# Data Science For Dummies (2016)

Part 1: Getting Started with Data Science

Chapter 1: Wrapping Your Head around Data Science

Chapter 2: Exploring Data Engineering Pipelines and Infrastructure

Chapter 3: Applying Data-Driven Insights to Business and Industry

Part 2: Using Data Science to Extract Meaning from Your Data

Chapter 4: Machine Learning: Learning from Data with Your Machine

Chapter 5: Math, Probability, and Statistical Modeling

Chapter 6: Using Clustering to Subdivide Data

Chapter 7: Modeling with Instances

Chapter 8: Building Models That Operate Internet-of-Things Devices

Part 3: Creating Data Visualizations That Clearly Communicate Meaning

Chapter 9: Following the Principles of Data Visualization Design

Chapter 10: Using D3.js for Data Visualization

Chapter 11: Web-Based Applications for Visualization Design

Chapter 12: Exploring Best Practices in Dashboard Design

Chapter 13: Making Maps from Spatial Data

Part 4: Computing for Data Science

Chapter 14: Using Python for Data Science

Chapter 15: Using Open Source R for Data Science

Chapter 16: Using SQL in Data Science

Chapter 17: Doing Data Science with Excel and Knime

Part 5: Applying Domain Expertise to Solve Real-World Problems Using Data Science

Chapter 18: Data Science in Journalism: Nailing Down the Five Ws (and an H)

Chapter 19: Delving into Environmental Data Science

Chapter 20: Data Science for Driving Growth in E-Commerce

Chapter 21: Using Data Science to Describe and Predict Criminal Activity

Part 6: The Part of Tens

Chapter 22: Ten Phenomenal Resources for Open Data

Chapter 23: Ten Free Data Science Tools and Applications