cpp-527-spr-2020

Course shell for CPP 527 Foundations of Data Science II for Spring 2020.

Program Requirements:

10 courses + 1 capstone semester = 33 credits

• 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)

Core Courses

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:

Program Evaluation Core

CPP 523 Foundations of Program Evaluation I: Multiple Regression & Hypothesis Testing

• 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

Cpp 524 Foundations of Program Evaluation II: Research Design

• 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)

CPP 525 Foundations of Program Evaluation III: Advanced Regression Tools

• Fixed Effects Models
• Instrumental Variables
• Matching
• Regression Discontinuity
• Difference-in-Difference
• Time Series
• Logistic Regression

Data Science Core

CPP 526 Foundations of Data Science I: R Programming

• 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

CPP 527 Foundations of Data Science II: Data Wrangling

• Advanced data structures
• Tidy data and tidyverse
• Advanced data wrangling
• Regular expressions and text analysis
• Data APIs
• Advanced markdown formats
• Animations

CPP 528 Foundations of Data Science III: Project Management & Collaboration

• 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-Based Course

CPP 529 Data Practicum: Data-Driven Models of Community Change

• 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