I just wanted suggest some readings that I personally found super helpful for my interviews and wanted to share with the NYCDSA.
1. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
John D. Kelleher, Brian Mac Namee, Aoife D’arcy ; 1st Edition
2. Machine Learning: A Probabilistic Perspective
Kevin P. Murphy, 1st Edition
The second book, Machine Learning: A probabilistic Perspective is a very technical read but gives good technical questions for in depth statistical learning. This may be more suited to those with an advanced degree in mathematics.
However, the first book, the Fundamentals of Machine Learning for Predictive Data Analytics is by far my favorite supplementary read. It is semi-technical (not as technical as the Machine learning: A Probabilistic Perspective I should say), fairly easy to read, and goes over the higher level thinking for data science methods in business applications. It goes over the CRISP-DM approach and gives examples on how to implement it as well for different situations. For myself it helped consolidate everything that I learned in the bootcamp and helped me develop a big picture understanding and approach to my data science methodologies. Furthermore, this book definitely helped me with phone and non-programming interviews that I had most of my data scientist interviews. In fact, most of my data science interviews involved questions that focused on my approach and data understanding rather than just pure programming questions and this book helped me prepare for such questions. Unfortunately, I don’t believe that there is a pdf version online, but there will likely be one soon as this book is gaining popularity.
Anyways, I just wanted to share these two great resources with the program!