Course shell for CPP 527 Foundations of Data Science II for Spring 2020.
- 9 credits in Foundations of Program Evaluation (3 course sequence)
- 9 credits in Foundations of Data Science (3 course sequence)
- 3 credits of Systems and Theories of Program Evaluation (1 course)
- 3 credits of applied data project (1 course)
- 6 credits of approved electives (2 courses)
- 3 credits of a 15-week capstone class (1 course)
Online courses are 7.5 weeks long and organized as two sessions (A and B) each semester. A full-time student could complete the program by taking courses in this order:
- Overview of the field of quantitative program evaluation
- Program impact as effect size
- Standard errors, confidence intervals, and hypothesis testing
- Multiple regression models
- Control variables and omitted variable bias
- Hypothesis testing using regression
- Measurement error and statistical power
- Counterfactual analysis
- Outcomes and measurement
- Three common counterfactuals (equivalent groups, reflexive, and synthetic)
- Average treatment effects (treatment on treated, intention to treat)
- True experiments
- Quasi-experiments
- Internal validity and competing hypotheses (Campbell Scores)
- Fixed Effects Models
- Instrumental Variables
- Matching
- Regression Discontinuity
- Difference-in-Difference
- Time Series
- Logistic Regression
- Overview of the field of data driven management
- Functions and arguments
- Data structures
- Data import / export
- Logical arguments and groups
- Subsets and merges
- Descriptive statistics, with groups
- Visualization, graphs, and maps
- Basic control structures and programming
- Building automated reports
- Advanced data structures
- Tidy data and tidyverse
- Advanced data wrangling
- Regular expressions and text analysis
- Data APIs
- Advanced markdown formats
- Animations
- Open science and reproducibility
- Open data movement and standards
- The agile framework for team management
- Building functions and packages
- GitHub pages
- Report methodology and results in professional format
- Building Packages
- Project management foundations
- Import data from several sources including APIs
- Aggregate all data to proper units of analysis
- Combine data into single research database
- Create a data dictionary
- Conduct analysis using Program Eval tools