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BRIEF CONTENTS Acknowledgments Introduction Part I: Data Mining Chapter 1: The Programming Languages You’ll Need to Know Chapter 2: Where to Get Your Data Chapter 3: Getting Data with Code Chapter 4: Scraping Your Own Facebook Data Chapter 5: Scraping a Live Site Part II: Data Analysis Chapter 6: Introduction to Data Analysis Chapter 7: Visualizing Your Data Chapter 8: Advanced Tools for Data Analysis Chapter 9: Finding Trends in Reddit Data Chapter 10: Measuring the Twitter Activity of Political Actors Chapter 11: Where to Go from Here Index CONTENTS IN DETAIL Acknowledgments Introduction What Is Data Analysis? Who Is This Book For? Conventions Used in This Book What This Book Covers Part I: Data Mining Part II: Data Analysis Downloading and Installing Python Installing on Windows Installing on macOS Getting Help When You’re Stuck Summary PART I: DATA MINING 1 THE PROGRAMMING LANGUAGES YOU’LL NEED TO KNOW Frontend Languages How HTML Works How CSS Works How JavaScript Works Backend Languages Using Python Getting Started with Python Working with Numbers Working with Strings Storing Values in Variables Storing Multiple Values in Lists Working with Functions Creating Your Own Functions Using Loops Using Conditionals Summary 2 WHERE TO GET YOUR DATA What Is an API? Using an API to Get Data Getting a YouTube API Key Retrieving JSON Objects Using Your Credentials Answering a Research Question Using Data Refining the Data That Your API Returns Summary 3 GETTING DATA WITH CODE Writing Your First Script Running a Script Planning Out a Script Libraries and pip Creating a URL-based API Call Storing Data in a Spreadsheet Converting JSON into a Dictionary Going Back to the Script Running the Finished Script Dealing with API Pagination Templates: How to Make Your Code Reusable Storing Values That Change in Variables Storing Code in a Reusable Function Summary 4 SCRAPING YOUR OWN FACEBOOK DATA Your Data Sources Downloading Your Facebook Data Reviewing the Data and Inspecting the Code Structuring Information as Data Scraping Automatically Analyzing HTML Code to Recognize Patterns Grabbing the Elements You Need Extracting the Contents Writing Data into a Spreadsheet Building Your Rows List Writing to Your .csv File Running the Script Summary 5 SCRAPING A LIVE SITE Messy Data Ethical Considerations for Data Scraping The Robots Exclusion Protocol The Terms of Service Technical Considerations for Data Scraping Reasons for Scraping Data Scraping from a Live Website Analyzing the Page’s Contents Storing the Page Content in Variables Making the Script Reusable Practicing Polite Scraping Summary PART II: DATA ANALYSIS 6 INTRODUCTION TO DATA ANALYSIS The Process of Data Analysis Bot Spotting Getting Started with Google Sheets Modifying and Formatting the Data Aggregating the Data Using Pivot Tables to Summarize Data Using Formulas to Do Math Sorting and Filtering the Data Merging Data Sets Other Ways to Use Google Sheets Summary 7 VISUALIZING YOUR DATA Understanding Our Bot Through Charts Choosing a Chart Specifying a Time Period Making a Chart Conditional Formatting Single-Color Formatting Color Scale Formatting Summary 8 ADVANCED TOOLS FOR DATA ANALYSIS Using Jupyter Notebook Setting Up a Virtual Environment Organizing the Notebook Installing Jupyter and Creating Your First Notebook Working with Cells What Is pandas? Working with Series and Data Frames Reading and Exploring Large Data Files Looking at the Data Viewing Specific Columns and Rows Summary 9 FINDING TRENDS IN REDDIT DATA Clarifying Our Research Objective Outlining a Method Narrowing the Data’s Scope Selecting Data from Specific Columns Handling Null Values Classifying the Data Summarizing the Data Sorting the Data Describing the Data Summary 10 MEASURING THE TWITTER ACTIVITY OF POLITICAL ACTORS Getting Started Setting Up Your Environment Loading the Data into Your Notebook Lambdas Filtering the Data Set Formatting the Data as datetimes Resampling the Data Plotting the Data Summary 11 WHERE TO GO FROM HERE Coding Styles Statistical Analysis Other Kinds of Analyses Conclusion Index
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