How to Think like a Data Scientist

Second Edition

by Brad Miller, Jacqueline Boggs, and Janice L Pearce

Table of Contents

• Assignments

• Preface

• 1. Introduction
  • 1.1. Introduction
    • 1.1.1. Learning Goals
    • 1.1.2. Learning Objectives
    • 1.1.3. What Is Data Science?
    • 1.1.4. What does it all mean?
    • 1.1.5. What does a data scientist do?
    • 1.1.6. Data Science in a Liberal Arts Context
    • 1.1.7. The Data Science Pipeline
    • 1.1.8. Data Science in This Text
    • 1.1.9. Datasets in this Book
    • 1.1.10. How to Use This Book
  • 1.2. Glossary
• 2. Exploring the Data Science Pipeline via Descriptive Statistics
  • 2.1. Introduction
    • 2.1.1. Learning Goals
    • 2.1.2. Learning Objectives
    • 2.1.3. Reading List
    • 2.1.4. Time Required
    • 2.1.5. Summary Statistics
  • 2.2. Case Study 1: The Happiness Report
    • 2.2.1. Introducing the Happiness Report
    • 2.2.2. Happiness Index Research Questions
    • 2.2.3. Summary Statistics
    • 2.2.4. Visualizing Happiness
  • 2.3. Case Study 1: Adding More Happiness Data
    • 2.3.1. Happiness by Region
    • 2.3.2. Joining Data from Other Sources
    • 2.3.3. Introducing Pivot Tables
  • 2.4. Case Study 1: Comparing Happiness Data across Years
  • 2.5. Challenge in Case Study 1: Calculating a Correlation Matrix
  • 2.6. Case Study 2: Considering Starting a Business?
    • 2.6.1. Thinking About Starting Your Business
    • 2.6.2. Business Start-Up Data Analysis Research Questions
    • 2.6.3. Visualizing How to Start a Business
  • 2.7. Case Study 2: Where Should We Start Our New Business?
    • 2.7.1. Business Score by Region
    • 2.7.2. Joining Data from Other Sources When Considering Starting a New Business
    • 2.7.3. Summarizing Key Business Data Using Pivot Table
  • 2.8. Case Study 2: How is Business Over Time?
  • 2.9. Case Study 2: Calculating a Correlation Matrix for Business Data
  • 2.10. Glossary
    • 2.10.1. Definitions
    • 2.10.2. Keywords
• 3. Optimization
  • 3.1. Introduction
    • 3.1.1. Learning Goals
    • 3.1.2. Learning Objectives
    • 3.1.3. Reading List
  • 3.2. Optimization Using Solver
  • 3.3. Small Problems To Try
  • 3.4. Optimizing Your Class Schedule
  • 3.5. Glossary
• 4. Python and Jupyter Notebooks
  • 4.1. Introduction
    • 4.1.1. Learning Goals
    • 4.1.2. Learning Objectives
  • 4.2. Python Review
    • 4.2.1. Variables
    • 4.2.2. Numeric Data Types
    • 4.2.3. Booleans
    • 4.2.4. Strings
    • 4.2.5. Conditional Statements
    • 4.2.6. Lists
    • 4.2.7. Range
    • 4.2.8. For Loops
    • 4.2.9. Dictionaries
    • 4.2.10. Functions
    • 4.2.11. Map Functions
    • 4.2.12. Lambda Functions
    • 4.2.13. List Comprehensions
    • 4.2.14. Some Additional Important Python Knowledge
  • 4.3. Jupyter Notebooks
    • 4.3.1. Notebook Functionalities
    • 4.3.2. Using Jupyter Notebooks
  • 4.4. Installing Anaconda
    • 4.4.1. Starting Jupyter Lab
  • 4.5. Using Google Colaboratory Notebooks
    • 4.5.1. Getting Google Colab Set Up
    • 4.5.2. The UI
    • 4.5.3. Colab Code Cells
  • 4.6. Markdown Cells
  • 4.7. Glossary
• 5. Learning Pandas with Movie Data
  • 5.1. Introduction
    • 5.1.1. Learning Goals
    • 5.1.2. Learning Objectives
  • 5.2. Jupyter Notebooks
    • 5.2.1. Text Cells
    • 5.2.2. Code Cells
  • 5.3. Pandas Exercises
    • 5.3.1. Exploring the Data
  • 5.4. Filtering the Data
  • 5.5. Numbers as Indices
    • 5.5.1. Practice Questions
  • 5.6. Dealing with Multiple DataFrames
  • 5.7. Glossary
    • 5.7.1. Definitions
    • 5.7.2. Keywords
• 6. Exploratory Data Analysis
  • 6.1. Introduction
    • 6.1.1. Learning Goals
    • 6.1.2. Learning Objectives
    • 6.1.3. Reading List
  • 6.2. Case Study 1: Exploratory Data Analysis
    • 6.2.1. Loading Data into a DataFrame from a CSV File
    • 6.2.2. Describing the Data
    • 6.2.3. Visualizing Distribution with Histograms
    • 6.2.4. Practice
    • 6.2.5. Scatter Plots for Discovering Relationships
    • 6.2.6. Power Tools: Scatter Matrix
    • 6.2.7. Developing Fluency
    • 6.2.8. Practice Questions
  • 6.3. Case Study 1: Graphing Infant Mortality on a Map
    • 6.3.1. Using a Web API to get Country Codes
    • 6.3.2. Using a Web API on Your Own
  • 6.4. Case Study 1: Screen Scraping the CIA
    • 6.4.1. Getting a List of All Fields
    • 6.4.2. Loading All the Data in Rough Form
    • 6.4.3. Cleaning the Data
    • 6.4.4. Saving the Data
    • 6.4.5. Comparing Across the Years
  • 6.5. Case Study 1: Comparing Forms of Government
    • 6.5.1. Project
  • 6.6. Case Study 2: Analyzing Protecting Minority Investors
    • 6.6.1. Loading Minority Investors Data into a DataFrame
    • 6.6.2. Describing Minority Investors
    • 6.6.3. Visualizing Minority Investors with a Histogram
    • 6.6.4. Discovering Relationships with Scatter Plots
    • 6.6.5. Minority Investors: Scatter Matrix
    • 6.6.6. Practice Questions
  • 6.7. Case Study 2: Graphing Business Data on a Map
    • 6.7.1. Getting Country Codes from a Web API
  • 6.8. Case Study 2: Scraping Business Data Using Panda and BeautifulSoup
    • 6.8.1. Getting a List of Business Fields
    • 6.8.2. Loading Business Data in Rough Form
    • 6.8.3. Cleaning Business Data
    • 6.8.4. Saving the Business Data
    • 6.8.5. Comparing Business Data Across the Years
  • 6.9. Case Study 2: Comparing CIA Government Forms
    • 6.9.1. Project
  • 6.10. Glossary
    • 6.10.1. Definitions
    • 6.10.2. Keywords
• 7. Ethical and Legal Considerations in Using Data
  • 7.1. Introduction
    • 7.1.1. Learning Goals
    • 7.1.2. Learning Objectives
    • 7.1.3. Reading List
  • 7.2. Good Data Sources
    • 7.2.1. Data Source List
    • 7.2.2. Screen Scraping Considerations
  • 7.3. Finding the Cheaters
  • 7.4. Glossary
    • 7.4.1. Definitions
• 8. Textual Analysis
  • 8.1. Introduction
    • 8.1.1. Learning Goals
    • 8.1.2. Learning Objectives
  • 8.2. Text Mining
    • 8.2.1. Reading List
    • 8.2.2. Exploratory Questions
    • 8.2.3. OH NO, what the heck!!
  • 8.3. Merging and Tidying Data
    • 8.3.1. Tidy Data
    • 8.3.2. Tidying the Movie Genres
  • 8.4. Tidying Text: Most and Least Common Words
    • 8.4.1. Practice
  • 8.5. Working with Text
    • 8.5.1. Research Questions
    • 8.5.2. Text Complexity
  • 8.6. Graphing Relationships Between Data
    • 8.6.1. Preparing the Data
    • 8.6.2. Visualizing the Relationships with a Heatmap
    • 8.6.3. Visualizing the Relationships with a Graph
    • 8.6.4. Projects for Further Exploration
  • 8.7. Sentiment Analysis
    • 8.7.1. Naive Bayes
    • 8.7.2. Using NLTK to Score the Speeches
    • 8.7.3. Questions for Investigation
  • 8.8. Glossary
    • 8.8.1. Defintions
    • 8.8.2. Keywords
• 9. Predictive Analytics
  • 9.1. Introduction
    • 9.1.1. Learning Goals
    • 9.1.2. Learning Objectives
  • 9.2. Predicting Bike Rentals
  • 9.3. Exploring Bike Rental Data with SQL
    • 9.3.1. Query Language
  • 9.4. Getting Started with the Bike Data
    • 9.4.1. Verify access to the dataset
  • 9.5. Filtering
    • 9.5.1. Practice Exercises
  • 9.6. Sorting
    • 9.6.1. Practice Exercises
  • 9.7. Aggregation or Group By
    • 9.7.1. Practice Exercises
  • 9.8. Joining
    • 9.8.1. Practice Exercises
  • 9.9. Getting SQL Data into a DataFrame
    • 9.9.1. Mapping Bike Stations Using Anaconda
    • 9.9.2. Practice and Further Exploration
  • 9.10. Mapping Bike Stations Using Colab
    • 9.10.1. Practice and Further Exploration
  • 9.11. Visualizing Bike Share Data as a Time Series
    • 9.11.1. Indexing with a DatetimeIndex
    • 9.11.2. Next Step: Predicting Rentals
    • 9.11.3. Working with ZIP Files (Optional)
  • 9.12. Predicting Pizza Prices - Linear Regression
    • 9.12.1. Closed-Form Solution
    • 9.12.2. The Payoff - Supervised Learning
    • 9.12.3. A Simple Machine Learning Approach (Optional)
  • 9.13. Improving our Pizza Price Predictions
    • 9.13.1. Introducing Scikit-Learn
    • 9.13.2. Practice
  • 9.14. Predicting Daily Bike Rentals
    • 9.14.1. The Train-Test Split
    • 9.14.2. Evaluating the Model
    • 9.14.3. Feature Engineering
    • 9.14.4. Version 1.0 Task List
    • 9.14.5. Refining the Model
    • 9.14.6. Version 2.0 Task List
    • 9.14.7. Version 2.5 Task List
    • 9.14.8. Version 3.0
    • 9.14.9. Feature Engineering - Re-Scaling
    • 9.14.10. Where to go from here?
  • 9.15. Glossary
    • 9.15.1. Definitions
    • 9.15.2. Keywords
• 10. Exploring Market Basket Analysis
  • 10.1. Introduction
    • 10.1.1. Learning Goals
    • 10.1.2. Learning Objectives
  • 10.2. Exploring Our Data Set
    • 10.2.1. Reading List
    • 10.2.2. The Data
    • 10.2.3. aisles.csv
    • 10.2.4. departments.csv
    • 10.2.5. order_products__prior.csv
    • 10.2.6. orders.csv
    • 10.2.7. products.csv
  • 10.3. Exploratory Data Analysis Questions
  • 10.4. Market Basket Analysis
    • 10.4.1. Constructing an Item-Item Matrix
    • 10.4.2. Testing The Item-Item Matrix
    • 10.4.3. Cleaning Up and Saving
    • 10.4.4. Understanding the Item-Item Matrix
    • 10.4.5. Experimenting with Item-Item Recommendations
  • 10.5. Visualizing Data
  • 10.6. Glossary
    • 10.6.1. Definitions
    • 10.6.2. Keywords

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