Course Description
Multiple strategies for inferential reasoning about quantitative data. Methods for connecting data provenance to substantive analytical conclusions
Credit(s)
3.0
Professor of Record
Jeffrey Stanton
Audience
Learning Objectives
After this course, students will be able to:
Demonstrate knowledge of contemporary inferential statistical concepts (from the perspective of two contemporary philosophies) and data analysis strategies by making sensible choices about:
- How data collection, the data themselves, and the analysis processes relate to the kinds of inferences that can be drawn
- What kinds of analysis will be feasible and developing the skill of planning data collection and measurement to facilitate appropriate analysis
Practice effective data science analytics:
- Preparing data for analysis, including screening data, dealing with missing data, doing data transformations
- Testing assumptions that data must meet for analyses and inferences to be reasonable
- Interpreting data analysis results and outputs and communicating them to others using language that accurately describes uncertainty
- Leaving a documentation/provenance trail for other analysts to follow and reproduce your work
Demonstrate competence and/or mastery of the skills needed for use of a popular statistics and data management platform to conduct sound and reproducible analyses including:
- Installing R and R-studio, and creating readable code to conduct analyses
- Exploring the limitations of existing data sets and how their provenance influences what analyses to perform and what inferences to draw
- Choosing appropriate R procedures and configuring the relevant operational parameters
Course Syllabus
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