Resources for Further Learning

You made it! What's next?

Congrats on finishing Mode's Python Tutorial!

Please help us make this better for the next set of aspiring analysts and data scientists by sending us your feedback.

By this point you've probably realized that this tutorial is just the tip of the iceberg. You can do a ton with Python—so much that it's not practical for us to continue in a generalized way. Rather, it's up to you to decide which skills you'd like to develop and seek out additional resources on your own.

If you're interested in exploratory analysis

If you're focused on exploratory data analysis—uncovering hidden insights in your data—you may want to spend a bit more time on the fundamentals. We've developed a couple more resources to help you solve one-off analytical problems effectively:

• Learn about basic Python libraries. We've created a small wiki to help you get accquainted with the capabilities of some of Python's popular libraries. Knowing when to use one library versus another is key to becoming an efficient problem solver in Python.
• Brush up on your SQL skills. If your SQL's a little rusty, check out Mode's SQL Tutorial. Clever SQL can help you join and filter datasets before you import them to Python to make your workflow more straightforward. Because of memory constraints, paring down the data with SQL is often mandatory when you're working with large datasets. It's a must-know tool for any analyst.
• Sharpen your analytical thinking. There's one specific section of the SQL Tutorial focused on analytical problem solving. It's a series of open-ended problems that require exploraiton similar to what you'd find on the job. The answers are shown in SQL, but you could easily do them using Python instead.

If you're interested in something more specialized

If you're looking to do something more specialized like machine learning or language processing, you'll want to seek out more specific instruction. These skillsets are primarily theoretical—most of your time will be spent learning what models exist, when to apply them, and how to interpret their results.

For guidance on more specialized pursuits, we recommend consulting the Open Source Data Science Masters curriculum. It's a curated list of books and online courses, sorted by discipline. It's thorough and well laid out, which makes it a good place to start if you don't even know what specializations exist. As its name implies, the amount of content organized here is equivalent to (or better than) an actual graduate degree.

Whatever you end up doing next, best of luck (and keep in touch)!