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There are a number of books on applied data analysis, or data science. A prominent example is Wickham and Grolemund's (2017) R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. This does not attempt to supplement or improve on this and other excellent books on data science, but rather to approach the topic from the perspective of educational researchers carrying out research.

Consequently, this focuses in on the needs of educational researchers in three ways:

• Demonstrating approaches the most widespread data analytic problems.
• Adapting to the background knowledge and training of educational researchers.
• Utilizing examples from or relevant to educational research.

Part 1 is organized around Peng and Matsui's (2016) view of data science. They data science to consist of five processes:

• Stating the question
• Exploratory data analysis
• Model building
• Interpretation
• Communication

Part 2 is organized around more complex topics in educational research, with each topic explored with a three-part structure:

• Why an approach (i.e., Hierarchical Linear Models (HLM)) is used in educational research.
• How to do the approach.
• The mathematical or computational details of the approach.

1. Getting Started
2. Accessing Data
3. Processing Data
4. Descriptive Statistics
5. Visualizing Data
6. Modeling Data
7. Communicating Findings
8. Hierarchical Linear Models
9. Exploratory Factor Analysis
10. Confirmatory Factor Analysis and Structural Equation Modeling
11. Propensity Score Matching
12. Social Network Analysis
13. Text Analysis