I feel like many books recommended for aspiring data scientists go two ways. The first kind of guide for data scientists is basically a math text book. The second kind is based around pandas, scikit-learn, and mySQL, but users never leave Jupyter notebooks.
This book will help you put all your hard work online and show it off to potential employers.
I am including some of my most useful code from across my projects.
A lot of this code might look like a tutorial you could find elsewhere. But these guides will be better. I did not work on any of the projects I use. I do not have a long history of I.T. deployments. These guides show how i went from zero-knowledge to deployment of the tool.
I wanted to show off my first data science project at Lambda School, but, like everything with programming, I struggled to find a guide for what I what I wanted to. Or what I should do. Or what I could do. The project was an analysis of how the chess openings. How low-rated players differ from high-rated players and the strength of openings compared to the frequency they are played.
I am writing this book because I want to share the tricks I have learned over the last 7 months of coding full time time.
Getting hired is all about unique projects. Don't believe read/listen to the links below.
My two cents as someone who interviews tons of data scientists is that most portfolio projects are way too easy, and amount to getting generally clean data, then just calling some API from sklearn or tensorflow.
I'd like to see either more non trivial software/coding skills in getting the data and setting up a good data infrastructure or more depth on a innovative science solution.
I have been able to simplify some concepts I spent many hours working on into repeatable patterns and deployed them across a number of platforms.
This book will pay for itself many times over in the amount of time you save.