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The data science of learning

Cat Zhou on the TDS podcast

Background image by Wes Hicks

Editor’s note: The Towards Data Science podcast’s “Climbing the Data Science Ladder” series is hosted by Jeremie Harris. Jeremie helps run a data science mentorship startup called SharpestMinds. You can listen to the podcast below:

If you’re interested in upping your coding game, or your data science game in general, then it’s worth taking some time to understand the process of learning itself.

And if there’s one company that’s studied the learning process more than almost anyone else, it’s Codecademy. With over 65 million users, Codecademy has developed a deep understanding of what it takes to get people to learn how to code, which is why I wanted to speak to their Head of Data Science, Cat Zhou, for this episode of the podcast.

Here were some of my favourite take-homes:

• There’s a lot of value in cultivating teams with different educational backgrounds. CS majors, economists, business people and die-hard Bayesians all notice different kinds of opportunities in data, and learning how to get these teams to work together is key to managing a data science effort.
• People who binge content, and code a whole bunch during a short period of time don’t tend to maintain their coding habit in the long run, according to Codecademy’s data. So spurts of coding activity probably aren’t the best way to go, because they have the same effect as cramming for a test. The key is to find the “sweet spot” of sustainable engagement you need to ensure that coding becomes a long-lasting habit.
• As data science is being taken more and more seriously, data teams are integrating more closely with product teams, which have come to rely on them to help guide the development of new features. As a result, data scientists need to develop good product instincts to be able to communicate with the product managers, designers and developers who depend on them to get a complete picture of user behavior.

You can also follow Cat on Twitter here to keep up with her work, and me here.