Leonard Apeltsin - Data Science Bookcamp: Five Python projects

Core algorithms inside the book

A trained logistic regression classifier distinguishes between two classes of points by slicing like a cleaver through 3D space (see section 21).

Five real-world Python projects

Leonard Apeltsin

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ISBN: 9781617296253

To my teacher, Alexander Vishnevsky, who taught me how to think

Another promising candidate had failed their data science interview, and I began to wonder why. The year was 2018, and I was struggling to expand the data science team at my startup. I had interviewed dozens of seemingly qualified candidates, only to reject them all. The latest rejected applicant was an economics PhD from a top-notch school. Recently, the applicant had transitioned into data science after completing a 10-week bootcamp. I asked the applicant to discuss an analytics problem that was very relevant to our company. They immediately brought up a trendy algorithm that was not applicable to the situation. When I tried to debate the algorithms incompatibilities, the candidate was at a loss. They didnt know how the algorithm actually worked or the appropriate circumstances under which to use it. These details hadnt been taught to them at the bootcamp.

After the rejected candidate departed, I began to reflect on my own data science education. How different it had been! Back in 2006, data science was not yet a coveted career choice, and DS bootcamps did not yet exist. In those days, I was a poor grad student struggling to pay the rent in pricey San Francisco. My graduate research required me to analyze millions of genetic links to diseases. I realized that my skills were transferable to other areas of analysis, and thus my data science consultancy was born.

Unbeknownst to my graduate advisor, I began to solicit analytics work from random Bay Area companies. That freelance work helped pay the bills, so I could not be too choosy about the data-driven assignments I tackled. Thus, I would sign up for a variety of data science tasks, ranging from simple statistical analyses to complex predictive modeling. Sometimes I would find myself overwhelmed by a seemingly intractable data problem, but in the end, Id persevere. My struggles taught me the nuances of diverse analytics techniques and how to best combine them to reach elegant solutions. More importantly, I learned how common techniques can fail and how to surmount these failure points to deliver impactful results. As my skill set grew, my data science career began to flourish. Eventually, I became a leader in the field.

Would I have achieved the same level of success through rote memorization at a 10-week bootcamp? Probably not. Many bootcamps prioritize the study of standalone algorithms over more cohesive problem-solving skills. Furthermore, the hype over an algorithms strengths tends to be emphasized over its weaknesses. Consequently, students are sometimes ill prepared to handle data science in real-world settings. That insight inspired me to write this book.