learningds.org

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• http://learningds.org/index.html
• http://learningds.org/preface.html
• 1. The Data Science Lifecycle
• 1.1. The Stages of the Lifecycle
• 1.2. Examples of the Lifecycle
• 1.3. Summary
• 2. Questions and Data Scope
• 2.1. Big Data and New Opportunities
• 2.2. Target Population, Access Frame, and Sample
• 2.3. Instruments and Protocols
• 2.4. Measuring Natural Phenomena
• 2.5. Accuracy
• 2.6. Summary
• 3. Simulation and Data Design
• 3.1. The Urn Model
• 3.2. Example: Simulating Election Poll Bias and Variance
• 3.3. Example: Simulating a Randomized Trial for a Vaccine
• 3.4. Example: Measuring Air Quality
• 3.5. Summary
• 4. Modeling with Summary Statistics
• 4.1. The Constant Model
• 4.2. Minimizing Loss
• 4.3. Summary
• 5. Case Study: Why Is My Bus Always Late?
• 5.1. Question and Scope
• 5.2. Data Wrangling
• 5.3. Exploring Bus Times
• 5.4. Modeling Wait Times
• 5.5. Summary
• 6. Working with Dataframes Using pandas
• 6.1. Subsetting
• 6.2. Aggregating
• 6.3. Joining
• 6.4. Transforming
• 6.5. How Are Dataframes Different from Other Data Representations?
• 6.6. Summary
• 7. Working with Relations Using SQL
• 7.1. Subsetting
• 7.2. Aggregating
• 7.3. Joining
• 7.4. Transforming and Common Table Expressions
• 7.5. Summary
• 8. Wrangling Files
• 8.1. Data Source Examples
• 8.2. File Formats
• 8.3. File Encoding
• 8.4. File Size
• 8.5. The Shell and Command-Line Tools
• 8.6. Table Shape and Granularity
• 8.7. Summary
• 9. Wrangling Dataframes
• 9.1. Example: Wrangling CO
• 9.2. Quality Checks
• 9.3. Missing Values and Records
• 9.4. Transformations and Timestamps
• 9.5. Modifying Structure
• 9.6. Example: Wrangling Restaurant Safety Violations
• 9.7. Summary
• 10. Exploratory Data Analysis
• 10.1. Feature Types
• 10.2. What to Look For in a Distribution
• 10.3. What to Look For in a Relationship
• 10.4. Comparisons in Multivariate Settings
• 10.5. Guidelines for Exploration
• 10.6. Example: Sale Prices for Houses
• 10.7. Summary
• 11. Data Visualization
• 11.1. Choosing Scale to Reveal Structure
• 11.2. Smoothing and Aggregating Data
• 11.3. Facilitating Meaningful Comparisons
• 11.4. Incorporating the Data Design
• 11.5. Adding Context
• 11.6. Creating Plots Using plotly
• 11.7. Other Tools for Visualization
• 11.8. Summary
• 12. Case Study: How Accurate Are Air Quality Measurements?
• 12.1. Question, Design, and Scope
• 12.2. Finding Collocated Sensors
• 12.3. Wrangling and Cleaning AQS Sensor Data
• 12.4. Wrangling PurpleAir Sensor Data
• 12.5. Exploring PurpleAir and AQS Measurements
• 12.6. Creating a Model to Correct PurpleAir Measurements
• 12.7. Summary
• 13. Working with Text
• 13.1. Examples of Text and Tasks
• 13.2. String Manipulation
• 13.3. Regular Expressions
• 13.4. Text Analysis
• 13.5. Summary
• 14. Data Exchange
• 14.1. NetCDF Data
• 14.2. JSON Data
• 14.3. HTTP
• 14.4. REST
• 14.5. XML, HTML, and XPath
• 14.6. Summary
• 15. Linear Models
• 15.1. Simple Linear Model
• 15.2. Example: A Simple Linear Model for Air Quality
• 15.3. Fitting the Simple Linear Model
• 15.4. Multiple Linear Model
• 15.5. Fitting the Multiple Linear Model
• 15.6. Example: Where Is the Land of Opportunity?
• 15.7. Feature Engineering for Numeric Measurements
• 15.8. Feature Engineering for Categorical Measurements
• 15.9. Summary
• 16. Model Selection
• 16.1. Overfitting
• 16.2. Train-Test Split
• 16.3. Cross-Validation
• 16.4. Regularization
• 16.6. Model Bias and Variance
• 16.7. Summary
• 17. Theory for Inference and Prediction
• 17.1. Distributions: Population, Empirical, Sampling
• 17.2. Basics of Hypothesis Testing
• 17.3. Bootstrapping for Inference
• 17.4. Basics of Confidence Intervals
• 17.5. Basics of Prediction Intervals
• 17.6. Probability for Inference and Prediction
• 17.7. Summary
• 18. Case Study: How to Weigh a Donkey
• 18.1. Donkey Study Question and Scope
• 18.2. Wrangling and Transforming
• 18.3. Exploring
• 18.4. Modeling a Donkey’s Weight
• 18.5. Summary
• 19. Classification
• 19.1. Example: Wind-Damaged Trees
• 19.2. Modeling and Classification
• 19.3. Modeling Proportions (and Probabilities)
• 19.4. A Loss Function for the Logistic Model
• 19.5. From Probabilities to Classification
• 19.6. Summary
• 20. Numerical Optimization
• 20.1. Gradient Descent Basics
• 20.2. Minimizing Huber Loss
• 20.3. Convex and Differentiable Loss Functions
• 20.4. Variants of Gradient Descent
• 20.5. Summary
• 21. Case Study: Detecting Fake News
• 21.1. Question and Scope
• 21.2. Obtaining and Wrangling the Data
• 21.3. Exploring the Data
• 21.4. Modeling
• 21.5. Summary
• Additional Material
• Data Sources
• Probability Review
• Vector Space Review
• http://learningds.org/_sources/intro.md
• #
• http://learningds.org/_images/book-cover.png

• Jupyter Book
• Sam Lau
• Joey Gonzalez
• Deb Nolan
• Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

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