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Introduction to Data Science:
A Low-Code Project-Based Approach
Hannah Kurzweil
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Front Matter
1
Unit 1: Foundations of Data Science
What is Data Science?
Defining Data Science
Why Data Literacy Matters
Applications of Data Science
Introduction to CODAP
The CODAP Interface
Your First CODAP Exploration
Project Launch: Community Health and Environment
The Sample Project
Selecting Your Own Dataset
Understanding Data Types
Qualitative vs. Quantitative Data
Measurement Scales
Other Data Classifications
Ethics Spotlight: Ethical Data Collection
Unit 1 Summary
2
Unit 2: The Data Investigation Process
The Data Investigation Framework
Overview of the Framework
The Framework in Practice
Formulating Statistical Questions
What Makes a Question Statistical?
Crafting Effective Statistical Questions
Planning an Investigation
Elements of an Investigation Plan
Sample Investigation Plan
Statistical Thinking: Understanding Variability
Key Concepts in Variability
Accounting for Variability in Data Analysis
Ethics Spotlight: Representation in Data
Unit 2 Summary
3
Unit 3: Essential Data Moves
Data Cleaning and Organization
Why Data Cleaning Matters
Handling Missing Values
Dealing with Outliers
Renaming and Restructuring Data
Filtering and Subsetting
The Purpose of Filtering
Filtering Methods in CODAP
Creating Meaningful Subsets
Ethics Spotlight: Selection Bias
Summarizing, Calculating, and Grouping
Essential Summary Statistics
Creating Derived Variables
Grouping and Aggregation Techniques
Statistical Thinking: Comparing Groups
Variation Within and Between Groups
Making Meaningful Comparisons
Unit 3 Summary
4
Unit 4: Data Visualization and Communication
Visualization Fundamentals
The Power of Data Visualization
Choosing the Right Visualization
Principles of Effective Visualization
Creating Visualizations in CODAP
Ethics Spotlight: Avoiding Misleading Visualizations
Advanced Visualization and Analysis
Visualizing Multi-Variable Relationships
Statistical Thinking: Correlation and Relationships
Project Synthesis and Presentation
Data Storytelling Principles
Effective Presentation Techniques
Communicating Data Ethically and Effectively
Unit 4 Summary
5
Data Science Project Guide
Project Overview
Learning Objectives
Dataset Selection Guidelines
Project Workflow and Timeline
Phase 1: Dataset Selection + Exploration (Unit 1)
Phase 2: Investigation Planning (Unit 2)
Phase 3: Data Analysis and Processing (Unit 3)
Phase 4: Visualization and Communication (Unit 4)
Assessment and Grading
Evaluation Rubrics
Dataset Selection Report
Investigation Plan
Data Processing Documentation
Final Presentation
Tips for Success
Common Challenges and Solutions
6
CODAP Reference Guide
Getting Started with CODAP
Accessing CODAP
Interface Overview
Importing Data
Core CODAP Features
Working with Tables
Creating and Customizing Graphs
Creating Calculated Attributes
Geographic Mapping
Advanced CODAP Techniques
Working with Hierarchical Data
Advanced Filtering Techniques
Advanced Data Import and Export
Building Interactive Dashboards
CODAP Tips, Tricks, and Troubleshooting
Keyboard Shortcuts and Efficiency Tips
Common Issues and Solutions
Advanced CODAP Tricks
CODAP Best Practices
Additional CODAP Resources
Backmatter
Colophon
Colophon
This book was authored in PreTeXt.
