9 Project Ideas for Your Data Analytics Portfolio

Finding the right data analyst portfolio projects can be tricky, especially when you’re new to the field. You might also think that your data projects need to be especially complex or showy, but that’s not the case. The most important thing is to demonstrate your skills, ideally using a dataset that interests you. And the good news? Data is everywhere—you just need to know where to find it and what to do with it.

A good option for getting experience in all domains is to take a  data analytics program  or course. We offer  a top-rated one here at CareerFoundry.

In this article, I’ll highlight the key elements that your data analytics portfolio should demonstrate. I’ll then share nine project ideas that will help you build your portfolio from scratch, focusing on three key areas: Data scraping , exploratory analysis , and data visualization .

Table of contents

  • What data analytics projects should you include in your portfolio?
  • Data scraping project ideas
  • Exploratory data analysis project ideas
  • Data visualization project ideas
  • What’s next?

Ready to get inspired? Let’s go!

1. What data analytics projects should you include in your portfolio?

Data analytics is all about finding insights that inform decision-making. But that’s just the end goal.

As any experienced analyst will tell you, the insights we see as consumers are the result of a great deal of work. In fact, about 80% of all data analytics tasks involve preparing data for analysis . This makes sense when you think about it—after all, our insights are only as good as the quality of our data.

Yes, your portfolio needs to show that you can carry out different types of data analysis . But it also needs to show that you can collect data, clean it, and report your findings in a clear, visual manner. As your skills improve, your portfolio will grow in complexity. As a beginner though, you’ll need to show that you can:

  • Scrape the web for data
  • Carry out exploratory analyses
  • Clean untidy datasets
  • Communicate your results using visualizations

If you’re inexperienced, it can help to present each item as a mini-data analyst portfolio project of its own . This makes life easier since you can learn the individual skills in a controlled way.

With that in mind, I’ll keep it nice and simple with some basic ideas, and a few tools you might want to explore to help you along the way.

2. Data scraping project ideas for your portfolio

What is data scraping.

Data scraping is the first step in any data analytics project. It involves pulling data (usually from the web) and compiling it into a usable format. While there’s no shortage of great repositories available online, scraping and cleaning data yourself is a great way to show off your skills.

The process of web scraping can be automated using tools like Parsehub , ScraperAPI , or Octoparse (for non-coders) or by using libraries like Beautiful Soup or Scrapy (for developers). Whichever tool you use, the important thing is to show that you understand how it works and can apply it effectively.

Before scraping a website, be sure that you have permission to do so. If you’re not certain, you can always search for a dataset on a repository site like  Kaggle . If it exists there, it’s a good bet you can go straight to the source and scrape it yourself. Bear in mind though—data scraping can be challenging if you’re mining complex, dynamic websites. We recommend starting with something easy—a mostly-static site. Here are some ideas to get you started.

Data scraping portfolio project ideas

The internet movie database.

A good beginner’s project is to extract data from IMDb. You can collect details about popular TV shows, movie reviews and trivia, the heights and weights of various actors, and so on. This kind of information on IMDb is stored in a consistent format across all its pages, making the task a lot easier. There’s also a lot of potential here for further analysis.

Job portals

Many beginners like scraping data from job portals since they often contain standard data types. You can also find lots of online tutorials explaining how to proceed.

To keep it interesting, why not focus on your local area? Collect job titles, companies, salaries, locations, required skills, and so on. This offers great potential for later visualization, such as graphing skillsets against salaries.

E-commerce sites

Another popular one is to scrape product and pricing data from e-commerce sites. For instance, extract product information about Bluetooth speakers on Amazon, or collect reviews and prices on various tablets and laptops.

Once again, this is relatively straightforward to do, and it’s scalable . This means you can start with a product that has a small number of reviews, and then upscale once you’re comfortable using the algorithms.

For something a bit less conventional, another option is to scrape a site like Reddit. You could search for particular keywords, upvotes, user data, and more. Reddit is a very static website, making the task nice and straightforward. You could even scrape Reddit for useful data analytics advice .

Later, you can carry out interesting exploratory analyses, for instance, to see if there are any correlations between popular posts and particular keywords. Which brings me to our next section…

3. Exploratory data analysis project ideas

What is exploratory data analysis.

The next step in any data analyst’s skillset is the ability to carry out an exploratory data analysis (EDA). An EDA looks at the structure of data, allowing you to determine their patterns and characteristics. They also help you to it up. You can extract important variables, detect outliers and anomalies, and generally test your underlying assumptions.

While this process is one of the most time-consuming tasks for a data analyst, it can also be one of the most rewarding. Later modeling focuses on generating answers to specific questions. An EDA, meanwhile, helps you do one of the most exciting bits—generating those questions in the first place.

Languages like R and Python are often used to carry out these tasks. They have many pre-existing algorithms that you can use to carry out the work for you . The real skill lies in presenting your project and its results. How you decide to do this is up to you, but one popular method is to use an interactive documentation tool like Jupyter Notebook . This lets you capture elements of code, along with explanatory text and visualizations, all in one place. Here are some ideas for your portfolio.

Exploratory data analysis portfolio project ideas

Global suicide rates.

This global suicide rates dataset covers suicide rates in various countries, with additional data including year, gender, age, population, GDP, and more. When carrying out your EDA, ask yourself: What patterns can you see? Are suicides rates climbing or falling in various countries? What variables (such as gender or age) can you find that might correlate to suicide rates?

World Happiness Report

On the other end of the scale, the World Happiness Report tracks six factors to measure happiness across the world’s citizens: life expectancy, economics, social support, absence of corruption, freedom, and generosity. So, which country is the happiest? Which continent? Which factor appears to have the greatest (or smallest) impact on a nation’s happiness? Overall, is happiness increasing or decreasing? Access the happiness data over on Kaggle .

Create your own!

Aside from the two ideas above, you could also use your own datasets . After all, if you’ve already scraped your own data, why not use them? For instance, if you scraped a job portal, which locations or regions offer the best-paid jobs? Which offer the least well-paid ones? Why might that be? Equally, with e-commerce data, you could look at which prices and products offer the best value for money.

Ultimately, whichever dataset you’re using, it should grab your attention. If the information is too complex or don’t interest you, you’re likely to run out of steam before you get very far. Keep in mind what further probing you can do to spot interesting trends or patterns, and to extract the insights you need.

We’ve compiled a list of ten great places to find free datasets for your next project .

4. Data visualization project ideas

What is data visualization.

Scraping, tidying, and analyzing data is one thing. Communicating your findings is another. Our brains don’t like looking at numbers and figures, but they love visuals. This is where the ability to create effective data visualizations comes in.

Good visualizations—whether static or interactive—make a great addition to any data analytics portfolio. Showing that you can create visualizations that are both effective and visually appealing will go a long way towards impressing a potential employer.

Some free visualization tools include Google Charts , Canva Graph Maker , and Tableau Public . Meanwhile, if you want to show off your coding abilities, use a Python library such as Seaborn , or flex your R skills with Shiny . Needless to say, there are many tools available to help you. The one you choose depends on what you’re looking to achieve. Here’s a bit of inspiration…

Data visualization portfolio project ideas

Topical subject matter looks great on any portfolio, and the pandemic is nothing if not topical! What’s more, sites like Kaggle already have thousands of Covid-19 datasets available .

How can you represent the data? Could you use a global heatmap to show where cases have spiked, versus where there are very few? Perhaps you could create two overlapping bar charts to show known infections versus predicted infections. Here’s a handy tutorial to help you visualize Covid-19 data using R, Shiny, and Plotly .

Most followed on Instagram

Whether you’re interested in social media, or celebrity and brand culture, this dataset of the most-followed people on Instagram has great potential for visualization. You could create an interactive bar chart that tracks changes in the most followed accounts over time. Or you could explore whether brand or celebrity accounts are more effective at influencer marketing.

Otherwise, why not find another social media dataset to create a visualization? For instance, data scientist Greg Rafferty’s map of the USA nicely highlights the geographical source of trending topics on Instagram.

Travel data

Another topic that lends itself well to visualization is transport data. There’s a great project by Chen Chen on github , using Python to visualize the top tourist destinations worldwide, and the correlation between inbound/outbound tourists with gross domestic product (GDP).

5. What’s next?

In this post, we’ve explored which skills every beginner needs to demonstrate through their data analytics portfolio project ideas. Regardless of the dataset you’re using, you should be able to demonstrate the following abilities:

  • Web scraping —using tools like Parsehub, Beautiful Soup, or Scrapy to extract data from websites (remember: static ones are easier!)
  • Exploratory data analysis and data cleaning —manipulating data with tools like R and Python, before drawing some initial insights.
  • Data visualization —utilizing tools like Tableau, Shiny, or Plotly to create crisp, compelling dashboards, and visualizations.

Once you’ve mastered the basics, you can start getting more ambitious with your data analytics projects. For example, why not introduce some machine learning projects , like sentiment analysis or predictive analysis? The key thing is to start simple and to remember that a good portfolio needn’t be flashy, just competent.

To further develop your skills, there are loads of online courses designed to set you on the right track. To start with, why not try our free, five-day data analytics short course ?

And, if you’d like to learn more about becoming a data analyst and building your portfolio, check out the following:

  • How to build a data analytics portfolio 
  • The best data analytics certification programs on the market right now
  • These are the most common data analytics interview questions

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10 Unique Data Science Capstone Project Ideas

A capstone project is a culminating assignment that allows students to demonstrate the skills and knowledge they’ve acquired throughout their degree program. For data science students, it’s a chance to tackle a substantial real-world data problem.

If you’re short on time, here’s a quick answer to your question: Some great data science capstone ideas include analyzing health trends, building a predictive movie recommendation system, optimizing traffic patterns, forecasting cryptocurrency prices, and more .

In this comprehensive guide, we will explore 10 unique capstone project ideas for data science students. We’ll overview potential data sources, analysis methods, and practical applications for each idea.

Whether you want to work with social media datasets, geospatial data, or anything in between, you’re sure to find an interesting capstone topic.

Project Idea #1: Analyzing Health Trends

When it comes to data science capstone projects, analyzing health trends is an intriguing idea that can have a significant impact on public health. By leveraging data from various sources, data scientists can uncover valuable insights that can help improve healthcare outcomes and inform policy decisions.

Data Sources

There are several data sources that can be used to analyze health trends. One of the most common sources is electronic health records (EHRs), which contain a wealth of information about patient demographics, medical history, and treatment outcomes.

Other sources include health surveys, wearable devices, social media, and even environmental data.

Analysis Approaches

When analyzing health trends, data scientists can employ a variety of analysis approaches. Descriptive analysis can provide a snapshot of current health trends, such as the prevalence of certain diseases or the distribution of risk factors.

Predictive analysis can be used to forecast future health outcomes, such as predicting disease outbreaks or identifying individuals at high risk for certain conditions. Machine learning algorithms can be trained to identify patterns and make accurate predictions based on large datasets.


The applications of analyzing health trends are vast and far-reaching. By understanding patterns and trends in health data, policymakers can make informed decisions about resource allocation and public health initiatives.

Healthcare providers can use these insights to develop personalized treatment plans and interventions. Researchers can uncover new insights into disease progression and identify potential targets for intervention.

Ultimately, analyzing health trends has the potential to improve overall population health and reduce healthcare costs.

Project Idea #2: Movie Recommendation System

When developing a movie recommendation system, there are several data sources that can be used to gather information about movies and user preferences. One popular data source is the MovieLens dataset, which contains a large collection of movie ratings provided by users.

Another source is IMDb, a trusted website that provides comprehensive information about movies, including user ratings and reviews. Additionally, streaming platforms like Netflix and Amazon Prime also provide access to user ratings and viewing history, which can be valuable for building an accurate recommendation system.

There are several analysis approaches that can be employed to build a movie recommendation system. One common approach is collaborative filtering, which uses user ratings and preferences to identify patterns and make recommendations based on similar users’ preferences.

Another approach is content-based filtering, which analyzes the characteristics of movies (such as genre, director, and actors) to recommend similar movies to users. Hybrid approaches that combine both collaborative and content-based filtering techniques are also popular, as they can provide more accurate and diverse recommendations.

A movie recommendation system has numerous applications in the entertainment industry. One application is to enhance the user experience on streaming platforms by providing personalized movie recommendations based on individual preferences.

This can help users discover new movies they might enjoy and improve overall satisfaction with the platform. Additionally, movie recommendation systems can be used by movie production companies to analyze user preferences and trends, aiding in the decision-making process for creating new movies.

Finally, movie recommendation systems can also be utilized by movie critics and reviewers to identify movies that are likely to be well-received by audiences.

For more information on movie recommendation systems, you can visit https://www.kaggle.com/rounakbanik/movie-recommender-systems or https://www.researchgate.net/publication/221364567_A_new_movie_recommendation_system_for_large-scale_data .

Project Idea #3: Optimizing Traffic Patterns

When it comes to optimizing traffic patterns, there are several data sources that can be utilized. One of the most prominent sources is real-time traffic data collected from various sources such as GPS devices, traffic cameras, and mobile applications.

This data provides valuable insights into the current traffic conditions, including congestion, accidents, and road closures. Additionally, historical traffic data can also be used to identify recurring patterns and trends in traffic flow.

Other data sources that can be used include weather data, which can help in understanding how weather conditions impact traffic patterns, and social media data, which can provide information about events or incidents that may affect traffic.

Optimizing traffic patterns requires the use of advanced data analysis techniques. One approach is to use machine learning algorithms to predict traffic patterns based on historical and real-time data.

These algorithms can analyze various factors such as time of day, day of the week, weather conditions, and events to predict traffic congestion and suggest alternative routes.

Another approach is to use network analysis to identify bottlenecks and areas of congestion in the road network. By analyzing the flow of traffic and identifying areas where traffic slows down or comes to a halt, transportation authorities can make informed decisions on how to optimize traffic flow.

The optimization of traffic patterns has numerous applications and benefits. One of the main benefits is the reduction of traffic congestion, which can lead to significant time and fuel savings for commuters.

By optimizing traffic patterns, transportation authorities can also improve road safety by reducing the likelihood of accidents caused by congestion.

Additionally, optimizing traffic patterns can have positive environmental impacts by reducing greenhouse gas emissions. By minimizing the time spent idling in traffic, vehicles can operate more efficiently and emit fewer pollutants.

Furthermore, optimizing traffic patterns can have economic benefits by improving the flow of goods and services. Efficient traffic patterns can reduce delivery times and increase productivity for businesses.

Project Idea #4: Forecasting Cryptocurrency Prices

With the growing popularity of cryptocurrencies like Bitcoin and Ethereum, forecasting their prices has become an exciting and challenging task for data scientists. This project idea involves using historical data to predict future price movements and trends in the cryptocurrency market.

When working on this project, data scientists can gather cryptocurrency price data from various sources such as cryptocurrency exchanges, financial websites, or APIs. Websites like CoinMarketCap (https://coinmarketcap.com/) provide comprehensive data on various cryptocurrencies, including historical price data.

Additionally, platforms like CryptoCompare (https://www.cryptocompare.com/) offer real-time and historical data for different cryptocurrencies.

To forecast cryptocurrency prices, data scientists can employ various analysis approaches. Some common techniques include:

  • Time Series Analysis: This approach involves analyzing historical price data to identify patterns, trends, and seasonality in cryptocurrency prices. Techniques like moving averages, autoregressive integrated moving average (ARIMA), or exponential smoothing can be used to make predictions.
  • Machine Learning: Machine learning algorithms, such as random forests, support vector machines, or neural networks, can be trained on historical cryptocurrency data to predict future price movements. These algorithms can consider multiple variables, such as trading volume, market sentiment, or external factors, to make accurate predictions.
  • Sentiment Analysis: This approach involves analyzing social media sentiment and news articles related to cryptocurrencies to gauge market sentiment. By considering the collective sentiment, data scientists can predict how positive or negative sentiment can impact cryptocurrency prices.

Forecasting cryptocurrency prices can have several practical applications:

  • Investment Decision Making: Accurate price forecasts can help investors make informed decisions when buying or selling cryptocurrencies. By considering the predicted price movements, investors can optimize their investment strategies and potentially maximize their returns.
  • Trading Strategies: Traders can use price forecasts to develop trading strategies, such as trend following or mean reversion. By leveraging predicted price movements, traders can make profitable trades in the volatile cryptocurrency market.
  • Risk Management: Cryptocurrency price forecasts can help individuals and organizations manage their risk exposure. By understanding potential price fluctuations, risk management strategies can be implemented to mitigate losses.

Project Idea #5: Predicting Flight Delays

One interesting and practical data science capstone project idea is to create a model that can predict flight delays. Flight delays can cause a lot of inconvenience for passengers and can have a significant impact on travel plans.

By developing a predictive model, airlines and travelers can be better prepared for potential delays and take appropriate actions.

To create a flight delay prediction model, you would need to gather relevant data from various sources. Some potential data sources include:

  • Flight data from airlines or aviation organizations
  • Weather data from meteorological agencies
  • Historical flight delay data from airports

By combining these different data sources, you can build a comprehensive dataset that captures the factors contributing to flight delays.

Once you have collected the necessary data, you can employ different analysis approaches to predict flight delays. Some common approaches include:

  • Machine learning algorithms such as decision trees, random forests, or neural networks
  • Time series analysis to identify patterns and trends in flight delay data
  • Feature engineering to extract relevant features from the dataset

By applying these analysis techniques, you can develop a model that can accurately predict flight delays based on the available data.

The applications of a flight delay prediction model are numerous. Airlines can use the model to optimize their operations, improve scheduling, and minimize disruptions caused by delays. Travelers can benefit from the model by being alerted in advance about potential delays and making necessary adjustments to their travel plans.

Additionally, airports can use the model to improve resource allocation and manage passenger flow during periods of high delay probability. Overall, a flight delay prediction model can significantly enhance the efficiency and customer satisfaction in the aviation industry.

Project Idea #6: Fighting Fake News

With the rise of social media and the easy access to information, the spread of fake news has become a significant concern. Data science can play a crucial role in combating this issue by developing innovative solutions.

Here are some aspects to consider when working on a project that aims to fight fake news.

When it comes to fighting fake news, having reliable data sources is essential. There are several trustworthy platforms that provide access to credible news articles and fact-checking databases. Websites like Snopes and FactCheck.org are good starting points for obtaining accurate information.

Additionally, social media platforms such as Twitter and Facebook can be valuable sources for analyzing the spread of misinformation.

One approach to analyzing fake news is by utilizing natural language processing (NLP) techniques. NLP can help identify patterns and linguistic cues that indicate the presence of misleading information.

Sentiment analysis can also be employed to determine the emotional tone of news articles or social media posts, which can be an indicator of potential bias or misinformation.

Another approach is network analysis, which focuses on understanding how information spreads through social networks. By analyzing the connections between users and the content they share, it becomes possible to identify patterns of misinformation dissemination.

Network analysis can also help in identifying influential sources and detecting coordinated efforts to spread fake news.

The applications of a project aiming to fight fake news are numerous. One possible application is the development of a browser extension or a mobile application that provides users with real-time fact-checking information.

This tool could flag potentially misleading articles or social media posts and provide users with accurate information to help them make informed decisions.

Another application could be the creation of an algorithm that automatically identifies fake news articles and separates them from reliable sources. This algorithm could be integrated into news aggregation platforms to help users distinguish between credible and non-credible information.

Project Idea #7: Analyzing Social Media Sentiment

Social media platforms have become a treasure trove of valuable data for businesses and researchers alike. When analyzing social media sentiment, there are several data sources that can be tapped into. The most popular ones include:

  • Twitter: With its vast user base and real-time nature, Twitter is often the go-to platform for sentiment analysis. Researchers can gather tweets containing specific keywords or hashtags to analyze the sentiment of a particular topic.
  • Facebook: Facebook offers rich data for sentiment analysis, including posts, comments, and reactions. Analyzing the sentiment of Facebook posts can provide valuable insights into user opinions and preferences.
  • Instagram: Instagram’s visual nature makes it an interesting platform for sentiment analysis. By analyzing the comments and captions on Instagram posts, researchers can gain insights into the sentiment associated with different images or topics.
  • Reddit: Reddit is a popular platform for discussions on various topics. By analyzing the sentiment of comments and posts on specific subreddits, researchers can gain insights into the sentiment of different communities.

These are just a few examples of the data sources that can be used for analyzing social media sentiment. Depending on the research goals, other platforms such as LinkedIn, YouTube, and TikTok can also be explored.

When it comes to analyzing social media sentiment, there are various approaches that can be employed. Some commonly used analysis techniques include:

  • Lexicon-based analysis: This approach involves using predefined sentiment lexicons to assign sentiment scores to words or phrases in social media posts. By aggregating these scores, researchers can determine the overall sentiment of a post or a collection of posts.
  • Machine learning: Machine learning algorithms can be trained to classify social media posts into positive, negative, or neutral sentiment categories. These algorithms learn from labeled data and can make predictions on new, unlabeled data.
  • Deep learning: Deep learning techniques, such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs), can be used to capture the complex patterns and dependencies in social media data. These models can learn to extract sentiment information from textual or visual content.

It is important to note that the choice of analysis approach depends on the specific research objectives, available resources, and the nature of the social media data being analyzed.

Analyzing social media sentiment has a wide range of applications across different industries. Here are a few examples:

  • Brand reputation management: By analyzing social media sentiment, businesses can monitor and manage their brand reputation. They can identify potential issues, respond to customer feedback, and take proactive measures to maintain a positive image.
  • Market research: Social media sentiment analysis can provide valuable insights into consumer opinions and preferences. Businesses can use this information to understand market trends, identify customer needs, and develop targeted marketing strategies.
  • Customer feedback analysis: Social media sentiment analysis can help businesses understand customer satisfaction levels and identify areas for improvement. By analyzing sentiment in customer feedback, companies can make data-driven decisions to enhance their products or services.
  • Public opinion analysis: Researchers can analyze social media sentiment to study public opinion on various topics, such as political events, social issues, or product launches. This information can be used to understand public sentiment, predict trends, and inform decision-making.

These are just a few examples of how analyzing social media sentiment can be applied in real-world scenarios. The insights gained from sentiment analysis can help businesses and researchers make informed decisions, improve customer experience, and drive innovation.

Project Idea #8: Improving Online Ad Targeting

Improving online ad targeting involves analyzing various data sources to gain insights into users’ preferences and behaviors. These data sources may include:

  • Website analytics: Gathering data from websites to understand user engagement, page views, and click-through rates.
  • Demographic data: Utilizing information such as age, gender, location, and income to create targeted ad campaigns.
  • Social media data: Extracting data from platforms like Facebook, Twitter, and Instagram to understand users’ interests and online behavior.
  • Search engine data: Analyzing search queries and user behavior on search engines to identify intent and preferences.

By combining and analyzing these diverse data sources, data scientists can gain a comprehensive understanding of users and their ad preferences.

To improve online ad targeting, data scientists can employ various analysis approaches:

  • Segmentation analysis: Dividing users into distinct groups based on shared characteristics and preferences.
  • Collaborative filtering: Recommending ads based on users with similar preferences and behaviors.
  • Predictive modeling: Developing algorithms to predict users’ likelihood of engaging with specific ads.
  • Machine learning: Utilizing algorithms that can continuously learn from user interactions to optimize ad targeting.

These analysis approaches help data scientists uncover patterns and insights that can enhance the effectiveness of online ad campaigns.

Improved online ad targeting has numerous applications:

  • Increased ad revenue: By delivering more relevant ads to users, advertisers can expect higher click-through rates and conversions.
  • Better user experience: Users are more likely to engage with ads that align with their interests, leading to a more positive browsing experience.
  • Reduced ad fatigue: By targeting ads more effectively, users are less likely to feel overwhelmed by irrelevant or repetitive advertisements.
  • Maximized ad budget: Advertisers can optimize their budget by focusing on the most promising target audiences.

Project Idea #9: Enhancing Customer Segmentation

Enhancing customer segmentation involves gathering relevant data from various sources to gain insights into customer behavior, preferences, and demographics. Some common data sources include:

  • Customer transaction data
  • Customer surveys and feedback
  • Social media data
  • Website analytics
  • Customer support interactions

By combining data from these sources, businesses can create a comprehensive profile of their customers and identify patterns and trends that will help in improving their segmentation strategies.

There are several analysis approaches that can be used to enhance customer segmentation:

  • Clustering: Using clustering algorithms to group customers based on similar characteristics or behaviors.
  • Classification: Building predictive models to assign customers to different segments based on their attributes.
  • Association Rule Mining: Identifying relationships and patterns in customer data to uncover hidden insights.
  • Sentiment Analysis: Analyzing customer feedback and social media data to understand customer sentiment and preferences.

These analysis approaches can be used individually or in combination to enhance customer segmentation and create more targeted marketing strategies.

Enhancing customer segmentation can have numerous applications across industries:

  • Personalized marketing campaigns: By understanding customer preferences and behaviors, businesses can tailor their marketing messages to individual customers, increasing the likelihood of engagement and conversion.
  • Product recommendations: By segmenting customers based on their purchase history and preferences, businesses can provide personalized product recommendations, leading to higher customer satisfaction and sales.
  • Customer retention: By identifying at-risk customers and understanding their needs, businesses can implement targeted retention strategies to reduce churn and improve customer loyalty.
  • Market segmentation: By identifying distinct customer segments, businesses can develop tailored product offerings and marketing strategies for each segment, maximizing the effectiveness of their marketing efforts.

Project Idea #10: Building a Chatbot

A chatbot is a computer program that uses artificial intelligence to simulate human conversation. It can interact with users in a natural language through text or voice. Building a chatbot can be an exciting and challenging data science capstone project.

It requires a combination of natural language processing, machine learning, and programming skills.

When building a chatbot, data sources play a crucial role in training and improving its performance. There are various data sources that can be used:

  • Chat logs: Analyzing existing chat logs can help in understanding common user queries, responses, and patterns. This data can be used to train the chatbot on how to respond to different types of questions and scenarios.
  • Knowledge bases: Integrating a knowledge base can provide the chatbot with a wide range of information and facts. This can be useful in answering specific questions or providing detailed explanations on certain topics.
  • APIs: Utilizing APIs from different platforms can enhance the chatbot’s capabilities. For example, integrating a weather API can allow the chatbot to provide real-time weather information based on user queries.

There are several analysis approaches that can be used to build an efficient and effective chatbot:

  • Natural Language Processing (NLP): NLP techniques enable the chatbot to understand and interpret user queries. This involves tasks such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis.
  • Intent recognition: Identifying the intent behind user queries is crucial for providing accurate responses. Machine learning algorithms can be trained to classify user intents based on the input text.
  • Contextual understanding: Chatbots need to understand the context of the conversation to provide relevant and meaningful responses. Techniques such as sequence-to-sequence models or attention mechanisms can be used to capture contextual information.

Chatbots have a wide range of applications in various industries:

  • Customer support: Chatbots can be used to handle customer queries and provide instant support. They can assist with common troubleshooting issues, answer frequently asked questions, and escalate complex queries to human agents when necessary.
  • E-commerce: Chatbots can enhance the shopping experience by assisting users in finding products, providing recommendations, and answering product-related queries.
  • Healthcare: Chatbots can be deployed in healthcare settings to provide preliminary medical advice, answer general health-related questions, and assist with appointment scheduling.

Building a chatbot as a data science capstone project not only showcases your technical skills but also allows you to explore the exciting field of artificial intelligence and natural language processing.

It can be a great opportunity to create a practical and useful tool that can benefit users in various domains.

Completing an in-depth capstone project is the perfect way for data science students to demonstrate their technical skills and business acumen. This guide outlined 10 unique project ideas spanning industries like healthcare, transportation, finance, and more.

By identifying the ideal data sources, analysis techniques, and practical applications for their chosen project, students can produce an impressive capstone that solves real-world problems and showcases their abilities.

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It will be applied various Data Analytics skills and techniques that wehave learned in the IBM Data Analyst Professional Certificate. It will be assumed the role of an Associate Data Analyst and be presented with a business challenge that requires data analysis to be performed on real-world datasets.


Folders and files, repository files navigation, ibm-data-analyst-capstone-project.

In this project we will assume that we have recently been hired as a Data Analyst by a global IT and business consulting services firm that is known for their expertise in IT solutions and their team of highly experienced IT consultants. In order to keep pace with changing technologies and remain competitive, our organization regularly analyzes data to help identify future skill requirements.

As a Data Analyst, we will be assisting with this initiative and have been tasked with collecting data from various sources and identifying trends for this year's report on emerging skills.

Our first task is to collect the top programming skills that are most in demand from various sources including: Job postings / Training portals / Surveys

Once we have collected enough data, we will begin analyzing the data and identify insights and trends.

Each step of the data analysis process is shown in different notebook of this repository and some aditional files needed for their understanding.

  • Jupyter Notebook 100.0%


  1. Capstone Projects 2020

    capstone project data analyst

  2. Capstone Project Ideas For Data Analytics

    capstone project data analyst

  3. IBM Data Analyst Capstone Project

    capstone project data analyst

  4. Data Analyst Capstone Project

    capstone project data analyst

  5. Analyzing Your Capstone Project Data (Including Survey Analysis and Content Analysis Methods)

    capstone project data analyst

  6. Google Data Analytics Certificate Course 8 of 8

    capstone project data analyst


  1. Capstone Project Data Analytics -RevoU

  2. A Data Analysis Presentation My Capstone Project on a Bike Share Company

  3. Demonstration Video Capstone Project Demeter

  4. Data Science Capstone Project Spotlight: Language Detection App

  5. 41-Configuring data alerts

  6. Data Analysis, Diet, and Alzheimer's


  1. Fissayo/IBM-Data-Analyst-Capstone-project

    This is my capstone project from the IBM Data Analyst course. In each analytics process, the data is stored in the Jupyter notebooks that are uploaded. Week 1 in the project is data collection; 2 is data wrangling, 3 is exploratory data analysis, 4 is Data visualisation, 5 is Building a dashboard and the last week is Presentation of findings.

  2. IBM Data Analyst Capstone Project

    By completing this final capstone project you will apply various Data Analytics skills and techniques that you have learned as part of the previous courses in the IBM Data Analyst Professional Certificate. You will assume the role of an Associate Data Analyst who has recently joined the organization and be presented with a business challenge ...

  3. GitHub

    When the data is ready you will then want to apply statistical techniques to analyze the data. Then bring all of your information together by using IBM Cognos Analytics to create your dashboard. And finally, show off your storytelling skills by sharing your findings in a presentation. You will be evaluated using quizzes in each module as well ...

  4. GitHub

    Capstone projects of the IBM Data Analyst Professional Topics. python data-science pandas data-visualization data-analysis data-manipulation data-analyst data-visualizations analyzing-data ibm-datascience-certification Resources. Readme Activity. Stars. 98 stars Watchers. 2 watching Forks. 47 forks

  5. IBM Data Analyst Capstone Project

    You will perform the various tasks that professional data analysts do as part of their jobs, including: - Data collection from multiple sources - Data wrangling and data preparation - Exploratory data analysis - Statistical analysis and data mining - Data visualization with different charts and plots, and - Interactive dashboard creation.

  6. How I created my first Data Analytics Capstone Project

    I completed this Data Analytics Capstone Project as a part of Google Data Analytics Professional Course on Coursera. Check even this blog for more about Business Intelligence v/s Business Analytics…

  7. Google Data Analytics Capstone: Complete a Case Study

    Module 1 • 2 hours to complete. A capstone is a crowning achievement. In this part of the course, you'll be introduced to capstone projects, case studies, and portfolios, and will learn how they help employers better understand your skills and capabilities. You'll also have an opportunity to explore the online portfolios of real data ...

  8. IBM Data Analyst Capstone Project

    Data Collection is the first step in solving any analysis problem and can be collected in many formats and from many sources. In the first module of the Capstone, we will collect data by scraping the internet and using web APIs. Data Wrangling. In this module, you will be focusing on the cleaning of your dataset with various techniques.

  9. Google Data Analytics Capstone Project

    Google Data Analytics Capstone Project. Updated: Jul 5, 2023. I worked on the Google Data Analytics Capstone Project, Track 1, Case Study 1. I will be diving into the background, my full process of cleaning, analyzing and visualizing the data, along with my final suggestions and summary of the data. Quick Links:

  10. Google Data Analytics Capstone Project: Cyclistic Case Study

    Background. In this case study, I am assuming the position of 'Jr. Data Analyst' at Cyclistic, a bike-share company based in Chicago. Cyclistic offers over 6000 bikes at 800+ docking stations ...

  11. Cyclistic: Google Data Analyst Certificate Capstone Project

    This is a capstone projects for the google data analytics professional certificate. In this project, we will try to solve questions that… 8 min read · Jan 23, 2024

  12. 9 Project Ideas for Your Data Analytics Portfolio

    As a beginner though, you'll need to show that you can: Scrape the web for data. Carry out exploratory analyses. Clean untidy datasets. Communicate your results using visualizations. If you're inexperienced, it can help to present each item as a mini-data analyst portfolio project of its own.

  13. IBM Data Analyst Capstone Project

    Offered by IBM. By completing this final capstone project you will apply various Data Analytics skills and techniques that you have learned ... Enroll for free.

  14. Badge: Data Analyst Capstone Project

    This badge earner has demonstrated an application of Data Analytics skills and techniques using a hands-on project involving a real-world dataset. The individual has gathered the required datasets, cleaned and wrangled data, performed data mining and analysis, created charts and visualizations, built interactive dashboards, and presented the results using a PowerPoint presentation.

  15. 10 Unique Data Science Capstone Project Ideas

    Project Idea #10: Building a Chatbot. A chatbot is a computer program that uses artificial intelligence to simulate human conversation. It can interact with users in a natural language through text or voice. Building a chatbot can be an exciting and challenging data science capstone project.

  16. GitHub

    IBM-Data-Analyst-Capstone-Project. The attached file comprises a comprehensive collection of capstone projects from IBM's Data Analyst Profeesional Certificate program as the final project prior to completing the course. These projects showcase the practical application of data analysis methodologies and techniques to real-world scenario of ...

  17. 5 Data Analyst Projects to Land a Job in 2024

    1. Job Trends Monitoring Dashboard. The first project is a dashboard displaying job trends in the data industry. I found this project in a video created by Luke Barousse, a former lead data analyst who also specializes in content creation. Here is a screenshot of this dashboard:

  18. Data Analysis Projects [Beginner to Advanced]

    Messy data leads to unreliable outcomes. Cleaning data is an essential part of data analysis, and demonstrating your data cleaning skills is key to landing a job. Here are some projects to get you started: Airbnb Open Data (New York) - Airbnb's open API lets you extract data on Airbnb stays from the company's website.

  19. Data Analytics Certificate & Training

    An introduction to data analytics. In this program, you'll be introduced to the world of data analytics through hands-on curriculum developed by Google. You'll develop in-demand data analytics skills using spreadsheets, SQL, Tableau, R, and more. This will help equip you with the skills you need to apply for entry-level data analyst roles.

  20. BWalliz/IBM-DA-Capstone: IBM Data Analyst Capstone Project

    IBM DA Capstone. This repository contains the Capstone Project for the IBM Data Analyst Certificate by Coursera. This repository contains the raw data, jupyter notebooks under Cheat Sheets, and visualizations of the results. The dashboard can be viewed from the link below:

  21. AFOTEC analyst training course adds new Capstone project

    In the most recent iteration of the course, the course cadre introduced a new Capstone project which provides practical application of the advanced methods. This project builds upon the classroom-based sections of the course which introduce the test and evaluation concepts of: outlining the design space ; designing the test ; collecting test data

  22. Google Data Analytics Capstone: Complete a Case Study

    Module 1 • 2 hours to complete. A capstone is a crowning achievement. In this part of the course, you'll be introduced to capstone projects, case studies, and portfolios, and will learn how they help employers better understand your skills and capabilities. You'll also have an opportunity to explore the online portfolios of real data ...


    agency or practice setting. Students will extend work from EDP 7998 Capstone Project I to include development of competence in conducting and reporting an investigation including evidence-based methods, analyzing data, and drawing and reporting on a conclusion. Pre-requisite: EDP 7992 Capstone Project in Applied Behavior Analysis I 40 credits total

  24. GitHub

    The project will culminate with a presentation of your data analysis report, with an executive summary for the various stakeholders in the organization. You will be assessed on both your work for the various stages in the Data Analysis process, as well as the final deliverable. This project is a great opportunity to showcase your Data Analytics ...

  25. Business Analytics Capstone

    There are 5 modules in this course. The Business Analytics Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge faced by global technology companies like Yahoo, Google, and Facebook. At the end of this Capstone, you'll be able to ask the right questions of ...

  26. CAPC Stock Earnings: Capstone Companies Reported Results for Q4 2023

    Capstone Companies reported earnings per share of -1 cent. The company reported revenue of $96,208. InvestorPlace Earnings is a project that leverages data from TradeSmith to automate coverage of ...

  27. alvarojob20/IBM-Data-Analyst-Capstone-Project

    About. It will be applied various Data Analytics skills and techniques that wehave learned in the IBM Data Analyst Professional Certificate. It will be assumed the role of an Associate Data Analyst and be presented with a business challenge that requires data analysis to be performed on real-world datasets.

  28. Learner Reviews & Feedback for IBM Data Analyst Capstone Project Course

    Find helpful learner reviews, feedback, and ratings for IBM Data Analyst Capstone Project from IBM. Read stories and highlights from Coursera learners who completed IBM Data Analyst Capstone Project and wanted to share their experience. This is a great course. I learned so much about data science. I appreciate all the help I received. ...