Data science from scratch: first principles with Python

Table of Content

Introduction
A crash course in Python
Visualizing data
Linear algebra
Statistics
Probability
Hypothesis and inference
Gradient descent
Getting data
Working with data
Machine learning
k-Nearest neighbors
Naive bayes
Simple linear regression
Multiple regression
Logistic regression
Decision trees
Neural networks
Clustering
Natural language processing
Network analysis
Recommender systems
Databases and SQL
MapReduce
Go forth and do data science.

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 PythonLearn the basics of linear algebra, statistics, and probability--and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases.