Essential Math for Data Science

Chapter 8. Career Advice and the Path Forward

As we come to a close with this book, it is a good idea to evaluate where to go from here. You learned and integrated a wide survey of applied mathematical topics: calculus, probability, statistics, and linear algebra. Then you applied these techniques to practical applications, including linear regression, logistic regression, and neural networks. In this chapter, we will cover how to use this knowledge going forward while navigating the strange, exciting, and oddly diverse landscape of a data science career. I will emphasize the importance of having direction and a tangible objective to work toward, rather than memorizing tools and techniques without an actual problem in mind.

Since we’re moving away from foundational concepts and applied methods, this chapter will have a different tone than the rest of the book. You might be expecting to learn how you can apply these mathematical modeling skills to your career in focused and tangible ways. However, if you want to be successful in a data science career, you will have to learn a few more hard skills like SQL and programming, as well as soft skills to develop professional awareness. The latter are especially important so you do not become lost in the shape-shifting profession that is data science and unseen market forces blindside you.

I am not going to presume to know your career goals or what you hope to achieve with this information. I will make a few safe bets, though, since you ...