Data Science From Scratch · First Principles With Python by Grus, Joel -- Read -- Imperial Library of Trantor

Index

Preface

Data Science From Scratch Conventions Used in This Book Using Code Examples Safari® Books Online How to Contact Us Acknowledgments

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

2. A Crash Course in Python

The Basics

Getting Python The Zen of Python Whitespace Formatting Modules Arithmetic Functions Strings Exceptions Lists Tuples Dictionaries

defaultdict Counter

Sets Control Flow Truthiness

The Not-So-Basics

Sorting List Comprehensions Generators and Iterators Randomness Regular Expressions Object-Oriented Programming Functional Tools enumerate zip and Argument Unpacking args and kwargs Welcome to DataSciencester!

For Further Exploration

3. Visualizing Data

matplotlib Bar Charts Line Charts Scatterplots For Further Exploration

4. Linear Algebra

Vectors Matrices For Further Exploration

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

6. Probability

Dependence and Independence Conditional Probability Bayes’s Theorem Random Variables Continuous Distributions The Normal Distribution The Central Limit Theorem For Further Exploration

7. Hypothesis and Inference

Statistical Hypothesis Testing Example: Flipping a Coin Confidence Intervals P-hacking Example: Running an A/B Test Bayesian Inference For Further Exploration

8. Gradient Descent

The Idea Behind Gradient Descent Estimating the Gradient Using the Gradient Choosing the Right Step Size Putting It All Together Stochastic Gradient Descent For Further Exploration

9. Getting Data

stdin and stdout Reading Files

The Basics of Text Files Delimited Files

Scraping the Web

HTML and the Parsing Thereof Example: O’Reilly Books About Data

Using APIs

JSON (and XML) Using an Unauthenticated API Finding APIs

Example: Using the Twitter APIs

Getting Credentials

Using Twython

For Further Exploration

10. Working with Data

Exploring Your Data

Exploring One-Dimensional Data Two Dimensions Many Dimensions

Cleaning and Munging Manipulating Data Rescaling Dimensionality Reduction For Further Exploration

11. Machine Learning

Modeling What Is Machine Learning? Overfitting and Underfitting Correctness The Bias-Variance Trade-off Feature Extraction and Selection For Further Exploration

12. k-Nearest Neighbors

The Model Example: Favorite Languages The Curse of Dimensionality For Further Exploration

13. Naive Bayes

A Really Dumb Spam Filter A More Sophisticated Spam Filter Implementation Testing Our Model For Further Exploration

14. Simple Linear Regression

The Model Using Gradient Descent Maximum Likelihood Estimation For Further Exploration

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

16. Logistic Regression

The Problem The Logistic Function Applying the Model Goodness of Fit Support Vector Machines For Further Investigation

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

18. Neural Networks

Perceptrons Feed-Forward Neural Networks Backpropagation Example: Defeating a CAPTCHA For Further Exploration

19. Clustering

The Idea The Model Example: Meetups Choosing k Example: Clustering Colors Bottom-up Hierarchical Clustering For Further Exploration

20. Natural Language Processing

Word Clouds n-gram Models Grammars An Aside: Gibbs Sampling Topic Modeling For Further Exploration

21. Network Analysis

Betweenness Centrality Eigenvector Centrality

Matrix Multiplication Centrality

Directed Graphs and PageRank For Further Exploration

22. Recommender Systems

Manual Curation Recommending What’s Popular User-Based Collaborative Filtering Item-Based Collaborative Filtering For Further Exploration

23. Databases and SQL

CREATE TABLE and INSERT UPDATE DELETE SELECT GROUP BY ORDER BY JOIN Subqueries Indexes Query Optimization NoSQL For Further Exploration

24. MapReduce

Example: Word Count Why MapReduce? MapReduce More Generally Example: Analyzing Status Updates Example: Matrix Multiplication An Aside: Combiners For Further Exploration

25. Go Forth and Do Data Science

IPython Mathematics Not from Scratch

NumPy pandas scikit-learn Visualization R

Find Data Do Data Science

Hacker News Fire Trucks T-shirts And You?

Index

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