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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science 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 hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

  • Get a crash course in Python
  • Learn the basics of linear algebra, statistics, and probability—and understand 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, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases


From the Publisher

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Data Science for Business

Data Science from Scratch

Doing Data Science

R for Data Science

Data Science at the Command Line

Python Data Science Handbook

Business Data Science from Scratch Doing Data Science R for Data Science Data Science at the Command Line Python Data Science Handbook What You Need to Know about Data Mining and Data-Analytic Thinking

First Principles with Python

Straight Talk from the Frontline

Visualize, Model, Transform, Tidy, and Import Data

Facing the Future with Time-Tested Tools

Tools and Techniques for Developers

Pages

414

330

408

250

212

500

Publisher‏:‎O′Reilly; 1st edition (1 May 2015)
Language‏:‎English
Paperback‏:‎330 pages
ISBN-10‏:‎149190142X
ISBN-13‏:‎978-1491901427
Item Weight‏:‎600 g
Dimensions‏:‎17.8 x 1.75 x 23.3 cm

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