Data Science from Scratch: First Principles with Python

To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but 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 today’s messy glut of data.

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

• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
• Practical Statistics for Data Scientists: 50 Essential Concepts
• 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
• Data Science (MIT Press Essential Knowledge series)
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
• Python Data Science Handbook: Essential Tools for Working with Data
• Deep Learning from Scratch: Building with Python from First Principles
• Practical Time Series Analysis: Prediction with Statistics and Machine Learning