One of the best things about data science is that you can always connect it to your other interests. Take professional sports. Data helps the pros strategize, assess players’ performance, and even predict the outcome of games.

In an interview with Forbes, former Philadelphia Eagles Game Management Coach Ryan Paganetti says that analytics play an increasingly important role in the NFL. So if you’re a sports fan and you’re interested in data science, you can learn a lot about a team by playing with their data — and we’ll show you how in our new case study: Analyze NFL Stats with Python.

Who is the new case study right for?

Have you been looking for a way to brush up on your analytical skills? Or maybe you just want to learn more about NFL stats? In either case, this course is a great pick.

“This case study is for NFL fans who want to start exploring sports data, as well as more seasoned programmers looking for some interesting practice,” says Codecademy Senior Curriculum Developer Andrea Hassler. “Whether it’s for a fantasy league, a future career-switch, or just more data analysis practice, this case study is for you! We provide plenty of hints and a cheatsheet to support beginners.”

And if you’re looking for a job, it can also help you add to your portfolio. “Case studies are a perfect way to build a data science portfolio and demonstrate to potential employers that you have the skills they’re looking for,” Andrea says. “If you’re an NFL fan, this is also an opportunity to add your own personality to your portfolio. If you’re not an NFL fan, this is a great way to demonstrate that you can work with a wide variety of data.”

But before you jump into the case study, you’ll need to be familiar with Python and pandas — which you can learn in our Analyze Data with Python and Data Science Foundations skill paths.

What will you learn from the new case study?

In this case study, we’ll show you how to use Python and Jupyter Notebook to analyze real NFL data about teams and performance. You’ll learn how to pull insights from sports data and build a machine learning model that uses key statistics to predict which team will win.

Ready to get started? Check out Analyze NFL Stats with Python.

And stay tuned for more case studies as we keep adding more opportunities to give you real-world practice. “We made this case study hoping people’s passion for football would be enhanced by having fun analyzing stats and making predictions,” Andrea says. “You don’t have to be a ‘numbers person’ to enjoy data science. There are data available to answer questions and explore all kinds of topics you’re already interested in!”