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Credit(s)

3.0

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This information can be pulled or linked directly from OneDrive (files that PoR saves there)Jeffrey Stanton

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

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