Data Science from Scratch First Principles with Python 2nd Edition by Joel Grus ISBN 1492041084 9781492041085

Here is the table of contents for the book “Data Science from Scratch” (likely referring to Data Science from Scratch: First Principles with Python by Joel Grus):

---

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
• 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