To really learn data science you should not only master the tools—data science libraries
frameworks modules and toolkits—but also understand the ideas and principles underlying them.
Updated for Python 3.6 this second edition of Data Science from Scratch shows you how these
tools and algorithms work by implementing them from scratch. If you have an aptitude for
mathematics and some programming skills author Joel Grus will help you get comfortable with
the math and statistics at the core of data science and with the hacking skills you need to
get started as a data scientist. Packed with new material on deep learning statistics and
natural language processing this updated book shows you how to find the gems in today's messy
glut of data. Get a crash course in Python Learn the basics of linear algebra statistics and
probability—and how and when they're used in data science Collect explore clean munge and
manipulate data Dive into the fundamentals of machine learning Implement models such as
k-nearest neighbors Naïve Bayes linear and logistic regression decision trees neural
networks and clustering Explore recommender systems natural language processing network
analysis MapReduce and databases
