Completing Python projects is the ultimate way to learn the language. When you work on real-world projects, you not only retain more of the lessons you learn, but you'll also find it super motivating to push yourself to pick up new skills. Because let's face it, no one actually enjoys sitting in front of a screen learning random syntax for hours on end―particularly if it's not going to be used right away.

Python projects don't have this problem. Anything new you learn will stick because you're immediately putting it into practice. There's just one problem: many Python learners struggle to come up with their own Python project ideas to work on. But that's okay, we can help you with that!

Are You Ready for This?

If you have some programming experience, you might be ready to jump straight into building a Python project. However, if you’re just starting out, it’s vital you have a solid foundation in Python before you take on any projects. Otherwise, you run the risk of getting frustrated and giving up before you even get going. For those in need, we recommend taking either:

1. Introduction to Python Programming course: meant for those looking to become a data professional while learning the fundamentals of programming with Python.
   • This course is part of the Data Analyst in Python career path
2. Introduction to Python Programming course: meant for those looking to leverage the power of AI while learning the fundamentals of programming with Python.
   • This course is part of the Generative AI Fundamentals in Python skill path

In both courses, the goal is to quickly learn the basics of Python so you can start working on a project as soon as possible. You'll learn by doing, not by passively watching videos.

Selecting a Project

Our list below has 60+ fun and rewarding Python projects for learners at all levels. Some are free guided projects that you can complete directly in your browser via the Dataquest platform. Others are more open-ended, serving as inspiration as you build your Python skills. The key is to choose a project that resonates with you and just go for it!

Now, let’s take a look at some Python project examples. There is definitely something to get you started in this list.

Free Python Projects (Recommended):

These free Dataquest guided projects are a great place to start. They provide an embedded code editor directly in your browser, step-by-step instructions to help you complete the project, and community support if you happen to get stuck.

1. Building an Interactive Word Game — In this guided project, you’ll use basic Python programming concepts to create a functional and interactive word-guessing game.

2. Profitable App Profiles for the App Store and Google Play Markets — In this one, you’ll work as a data analyst for a company that builds mobile apps. You’ll use Python to analyze real app market data to find app profiles that attract the most users.

3. Exploring Hacker News Posts — Use Python string manipulation, OOP, and date handling to analyze trends driving post popularity on Hacker News, a popular technology site.

4. Learn and Install Jupyter Notebook — A guide to using and setting up Jupyter Notebook locally to prepare you for real-world data projects.

5. Predicting Heart Disease — We're tasked with using a dataset from the World Health Organization to accurately predict a patient’s risk of developing heart disease based on their medical data.

6. Analyzing Accuracy in Data Presentation — In this project, we'll step into the role of data journalists to analyze movie ratings data and determine if there’s evidence of bias in Fandango’s rating system.

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More Projects to Help Build Your Portfolio:

7. Finding Heavy Traffic Indicators on I-94 — Explore how using the pandas plotting functionality along with the Jupyter Notebook interface allows us to analyze data quickly using visualizations to determine indicators of heavy traffic.

8. Storytelling Data Visualization on Exchange Rates — You'll assume the role of a data analyst tasked with creating an explanatory data visualization about Euro exchange rates to inform and engage an audience.

9. Clean and Analyze Employee Exit Surveys — Work with exit surveys from employees of the Department of Education in Queensland, Australia. Play the role of a data analyst to analyze employee exit surveys and uncover insights about why employees resign.

10. Star Wars Survey — In this data cleaning project, you’ll work with Jupyter Notebook to analyze data on the Star Wars movies to answer the hotly contested question, "Who shot first?"

11. Analyzing NYC High School Data — For this project, you’ll assume the role of a data scientist analyzing relationships between SAT scores and demographic factors in NYC public schools to determine if the SAT is a fair test.

12. Predicting the Weather Using Machine Learning — For this project, you’ll step into the role of a data scientist to predict tomorrow’s weather using historical data and machine learning, developing skills in data preparation, time series analysis, and model evaluation.

13. Credit Card Customer Segmentation — For this project, we’ll play the role of a data scientist at a credit card company to segment customers into groups using K-means clustering in Python, allowing the company to tailor strategies for each segment.

Python Projects for AI Enthusiasts:

14. Building an AI Chatbot with Streamlit — Build a simple website with an AI chatbot user interface similar to the OpenAI Playground in this intermediate-level project using Streamlit.

15. Developing a Dynamic AI Chatbot — Create your very own AI-powered chatbot that can take on different personalities, keep track of conversation history, and provide coherent responses in this intermediate-level project.

16. Building a Food Ordering App — Create a functional application using Python dictionaries, loops, and functions to create an interactive system for viewing menus, modifying carts, and placing orders.

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Fun Python Projects for Building Data Skills:

17. Exploring eBay Car Sales Data — Use Python to work with a scraped dataset of used cars from eBay Kleinanzeigen, a classifieds section of the German eBay website.

18. Find out How Much Money You’ve Spent on Amazon — Dig into your own spending habits with this beginner-level tutorial!

19. Analyze Your Personal Netflix Data — Another beginner-to-intermediate tutorial that gets you working with your own personal dataset.

20. Analyze Your Personal Facebook Data with Python — Are you spending too much time posting on Facebook? The numbers don’t lie, and you can find them in this beginner-to-intermediate Python project.

21. Analyze Survey Data — This walk-through will show you how to set up Python and how to filter survey data from any dataset (or just use the sample data linked in the article).

22. All of Dataquest’s Guided Projects — These guided data science projects walk you through building real-world data projects of increasing complexity, with suggestions for how to expand each project.

23. Analyze Everything — Grab a free dataset that interests you, and start poking around! If you get stuck or aren’t sure where to start, our introduction to Python lessons are here to help, and you can try them for free!

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Cool Python Projects for Game Devs:

24. Rock, Paper, Scissors — Learn Python with a simple-but-fun game that everybody knows.

25. Build a Text Adventure Game — This is a classic Python beginner project (it also pops up in this book) that’ll teach you many basic game setup concepts that are useful for more advanced games.

26. Guessing Game — This is another beginner-level project that’ll help you learn and practice the basics.

27. Mad Libs — Use Python code to make interactive Python Mad Libs!

28. Hangman — Another childhood classic that you can make to stretch your Python skills.

29. Snake — This is a bit more complex, but it’s a classic (and surprisingly fun) game to make and play.

Simple Python Projects for Web Devs:

30. URL shortener — This free video course will show you how to build your own URL shortener like Bit.ly using Python and Django.

31. Build a Simple Web Page with Django — This is a very in-depth, from-scratch tutorial for building a website with Python and Django, complete with cartoon illustrations!

Easy Python Projects for Aspiring Developers:

32. Password generator — Build a secure password generator in Python.

33. Use Tweepy to create a Twitter bot — This Python project idea is a bit more advanced, as you’ll need to use the Twitter API, but it’s definitely fun!

34. Build an Address Book — This could start with a simple Python dictionary or become as advanced as something like this!

35. Create a Crypto App with Python — This free video course walks you through using some APIs and Python to build apps with cryptocurrency data.

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Additional Python Project Ideas

Still haven’t found a project idea that appeals to you? Here are many more, separated by experience level.

These aren’t tutorials; they’re just Python project ideas that you’ll have to dig into and research on your own, but that’s part of the fun! And it’s also part of the natural process of learning to code and working as a programmer.

The pros use Google and AI tools for answers all the time — so don’t be afraid to dive in and get your hands dirty!

Beginner Python Project Ideas

36. Create a text encryption generator. This would take text as input, replaces each letter with another letter, and outputs the “encoded” message.

37. Build a countdown calculator. Write some code that can take two dates as input, and then calculate the amount of time between them. This will be a great way to familiarize yourself with Python’s datetime module.

38. Write a sorting method. Given a list, can you write some code that sorts it alphabetically, or numerically? Yes, Python has this functionality built-in, but see if you can do it without using the sort() function!

39. Build an interactive quiz application. Which Avenger are you? Build a personality or recommendation quiz that asks users some questions, stores their answers, and then performs some kind of calculation to give the user a personalized result based on their answers

40. Tic-Tac-Toe by Text. Build a Tic-Tac-Toe game that’s playable like a text adventure. Can you make it print a text-based representation of the board after each move?

41. Make a temperature/measurement converter. Write a script that can convert Fahrenheit (℉) to Celcius (℃) and back, or inches to centimeters and back, etc. How far can you take it?

42. Build a counter app. Take your first steps into the world of UI by building a very simple app that counts up by one each time a user clicks a button.

43. Build a number-guessing game. Think of this as a bit like a text adventure, but with numbers. How far can you take it?

44. Build an alarm clock. This is borderline beginner/intermediate, but it’s worth trying to build an alarm clock for yourself. Can you create different alarms? A snooze function?

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Intermediate Python Project Ideas

45. Build an upgraded text encryption generator. Starting with the project mentioned in the beginner section, see what you can do to make it more sophisticated. Can you make it generate different kinds of codes? Can you create a “decoder” app that reads encoded messages if the user inputs a secret key? Can you create a more sophisticated code that goes beyond simple letter-replacement?

46. Make your Tic-Tac-Toe game clickable. Building off the beginner project, now make a version of Tic-Tac-Toe that has an actual UI  you’ll use by clicking on open squares. Challenge: can you write a simple “AI” opponent for a human player to play against?

47. Scrape some data to analyze. This could really be anything, from any website you like. The web is full of interesting data. If you learn a little about web-scraping, you can collect some really unique datasets.

48. Build a clock website. How close can you get it to real-time? Can you implement different time zone selectors, and add in the “countdown calculator” functionality to calculate lengths of time?

49. Automate some of your job. This will vary, but many jobs have some kind of repetitive process that you can automate! This intermediate project could even lead to a promotion.

50. Automate your personal habits. Do you want to remember to stand up once every hour during work? How about writing some code that generates unique workout plans based on your goals and preferences? There are a variety of simple apps you can build to automate or enhance different aspects of your life.

51. Create a simple web browser. Build a simple UI that accepts  URLs and loads webpages. PyWt will be helpful here! Can you add a “back” button, bookmarks, and other cool features?

52. Write a notes app. Create an app that helps people write and store notes. Can you think of some interesting and unique features to add?

53. Build a typing tester. This should show the user some text, and then challenge them to type it quickly and accurately. Meanwhile, you time them and score them on accuracy.

54. Create a “site updated” notification system. Ever get annoyed when you have to refresh a website to see if an out-of-stock product has been relisted? Or to see if any news has been posted? Write a Python script that automatically checks a given URL for updates and informs you when it identifies one. Be careful not to overload the servers of whatever site you’re checking, though. Keep the time interval reasonable between each check.

55. Recreate your favorite board game in Python. There are tons of options here, from something simple like Checkers all the way up to Risk. Or even more modern and advanced games like Ticket to Ride or Settlers of Catan. How close can you get to the real thing?

56. Build a Wikipedia explorer. Build an app that displays a random Wikipedia page. The challenge here is in the details: can you add user-selected categories? Can you try a different “rabbit hole” version of the app, wherein each article is randomly selected from the articles linked in the previous article? This might seem simple, but it can actually require some serious web-scraping skills.

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Advanced Python Project Ideas

57. Build a stock market prediction app. For this one, you’ll need a source of stock market data and some machine learning and data analytics skills. Fortunately, many people have tried this, so there’s plenty of source code out there to work from.

58. Build a chatbot. The challenge here isn’t so much making the chatbot as it is making it good. Can you, for example, implement some natural language processing techniques to make it sound more natural and spontaneous?

59. Program a robot. This requires some hardware (which isn’t usually free), but there are many affordable options out there — and many learning resources, too. Definitely look into Raspberry Pi if you’re not already thinking along those lines.

60. Build an image recognition app. Starting with handwriting recognition is a good idea — Dataquest has a guided data science project to help with that! Once you’ve learned it, you can take it to the next level.

61. Create a sentiment analysis tool for social media. Collect data from various social media platforms, preprocess it, and then train a deep learning model to analyze the sentiment of each post (positive, negative, neutral).

62. Make a price prediction model. Select an industry or product that interests you, and build a machine learning model that predicts price changes.

63. Create an interactive map. This will require a mix of data skills and UI creation skills. Your map can display whatever you’d like — bird migrations, traffic data, crime reports — but it should be interactive in some way. How far can you take it?

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Next Steps

Each of the examples in the previous sections built on the idea of choosing a great Python project for a beginner and then enhancing it as your Python skills progress. Next, you can advance to the following:

• Think about what interests you, and choose a project that overlaps with your interests.

• Think about your Python learning goals, and make sure your project moves you closer to achieving those goals.

• Start small. Once you’ve built a small project, you can either expand it or build another one.

Now you’re ready to get started. If you haven’t learned the basics of Python yet, I recommend diving in with Dataquest’s Introduction to Python Programming course.

If you already know the basics, there’s no reason to hesitate! Now is the time to get in there and find your perfect Python project.

Data is everywhere. Every click, purchase, or social media like creates mountains of information, but raw numbers do not tell a story. That is where data analysts come in. They turn messy datasets into actionable insights that help businesses grow.

Whether you're looking to become a junior data analyst or looking to level up, here are the top 11 data analyst skills every professional needs in 2025, including one optional skill that can help you stand out.

1. SQL

SQL (Structured Query Language) is the language of databases and is arguably the most important technical skill for analysts. It allows you to efficiently query and manage large datasets across multiple systems—something Excel cannot do at scale.

Example in action: Want last quarter's sales by region? SQL pulls it in seconds, no matter how huge the dataset.

Learning Tip: Start with basic queries, then explore joins, aggregations, and subqueries. Practicing data analytics exercises with SQL will help you build confidence and precision.

2. Excel

Since it’s not going anywhere, it’s still worth it to learn Microsoft Excel. Beyond spreadsheets, it offers pivot tables, macros, and Power Query, which are perfect for quick analysis on smaller datasets. Many startups or lean teams still rely on Excel as their first database.

Example in action: Summarize thousands of rows of customer feedback in minutes with pivot tables, then highlight trends visually.

Learning Tip: Focus on pivot tables, logical formulas, and basic automation. Once comfortable, try linking Excel to SQL queries or automating repetitive tasks to strengthen your technical skills in data analytics.

3. Python or R

Python and R are essential for handling big datasets, advanced analytics, and automation. Python is versatile for cleaning data, automation, and integrating analyses into workflows, while R excels at exploratory data analysis and statistical analysis.

Example in action: Clean hundreds of thousands of rows with Python’s pandas library in seconds, something that would take hours in Excel.

Learning Tip: Start with data cleaning and visualization, then move to complex analyses like regression or predictive modeling. Building these data analyst skills is critical for anyone working in data science. Of course, which is better to learn is still up for debate.

4. Data Visualization

Numbers alone rarely persuade anyone. Data visualization is how you make your insights clear and memorable. Tools like Tableau, Power BI, or Python/R libraries help you tell a story that anyone can understand.

Example in action: A simple line chart showing revenue trends can be far more persuasive than a table of numbers.

Learning Tip: Design visuals with your audience in mind. Recreate dashboards from online tutorials to practice clarity, storytelling, and your soft skills in communicating data analytics results.

5. Statistics & Analytics

Strong statistical analysis knowledge separates analysts who report numbers from those who generate insights. Skills like regression, correlation, hypothesis testing, and A/B testing help you interpret trends accurately.

Example in action: Before recommending a new marketing campaign, test whether the increase in sales is statistically significant or just random fluctuation.

Learning Tip: Focus on core probability and statistics concepts first, then practice applying them in projects. Our Probability and Statistics with Python skill path is a great way to learn theoretical concepts in a hands-on way.

6. Data Cleaning & Wrangling

Data rarely comes perfect, so data cleaning skills will always be in demand. Cleaning and transforming datasets, removing duplicates, handling missing values, and standardizing formats are often the most time-consuming but essential parts of the job.

Example in action: You want to analyze customer reviews, but ratings are inconsistent and some entries are blank. Cleaning the data ensures your insights are accurate and actionable.

Learning Tip: Practice on free datasets or public data repositories to build real-world data analyst skills.

7. Communication & Presentation Skills

Analyzing data is only half the battle. Sharing your findings clearly is just as important. Being able to present insights in reports, dashboards, or meetings ensures your work drives decisions.

Example in action: Presenting a dashboard to a marketing team that highlights which campaigns brought the most new customers can influence next-quarter strategy.

Learning Tip: Practice explaining complex findings to someone without a technical background. Focus on clarity, storytelling, and visuals rather than technical jargon. Strong soft skills are just as valuable as your technical skills in data analytics.

8. Dashboard & Report Creation

Beyond visualizations, analysts need to build dashboards and reports that allow stakeholders to interact with data. A dashboard is not just a fancy chart. It is a tool that empowers teams to make data-driven decisions without waiting for you to interpret every number.

Example in action: A sales dashboard with filters for region, product line, and time period can help managers quickly identify areas for improvement.

Learning Tip: Start with simple dashboards in Tableau, Power BI, or Google Data Studio. Focus on making them interactive, easy to understand, and aligned with business goals. This is an essential part of professional data analytics skills.

9. Domain Knowledge

Understanding the industry or context of your data makes you exponentially more effective. Metrics and trends mean different things depending on the business.

Example in action: Knowing e-commerce metrics like cart abandonment versus subscription churn metrics can change how you interpret the same type of data.

Learning Tip: Study your company’s industry, read case studies, or shadow colleagues in different departments to build context. The more you know, the better your insights and analysis will be.

10. Critical Thinking & Problem-Solving

Numbers can be misleading. Critical thinking lets analysts ask the right questions, identify anomalies, and uncover hidden insights.

Example in action: Revenue drops in one region. Critical thinking helps you ask whether it is seasonal, a data error, or a genuine trend.

Learning Tip: Challenge assumptions and always ask “why” multiple times when analyzing a dataset. Practice with open-ended case studies to sharpen your analytical thinking and overall data analyst skills.

11. Machine Learning Basics

Not every analyst uses machine learning daily, but knowing the basics—predictive modeling, clustering, or AI-powered insights—can help you stand out. You do not need this skill to get started as an analyst, but familiarity with it is increasingly valuable for advanced roles.

Example in action: Using a simple predictive model to forecast next month’s sales trends can help your team allocate resources more effectively.

Learning Tip: Start small with beginner-friendly tools like Python’s scikit-learn library, then explore more advanced models as you grow. Treat it as an optional skill to explore once you are confident in SQL, Python/R, and statistical analysis.

Where to Learn These Skills

Want to become a data analyst? Dataquest makes it easy to learn the skills you need to get hired.

With our Data Analyst in Python and Data Analyst in R career paths, you’ll learn by doing real projects, not just watching videos. Each course helps you build the technical and practical skills employers look for.

By the end, you’ll have the knowledge, experience, and confidence to start your career in data analysis.

Wrapping It Up

Being a data analyst is not just about crunching numbers. It is about turning data into actionable insights that drive decisions. Master these data analytics and data analyst skills, and you will be prepared to handle the challenges of 2025 and beyond.