Data Science from Scratch: First Principles with Python

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

• Python Data Science Handbook: Essential Tools for Working with Data
• Practical Statistics for Data Scientists: 50 Essential Concepts
• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
• Introduction to Machine Learning with Python: A Guide for Data Scientists
• Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
• Doing Data Science: Straight Talk from the Frontline
• Data Science (MIT Press Essential Knowledge series)
• Deep Learning with Python