Data Science from Scratch. First Principles with Python. 2nd Edition

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• Preface to the Second Edition
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly 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
  • Simpsons Paradox
  • Some Other Correlational Caveats
  • Correlation and Causation
  • For Further Exploration
• 6. Probability
  • Dependence and Independence
  • Conditional Probability
  • Bayess 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 Whats 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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