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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.

First Principles with Python

  • Get a crash course in Python
  • Learn the basics of linear algebra, statistics, and probability—and understand 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, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
  • Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Contents

Chapter 1 Introduction

  • The Ascendance of Data
  • What Is Data Science?
  • Motivating Hypothetical: DataSciencester

Chapter 2 A Crash Course in Python

  • The Basics
  • The Not-So-Basics
  • 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
  • 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
  • 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
  • Putting It All Together
  • Stochastic Gradient Descent
  • For Further Exploration

Chapter 9 Getting Data

  • stdin and stdout
  • Reading Files
  • Scraping the Web
  • Using APIs
  • Example: Using the Twitter APIs
  • For Further Exploration

Chapter 10 Working with Data

  • Exploring Your Data
  • Cleaning and Munging
  • Manipulating Data
  • Rescaling
  • Dimensionality Reduction
  • For Further Exploration

Chapter 11 Machine Learning

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

Chapter 12 k-Nearest Neighbors

  • The Model
  • Example: Favorite Languages
  • 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
  • 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: Defeating a CAPTCHA
  • For Further Exploration

Chapter 19 Clustering

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

Chapter 20 Natural Language Processing

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

Chapter 21 Network Analysis

  • Betweenness Centrality
  • Eigenvector Centrality
  • Directed Graphs and PageRank
  • For Further Exploration

Chapter 22 Recommender Systems

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

Chapter 23 Databases and SQL

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

Chapter 24 MapReduce

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

Chapter 25 Go Forth and Do Data Science

  • IPython
  • Mathematics
  • Not from Scratch
  • Find Data
  • Do Data Science

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