.e. 0-20% coverage), possibly due to limited natural light penetration ( Figure 1D ).

Profiles and vertical gradients of water temperature, salinity, and density are depicted in Figure 2 . The depths attained from CTD casts on both December 6 th and 13 th , 2023, recorded 416.0 and 423.6 mbsl, respectively. Consequently, these new findings unequivocally establish the Taam Ja’ Blue Hole (TJBH) as the world's deepest known blue hole, with its bottom still not reached.

Figure 2 Vertical profiles and gradients of (A) water temperature, (B) salinity, (C) density, and (D) sound speed measured on 06.12.2023 and 13.12.2023 in TJBH with a CTD profiler. Pycnoclines are given by the maximum density gradient above a threshold δ 1 =0.5 kg/m 4 . Regions next to the pycnoclines location with a density gradient δ 2 >0.05 kg/m 4 (TZ) are also shown.

The CTD measurements revealed a depth shorter than the cable length (~500 m) employed to lower the CTD profiler, indicating an oblique descent of the instrument at an angle of approximately 32.1-33.7° from the vertical. This deviation in orientation could be ascribed to either the specific geomorphology of the Taam Ja’ Blue Hole (TJBH) or the influence of prevailing underwater currents. Moreover, echo sounding data from prior investigations ( Alcérreca-Huerta et al., 2023 ) had reported a maximum depth of 274.4 mbsl, with the deeper regions of the TJBH concentrated predominantly on the northern side, where depths were in average 250 mbsl. This depth coincides with the location of a pycnocline, positioned at a depth of 246.1 mbsl. Consequently, it can be inferred that the echo sounding results reported by Alcérreca-Huerta et al. (2023) might have been affected by a possibly non-strictly vertical morphology of the TJBH or acoustic scattering given by fluctuations in water density ( Figure 2C, D ).

The development of four primary clines with density gradients exceeding 0.5 kg/m 4 is also shown in Figure 2A–C . Pycnoclines were delineated on average at 4.6-5.3 mbsl for the 1 st pycnocline, 246.1 mbsl for the 2 nd pycnocline, 323.3 mbsl for the 3 rd pycnocline, and 414.5 mbsl for the 4 th pycnocline. Transition zones (TZ) between layers above and below the pycnoclines are defined by gradients ∂ρ/∂z > 0.05 kg/m 4 .

The surface water layer (~0-4 mbsl) above the 1st pycnocline exhibits substantial variability in temperature (ranging from 24.9 to 27.9°C) and salinity (13.5-15.0 PSU) across measurements. Temperature and salinity variabilities decrease within the layers below the 1st pycnocline within the TJBH. On average, the layer between pycnoclines 1-2 describes an average temperature of 24.9±0.30 °C and salinity of 22.2±1.02 PSU within a depth range of 8 to 236 mbsl. In the layer encompassing depths of 249-313 mbsl (between pycnoclines 2-3), the average temperature decreases, while salinity increases, with values of 22.3±0.18 °C and 29.5±0.53 PSU, respectively. The layer below, spanning depths of 332-399 mbsl, registers an average salinity of 35.1±0.01 PSU and the lowest average temperature (19.8±0.01 °C). Beyond 400 mbsl, there is a significant increase in temperature within the transition zone, rising from 19.8 to 23.9 °C, accompanied by a salinity increase of up to 37.5 PSU and an average water density of 1027 kg/m 3 .

Possible hydrographic relationships across the TJBH, Chetumal Bay (CB), the Caribbean Surface Water (CSW) and Mexican Caribbean reef lagoons (CC) are explored in the temperature-salinity diagram in Figure 3 . The CB data presents a wide variability of temperature (>25°C) and salinity (<17 PSU) with water densities below 1010 kg/m 3 , similar to those observed in the surface layer above the entrance of TJBH. This reflects the influence of the estuarine Chetumal Bay water atop the TJBH entrance.

Figure 3 Temperature-salinity diagram for the water features corresponding to the TJBH. Water temperature and salinity from measured data in Chetumal Bay (CB) between 2021-2023 is also depicted together with data corresponding to the Caribbean Surface Water (CSW) for water depths 2-150 m ( Carrillo et al., 2016 ) and to reef lagoons in the Caribbean Coast (CC) ( Tovar et al., 2009 ). Curves show density in kilograms per cubic meter. Color bar refers to water depth in meters.

Beyond the depth of 400 mbsl within the TJBH, the water conditions gradually converge with those of in the Caribbean Sea (CSW and CC, Figure 3 ). Salinity levels in the Caribbean Surface Water reach up to 36.9 PSU, particularly at depths ranging from 115 to 150 mbsl, where the water densities are in average 1023±0.1 kg/m³ and reach up to 1026 kg/m³. These marine hydrographic values resemble the results obtained from CTD casts within TJBH at depths exceeding 400 mbsl with average salinity of 36.0±0.74 PSU and density of 1027±0.3 kg/m³. Similarly, data from the coastal reef lagoons of the Mexican Caribbean describe an average salinity value of 36.0±0.53 PSU, accompanied by water temperatures surpassing 18.3 °C and averaging approximately 27.9±2.48 °C. Coastal reef hydrographic data represents shallow areas (less than 9.5 mbsl) showing a wider range of density values between 1020 and 1026 kg/m³ with a mean value of 1023±0.8 kg/m³. This data alignment suggests a potential subterranean connection between these water bodies and the TJBH.

Discussion and concluding remarks

Hydrogeology and geomorphology of karst systems such as blue holes are highly valuable with implications for water resources, biodiversity, or physicochemical and geological processes. The initial results in Alcérreca-Huerta et al. (2023) yielded preliminary insights into the geomorphology, depths, and water properties of TJBH. Confirmation of the maximum depth was not possible due to instrumental limitations during the scientific expeditions in 2021, prompting the need for further exploration and analysis.

The recent records from CTD profiling in 2023 conclusively verifies that the TJBH is now the deepest blue hole discovered to date, exhibiting water depths surpassing 420 mbsl, with its bottom yet to be reached. In line with the approach undertaken by Li et al. (2018) , further investigations should incorporate advanced underwater navigation technologies in conjunction with CTD profilers. This integrated methodology would allow an accurate three-dimensional spatial representation of the TJBH leading to a detailed analysis on its geomorphological features and water depths.

CTD measurements provided valuable results into the temperature–salinity stratification of the TJBH, contributing to a more comprehensive understanding of its hydrographical characteristics. Variations in temperature and salinity within the water layers of the TJBH and the pycnoclines development offered insights of TJBH in relation to surrounding marine environments. In this regard, the CTD measurements hint potential yet undiscovered connections with the seawater of either the coastal reef lagoons or deeper coastal zones of the Mesoamerican Barrier Reef System. The notable increase of temperature (~ΔT>4.0 °C) and salinity (up to 37.5 PSU) at depths beyond 400 mbsl could probably be related to these connections. The increase in salinity may stem from various mechanisms, as delineated by Fleury et al. (2007) . These mechanisms could include salinization processes triggered by the inflow of marine water through a Venturi effect, water density differences ( Mijatovic, 1962 ; Fleury et al., 2007 ), or the difference in hydraulic head as long as that of the seawater is higher than that of the freshwater ( Whitaker and Smart, 1997 ). Thermal specific features could also be related to geological, volcanic or tectonic processes in relation to water circulation ( Šušmelj et al., 2024 ). The increase in water temperature at depths >400 mbsl in TJBH could be hypothesized to resemble that observed in the Floridian aquifer ( Meyer, 1989 ; Fleury et al., 2007 ), where geothermal activity warms cold seawater at deep layers, prompting its upward movement through existing sinkholes or factures at confining units. Subsequent interaction with the aquifer and the presence of further hydraulic connections with seawater could occur at upper layers, resulting in a reduction of the water temperature. This geothermal activity and the recharging areas from seawater have been related with fracture and fault zones in Florida ( Whitaker and Smart, 1997 ) and the Northern Adriatic Sea ( Šušmelj et al., 2024 ).

Research on blue holes encompasses a series of ambitious and exploration projects, often spanning several years or even decades, as occurred for the SYBH (e.g. Li et al., 2018 ; He et al., 2019 ; Xie et al., 2019 ; He et al., 2020 ; Jinwei et al., 2022 ; Chen et al., 2023 ) or the Bahamian blue holes (e.g. Bottrell et al., 1991 ; Mylroie, 2008 ; Gonzalez et al., 2011 ; Vimpere, 2017 ; van Hengstum et al., 2020 ; Sha et al., 2021 ). Moreover, the exploration and research of inland vertical caves, such as the Krubera–Voronya, the world's deepest known cave with a depth of 2191 meters, has continually set successive new depth records since 1960s ( Klimchouk et al., 2009 ; Klimchouk, 2019 ). This evinces the needs of continuous exploration of these karst geological structures, their intricate geomorphology, and the development of cave branches. Delving into the underwater spatial geomorphology of TJBH, the focus is on deciphering its maximum depth and the possibilities of forming part of an underwater intricate and potentially interconnected system of caves and tunnels.

Therefore, the new findings and the discovered challenging depths of TJBH entails a multifaceted inquiry encompassing various scientific dimensions. Efforts should extend to unravel the hydrogeology, stratification, and mixing processes within TJBH, delineating their relationship with regional water bodies, hydraulic connections, water quality dynamics, and water residence times. Within the depths of TJBH could also lie a biodiversity to be explored and linked to physicochemical and geomorphological processes, forming a unique biotope. Geological studies should extend to understanding TJBH's relationship with the fault and fracture system of the region (i.e. the Rio Hondo Fault Zone), with implications for its origin. Analyses are needed to describe the stratigraphic sequence within TJBH and potential connections between TJBH, other blue holes and cenotes in or nearby Chetumal Bay. Thus, uncovering the challenges and mysteries concealed in TJBH urges further exploration, monitoring, and scientific inquiry.

Data availability statement

The datasets presented in this article are not readily available because the data belong to a project funded by the authors. Once published, data eventually will be shared on the institutional data reservoir. Requests to access the datasets should be directed to Laura Carrillo, [email protected].

Author contributions

JA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Software, Visualization, Writing – original draft. OR: Investigation, Methodology, Writing – review & editing, Writing – original draft. JS: Investigation, Methodology, Writing – review & editing. TÁ: Investigation, Methodology, Writing – review & editing, Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Validation. LC: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review & editing, Data curation, Formal analysis.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The first author personally funded fieldwork expenses during the survey. The fourth author funded the acquisition of the SWiFT CTD Profiler for hydrographic measurements. APCs funded by El Colegio de la Frontera Sur.

Acknowledgments

The support of Mr. Jesús Artemio Poot Villa (COBIA Team) for their navigation services and support during field surveys is gratefully acknowledged. Technical support of Johnny Omar Valdez from UNAM-UMDI during the fieldwork and scuba-explorations in TJBH is highly appreciated and recognized. Permissions and collaboration with IBANQROO (Institute of Biodiversity and Protected Areas of the State of Quintana Roo) are accredited. Recognition is given to the CONAHCYT (Mexican National Council of Humanities, Sciences and Technologies) program ‘Investigadoras e Investigadores por México’ (Project 761).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: coastal karst structures, underwater geomorphology, blue holes, Yucatán Peninsula, Mexican Caribbean, cave system, anchialine system

Citation: Alcérreca-Huerta JC, Reyes-Mendoza OF, Sánchez-Sánchez JA, Álvarez-Legorreta T and Carrillo L (2024) Recent records of thermohaline profiles and water depth in the Taam ja’ Blue Hole (Chetumal Bay, Mexico). Front. Mar. Sci. 11:1387235. doi: 10.3389/fmars.2024.1387235

Received: 17 February 2024; Accepted: 15 April 2024; Published: 29 April 2024.

Reviewed by:

Copyright © 2024 Alcérreca-Huerta, Reyes-Mendoza, Sánchez-Sánchez, Álvarez-Legorreta and Carrillo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Laura Carrillo, [email protected]