Doing Data Science

Chapter 16. Next-Generation Data Scientists, Hubris, and Ethics

We want to wrap up by thinking about what just happened and what we hope for you going forward.

What Just Happened?

The two main goals of this book were to communicate what it’s like to be a data scientist and to teach you how to do some of what a data scientist does.

We’d like to think we accomplished both of these goals.

The various contributors for the chapters brought multiple first-hand accounts of what it’s like to be a data scientist, which addressed the first goal. As for the second goal, we are proud of the breadth of topics we’ve covered, even if we haven’t been able to be as thorough as we’d like to be.

It’s possible one could do better than we have, so the next sidebar gives you something to think about.

Thought Experiment: Teaching Data Science

How would you design a data science textbook?

It’s not a well-defined body of knowledge, and there’s no canonical corpus. It’s popularized and celebrated in the press and media, but there’s no “authority” to push back on erroneous or outrageous accounts. There’s a lot of overlap with various other subjects as well; it could become redundant with a machine learning textbook, for example.

How does one measure the success and impact of a data science text? What’s the counterfactual?

Can we set this up as a data science problem? Even better, can we use a causal modeling approach? This would require finding people who were more or less like you (dear reader) but didn’t ...