Every algorithm is editorial.
— Emily Bell (director of the Tow Center for Digital Journalism at Columbia’s Graduate School of Journalism)
We invited the students who took Introduction to Data Science version 1.0 to contribute a chapter to the book. They chose to use their chapter to reflect on the course and describe how they experienced it. Contributors to this chapter are Alexandra Boghosian, Jed Dougherty, Eurry Kim, Albert Lee, Adam Obeng, and Kaz Sakamoto.
When you’re learning data science, you can’t start anywhere except the cutting edge.
An introductory physics class will typically cover mechanics, electricity, and magnetism, and maybe move on to some more “modern” subjects like special relativity, presented broadly in order of ascending difficulty. But this presentation of accumulated and compounded ideas in an aggregated progression doesn’t give any insight into, say, how Newton actually came up with a differential calculus. We are not taught about his process; how he got there. We don’t learn about his tools or his thoughts. We don’t learn which books he read or whether he took notes. Did he try to reproduce other people’s proofs? Did he focus on problems that followed from previous writing? What exactly made him think, “I just can’t do this without infinitesimals?” Did Newton need scratch paper? Or did the ideas emerge somehow fully formed when he saw an apple drop? These things aren’t taught, but they have to be learned; this ...