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On the Shelf: Data Science Books

Colorful BooksOne of my favorite things about data nerds is that they’re always eager to learn. (I certainly know the feeling!) No matter how much education they have, no matter how many technical skills they possess, they always want more.

One of the best ways to go about gaining this knowledge is to read, read, read. Read the work of those who came before you, read to stay abreast of current technology, read anything you can get your hands on.

Want to know more about the business, sociology, or nitty—gritty of data science? Here are some great books on the discipline to get you on your way:

About Data Science

Data Science for Businesses

Data Science in Popular Culture

Data Visualization

How To’s & Manuals

The Sociology of Data Science

Have you read any books that you think should be included on this list? Are there areas of the data science field that you think haven’t received enough attention in the popular press? Let us know in the comments!

Tell Me More

  • Matthew Russell

    This is a terrific list, and I especially appreciate the categorization of the books. There are several new ones from 2013 that I need to check out soon. (Thanks!)

    A book (I wrote) that might add some value to the list is Mining the Social Web. You can check out the Amazon page to get a better idea about the book’s scope and the overall response, which has been fairly positive so far:

    MTSW would probably fit into your “how-to” section and is designed to help the reader get familiar with basic data mining tasks. It’s Python centric (more specifically, it’s IPython Notebook centric, which you might appreciate given Berkeley’s involvement in leading that initiative), and all of the source code is available on GitHub, packaged as a turn-key virtual machine, so that it’s maximally accessible to all.

    • Renan Jacomassi

      Wow! First, thank you so much for the list, Jenna Dutcher!

      And, wow! Matthew Russel, Mining the Social Web was already in my list!

      This book, Mining the Social Web, is a MUST item to this list!

  • Vincent

    Greate list, tnx!
    What I’m missing in the list are some machine learning books.
    Especially for beginning data scientists, I would recommend to get a decent foundation in machine learning and pattern recognition.
    My top-4 books:
    1. Pattern Classification – Richard O. Duda
    2. Machine Learning – Tom M. Mitchell
    3. Pattern Recognition and Machine Learning – Christopher Bishop
    4. Machine Learning: A Probabilistic Perspective – Kevin P. Murphy

    A review of these books regarding their level of detail and mathematics is available on