Data Science from Scratch: First Principles with Python, Second Edition

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. . Cover Copyright Table of Contents Preface to the Second Edition Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments Preface to the First Edition Data Science From Scratch Chapter 1. Introduction The Ascendance of Data What Is Data Science? Motivating Hypothetical: DataSciencester Finding Key Connectors Data Scientists You May Know Salaries and Experience Paid Accounts Topics of Interest Onward Chapter 2. A Crash Course in Python The Zen of Python Getting Python Virtual Environments Whitespace Formatting Modules Functions Strings Exceptions Lists Tuples Dictionaries defaultdict Counters Sets Control Flow Truthiness Sorting List Comprehensions Automated Testing and assert Object-Oriented Programming Iterables and Generators Randomness Regular Expressions Functional Programming zip and Argument Unpacking args and kwargs Type Annotations How to Write Type Annotations Welcome to DataSciencester! For Further Exploration Chapter 3. Visualizing Data matplotlib Bar Charts Line Charts Scatterplots For Further Exploration Chapter 4. Linear Algebra Vectors Matrices For Further Exploration Chapter 5. Statistics Describing a Single Set of Data Central Tendencies Dispersion Correlation Simpson’s Paradox Some Other Correlational Caveats Correlation and Causation For Further Exploration Chapter 6. Probability Dependence and Independence Conditional Probability Bayes’s Theorem Random Variables Continuous Distributions The Normal Distribution The Central Limit Theorem For Further Exploration Chapter 7. Hypothesis and Inference Statistical Hypothesis Testing Example: Flipping a Coin p-Values Confidence Intervals p-Hacking Example: Running an A/B Test Bayesian Inference For Further Exploration Chapter 8. Gradient Descent The Idea Behind Gradient Descent Estimating the Gradient Using the Gradient Choosing the Right Step Size Using Gradient Descent to Fit Models Minibatch and Stochastic Gradient Descent For Further Exploration Chapter 9. Getting Data stdin and stdout Reading Files The Basics of Text Files Delimited Files Scraping the Web HTML and the Parsing Thereof Example: Keeping Tabs on Congress Using APIs JSON and XML Using an Unauthenticated API Finding APIs Example: Using the Twitter APIs Getting Credentials For Further Exploration Chapter 10. Working with Data Exploring Your Data Exploring One-Dimensional Data Two Dimensions Many Dimensions Using NamedTuples Dataclasses Cleaning and Munging Manipulating Data Rescaling An Aside: tqdm Dimensionality Reduction For Further Exploration Chapter 11. Machine Learning Modeling What Is Machine Learning? Overfitting and Underfitting Correctness The Bias-Variance Tradeoff Feature Extraction and Selection For Further Exploration Chapter 12. k-Nearest Neighbors The Model Example: The Iris Dataset The Curse of Dimensionality For Further Exploration Chapter 13. Naive Bayes A Really Dumb Spam Filter A More Sophisticated Spam Filter Implementation Testing Our Model Using Our Model For Further Exploration Chapter 14. Simple Linear Regression The Model Using Gradient Descent Maximum Likelihood Estimation For Further Exploration Chapter 15. Multiple Regression The Model Further Assumptions of the Least Squares Model Fitting the Model Interpreting the Model Goodness of Fit Digression: The Bootstrap Standard Errors of Regression Coefficients Regularization For Further Exploration Chapter 16. Logistic Regression The Problem The Logistic Function Applying the Model Goodness of Fit Support Vector Machines For Further Investigation Chapter 17. Decision Trees What Is a Decision Tree? Entropy The Entropy of a Partition Creating a Decision Tree Putting It All Together Random Forests For Further Exploration Chapter 18. Neural Networks Perceptrons Feed-Forward Neural Networks Backpropagation Example: Fizz Buzz For Further Exploration Chapter 19. Deep Learning The Tensor The Layer Abstraction The Linear Layer Neural Networks as a Sequence of Layers Loss and Optimization Example: XOR Revisited Other Activation Functions Example: FizzBuzz Revisited Softmaxes and Cross-Entropy Dropout Example: MNIST Saving and Loading Models For Further Exploration Chapter 20. Clustering The Idea The Model Example: Meetups Choosing k Example: Clustering Colors Bottom-Up Hierarchical Clustering For Further Exploration Chapter 21. Natural Language Processing Word Clouds n-Gram Language Models Grammars An Aside: Gibbs Sampling Topic Modeling Word Vectors Recurrent Neural Networks Example: Using a Character-Level RNN For Further Exploration Chapter 22. Network Analysis Betweenness Centrality Eigenvector Centrality Matrix Multiplication Centrality Directed Graphs and PageRank For Further Exploration Chapter 23. Recommender Systems Manual Curation Recommending What’s Popular User-Based Collaborative Filtering Item-Based Collaborative Filtering Matrix Factorization For Further Exploration Chapter 24. Databases and SQL CREATE TABLE and INSERT UPDATE DELETE SELECT GROUP BY ORDER BY JOIN Subqueries Indexes Query Optimization NoSQL For Further Exploration Chapter 25. MapReduce Example: Word Count Why MapReduce? MapReduce More Generally Example: Analyzing Status Updates Example: Matrix Multiplication An Aside: Combiners For Further Exploration Chapter 26. Data Ethics What Is Data Ethics? No, Really, What Is Data Ethics? Should I Care About Data Ethics? Building Bad Data Products Trading Off Accuracy and Fairness Collaboration Interpretability Recommendations Biased Data Data Protection In Summary For Further Exploration Chapter 27. Go Forth and Do Data Science IPython Mathematics Not from Scratch NumPy pandas scikit-learn Visualization R Deep Learning Find Data Do Data Science Hacker News Fire Trucks T-Shirts Tweets on a Globe And You? Index About the Author Colophon 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. -- Provided by publisher 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. With this updated second edition, 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.