Reading List For A Data Scientist Interview

Reading List For A Data Scientist Interview

Posted by Joseph Lee

Updated: Feb 28, 2016


Data Scientist Reading List

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 data science 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!

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Joseph Lee

A recent graduate from Northwestern University with a B.S. in Biomedical Engineering and a Minor in computer science, Joseph has a strong background in computer engineering and programming concepts. His previous work and academic studies contains a panoply of topics including micro-electronics, cardiovascular instrumentation, machine learning, artificial intelligence, computer systems, biomedical signal processing, and programming languages. A polyglot enthusiast, Joseph is well versed and in multiple programming languages and computing environments including C++, C#, Java, Python, R, Lisp, and Matlab. As an aspiring data scientist, he has built a strong foundation in statistical learning and data manipulation methodologies in R and Python.

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