Learning Data Science

Spis treści książki

• Preface
  • Expected Background Knowledge
  • Organization of the Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. The Data Science Lifecycle
• 1. The Data Science Lifecycle
  • The Stages of the Lifecycle
  • Examples of the Lifecycle
  • Summary
• 2. Questions and Data Scope
  • Big Data and New Opportunities
    • Example: Google Flu Trends
  • Target Population, Access Frame, and Sample
    • Example: What Makes Members of an Online Community Active?
    • Example: Who Will Win the Election?
    • Example: How Do Environmental Hazards Relate to an Individuals Health?
  • Instruments and Protocols
  • Measuring Natural Phenomena
    • Example: What Is the Level of CO2 in the Air?
  • Accuracy
    • Types of Bias
    • Types of Variation
  • Summary
• 3. Simulation and Data Design
  • The Urn Model
    • Sampling Designs
    • Sampling Distribution of a Statistic
    • Simulating the Sampling Distribution
    • Simulation with the Hypergeometric Distribution
  • Example: Simulating Election Poll Bias and Variance
    • The Pennsylvania Urn Model
    • An Urn Model with Bias
    • Conducting Larger Polls
  • Example: Simulating a Randomized Trial for a Vaccine
    • Scope
    • The Urn Model for Random Assignment
  • Example: Measuring Air Quality
  • Summary
• 4. Modeling with Summary Statistics
  • The Constant Model
  • Minimizing Loss
    • Mean Absolute Error
    • Mean Squared Error
    • Choosing Loss Functions
  • Summary
• 5. Case Study: Why Is My Bus Always Late?
  • Question and Scope
  • Data Wrangling
  • Exploring Bus Times
  • Modeling Wait Times
  • Summary
• II. Rectangular Data
• 6. Working with Dataframes Using pandas
  • Subsetting
    • Data Scope and Question
    • Dataframes and Indices
    • Slicing
    • Filtering Rows
    • Example: How Recently Has Luna Become a Popular Name?
  • Aggregating
    • Basic Group-Aggregate
      • Example: Using .value_counts()
    • Grouping on Multiple Columns
    • Custom Aggregation Functions
    • Pivoting
  • Joining
    • Inner Joins
    • Left, Right, and Outer Joins
    • Example: Popularity of NYT Name Categories
  • Transforming
    • Apply
    • Example: Popularity of L Names
    • The Price of Apply
  • How Are Dataframes Different from Other Data Representations?
    • Dataframes and Spreadsheets
    • Dataframes and Matrices
    • Dataframes and Relations
  • Summary
• 7. Working with Relations Using SQL
  • Subsetting
    • SQL Basics: SELECT and FROM
    • Whats a Relation?
    • Slicing
    • Filtering Rows
    • Example: How Recently Has Luna Become a Popular Name?
  • Aggregating
    • Basic Group-Aggregate Using GROUP BY
    • Grouping on Multiple Columns
    • Other Aggregation Functions
  • Joining
    • Inner Joins
    • Left and Right Joins
    • Example: Popularity of NYT Name Categories
  • Transforming and Common Table Expressions
    • SQL Functions
    • Multistep Queries Using a WITH Clause
    • Example: Popularity of L Names
  • Summary
• III. Understanding The Data
• 8. Wrangling Files
  • Data Source Examples
    • Drug Abuse Warning Network (DAWN) Survey
    • San Francisco Restaurant Food Safety
  • File Formats
    • Delimited Format
    • Fixed-Width Format
    • Hierarchical Formats
    • Loosely Formatted Text
  • File Encoding
  • File Size
  • The Shell and Command-Line Tools
  • Table Shape and Granularity
    • Granularity of Restaurant Inspections and Violations
    • DAWN Survey Shape and Granularity
  • Summary
• 9. Wrangling Dataframes
  • Example: Wrangling CO2 Measurements from the Mauna Loa Observatory
    • Quality Checks
    • Addressing Missing Data
    • Reshaping the Data Table
  • Quality Checks
    • Quality Based on Scope
    • Quality of Measurements and Recorded Values
    • Quality Across Related Features
    • Quality for Analysis
    • Fixing the Data or Not
  • Missing Values and Records
  • Transformations and Timestamps
    • Transforming Timestamps
    • Piping for Transformations
  • Modifying Structure
  • Example: Wrangling Restaurant Safety Violations
    • Narrowing the Focus
    • Aggregating Violations
    • Extracting Information from Violation Descriptions
  • Summary
• 10. Exploratory Data Analysis
  • Feature Types
    • Example: Dog Breeds
    • Transforming Qualitative Features
      • Relabel categories
      • Collapse categories
      • Convert quantitative to ordinal
    • The Importance of Feature Types
  • What to Look For in a Distribution
  • What to Look For in a Relationship
    • Two Quantitative Features
    • One Qualitative and One Quantitative Variable
    • Two Qualitative Features
  • Comparisons in Multivariate Settings
  • Guidelines for Exploration
  • Example: Sale Prices for Houses
    • Understanding Price
    • What Next?
    • Examining Other Features
    • Delving Deeper into Relationships
    • Fixing Location
    • EDA Discoveries
  • Summary
• 11. Data Visualization
  • Choosing Scale to Reveal Structure
    • Filling the Data Region
    • Including Zero
    • Revealing Shape Through Transformations
    • Banking to Decipher Relationships
    • Revealing Relationships Through Straightening
  • Smoothing and Aggregating Data
    • Smoothing Techniques to Uncover Shape
    • Smoothing Techniques to Uncover Relationships and Trends
    • Smoothing Techniques Need Tuning
    • Reducing Distributions to Quantiles
    • When Not to Smooth
  • Facilitating Meaningful Comparisons
    • Emphasize the Important Difference
    • Ordering Groups
    • Avoid Stacking
    • Selecting a Color Palette
    • Guidelines for Comparisons in Plots
  • Incorporating the Data Design
    • Data Collected Over Time
    • Observational Studies
    • Unequal Sampling
    • Geographic Data
  • Adding Context
    • Example: 100m Sprint Times
  • Creating Plots Using plotly
    • Figure and Trace Objects
    • Modifying Layout
    • Plotting Functions
    • Annotations
  • Other Tools for Visualization
    • matplotlib
    • Grammar of Graphics
  • Summary
• 12. Case Study: How Accurate Are Air Quality Measurements?
  • Question, Design, and Scope
  • Finding Collocated Sensors
    • Wrangling the List of AQS Sites
    • Wrangling the List of PurpleAir Sites
    • Matching AQS and PurpleAir Sensors
  • Wrangling and Cleaning AQS Sensor Data
    • Checking Granularity
    • Removing Unneeded Columns
    • Checking the Validity of Dates
    • Checking the Quality of PM2.5 Measurements
  • Wrangling PurpleAir Sensor Data
    • Checking the Granularity
      • Visualizing timestamps
      • Checking the sampling rate
    • Handling Missing Values
  • Exploring PurpleAir and AQS Measurements
  • Creating a Model to Correct PurpleAir Measurements
  • Summary
• IV. Other Data Sources
• 13. Working with Text
  • Examples of Text and Tasks
    • Convert Text into a Standard Format
    • Extract a Piece of Text to Create a Feature
    • Transform Text into Features
    • Text Analysis
  • String Manipulation
    • Converting Text to a Standard Format with Python String Methods
    • String Methods in pandas
    • Splitting Strings to Extract Pieces of Text
  • Regular Expressions
    • Concatenation of Literals
      • Character classes
      • Wildcard character
      • Negated character classes
      • Shorthands for character classes
      • Anchors and boundaries
      • Escaping metacharacters
    • Quantifiers
    • Alternation and Grouping to Create Features
    • Reference Tables
  • Text Analysis
  • Summary
• 14. Data Exchange
  • NetCDF Data
  • JSON Data
  • HTTP
  • REST
  • XML, HTML, and XPath
    • Example: Scraping Race Times from Wikipedia
    • XPath
    • Example: Accessing Exchange Rates from the ECB
  • Summary
• V. Linear Modeling
• 15. Linear Models
  • Simple Linear Model
  • Example: A Simple Linear Model for Air Quality
    • Interpreting Linear Models
    • Assessing the Fit
  • Fitting the Simple Linear Model
  • Multiple Linear Model
  • Fitting the Multiple Linear Model
  • Example: Where Is the Land of Opportunity?
    • Explaining Upward Mobility Using Commute Time
    • Relating Upward Mobility Using Multiple Variables
  • Feature Engineering for Numeric Measurements
  • Feature Engineering for Categorical Measurements
  • Summary
• 16. Model Selection
  • Overfitting
    • Example: Energy Consumption
  • Train-Test Split
  • Cross-Validation
  • Regularization
  • Model Bias and Variance
  • Summary
• 17. Theory for Inference and Prediction
  • Distributions: Population, Empirical, Sampling
  • Basics of Hypothesis Testing
    • Example: A Rank Test to Compare Productivity of Wikipedia Contributors
    • Example: A Test of Proportions for Vaccine Efficacy
  • Bootstrapping for Inference
  • Basics of Confidence Intervals
  • Basics of Prediction Intervals
    • Example: Predicting Bus Lateness
    • Example: Predicting Crab Size
    • Example: Predicting the Incremental Growth of a Crab
  • Probability for Inference and Prediction
    • Formalizing the Theory for Average Rank Statistics
    • General Properties of Random Variables
    • Probability Behind Testing and Intervals
    • Probability Behind Model Selection
  • Summary
• 18. Case Study: How to Weigh a Donkey
  • Donkey Study Question and Scope
  • Wrangling and Transforming
  • Exploring
  • Modeling a Donkeys Weight
    • A Loss Function for Prescribing Anesthetics
    • Fitting a Simple Linear Model
    • Fitting a Multiple Linear Model
    • Bringing Qualitative Features into the Model
    • Model Assessment
  • Summary
• VI. Classification
• 19. Classification
  • Example: Wind-Damaged Trees
  • Modeling and Classification
    • A Constant Model
    • Examining the Relationship Between Size and Windthrow
  • Modeling Proportions (and Probabilities)
    • A Logistic Model
    • Log Odds
    • Using a Logistic Curve
  • A Loss Function for the Logistic Model
  • From Probabilities to Classification
    • The Confusion Matrix
    • Precision Versus Recall
  • Summary
• 20. Numerical Optimization
  • Gradient Descent Basics
  • Minimizing Huber Loss
  • Convex and Differentiable Loss Functions
  • Variants of Gradient Descent
    • Stochastic Gradient Descent
    • Mini-Batch Gradient Descent
    • Newtons Method
  • Summary
• 21. Case Study: Detecting Fake News
  • Question and Scope
  • Obtaining and Wrangling the Data
  • Exploring the Data
    • Exploring the Publishers
    • Exploring Publication Date
    • Exploring Words in Articles
  • Modeling
    • A Single-Word Model
    • Multiple-Word Model
    • Predicting with the tf-idf Transform
  • Summary
• Additional Material
• Data Sources
• Index

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