Data Science from Scratch: First Principles with Python

To really learn data science, you should not only master the toolsdata science libraries, frameworks, modules, and toolkitsbut 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 todays messy glut of data. Get a crash course in Python Learn the basics of linear algebra, statistics, and probabilityand how and when theyre 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, Nave Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases. .