Data Science from Scratch 2e

To truly learn Data Science, you should not only master the tools such as data science libraries, frameworks, modules, and toolkits but also understand the underlying ideas and principles. This updated second edition of "Data Science from Scratch," revised for Python 3.6, shows you how these tools and algorithms work by implementing them from the ground up.

If you have a knack for mathematics and some programming knowledge, author Joel Grus will help you become familiar with the mathematics and statistics at the core of Data Science, as well as the hacking skills you need to get started as a Data Scientist. This updated book is packed with new material on deep learning, statistics, and natural language processing, showing you how to find valuable insights in today's overwhelming data deluge.

• Get a crash course in Python
• Learn the basics of linear algebra, statistics, and probability, and their application in Data Science
• Collect, explore, clean, process, 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 recommendation systems, natural language processing, network analysis, MapReduce, and databases.