Data Science from Scratch: First Principles with Python, 2nd Edition
- Length: 500 pages
- Edition: 2
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2019-06-04
- ISBN-10: 1492041130
- ISBN-13: 9781492041139
- Sales Rank: #504782 (See Top 100 Books)
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.
Table of Contents
1. Introduction
2. A Crash Course in Python
3. Visualizing Data
4. Linear Algebra
5. Statistics
6. Probability
7. Hypothesis and Inference
8. Gradient Descent
9. Getting Data
10. Working with Data
11. Machine Learning
12. k-Nearest Neighbors
13. Naive Bayes
14. Simple Linear Regression
15. Multiple Regression
16. Logistic Regression
17. Decision Trees
18. Neural Networks
19. Deep Learning
20. Clustering
21. Natural Language Processing
22. Network Analysis
23. Recommender Systems
24. Databases and SQL
25. MapReduce
26. Data Ethics
27. Go Forth and Do Data Science
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 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 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 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 p-Values 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 Using Gradient Descent to Fit Models Minibatch and 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: Keeping Tabs on Congress 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 Using NamedTuples Dataclasses Cleaning and Munging Manipulating Data Rescaling An Aside: tqdm Dimensionality Reduction For Further Exploration 11. Machine Learning Modeling What Is Machine Learning? Overfitting and Underfitting Correctness The Bias-Variance Tradeoff Feature Extraction and Selection For Further Exploration 12. k-Nearest Neighbors The Model Example: The Iris Dataset The Curse of Dimensionality For Further Exploration 13. Naive Bayes A Really Dumb Spam Filter A More Sophisticated Spam Filter Implementation Testing Our Model Using 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: Fizz Buzz For Further Exploration 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 20. Clustering The Idea The Model Example: Meetups Choosing k Example: Clustering Colors Bottom-Up Hierarchical Clustering For Further Exploration 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 22. Network Analysis Betweenness Centrality Eigenvector Centrality Matrix Multiplication Centrality Directed Graphs and PageRank For Further Exploration 23. Recommender Systems Manual Curation Recommending What’s Popular User-Based Collaborative Filtering Item-Based Collaborative Filtering Matrix Factorization For Further Exploration 24. Databases and SQL CREATE TABLE and INSERT UPDATE DELETE SELECT GROUP BY ORDER BY JOIN Subqueries Indexes Query Optimization NoSQL For Further Exploration 25. MapReduce Example: Word Count Why MapReduce? MapReduce More Generally Example: Analyzing Status Updates Example: Matrix Multiplication An Aside: Combiners For Further Exploration 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 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
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