Coming in 2019 …
Introduction to Critical Data Science (co-taught course, shared digitally)
- Familiarity and expertise in basic coding (R or python, Excel).
- Understanding of theory and application of basic concepts in statistics.
- Ability to write and present technical material to diverse audiences.
- Intensive 8-week course with data lab component (fully digital)
- Student centered learning design including pre-recorded lectures, real-time lectures, and laboratory/supported work time
- Course to be co-taught by 4-5 instructors (one from each participating campus)
- Delivery is fully online with some scheduled and some asynchronous events.
Level: This class is intended for non-majors. There are no formal prerequisites; preference will be given for students with no prior coding experience; preference will be given to students who have taken college-level calculus. Enrollment must be approved by the student’s advisor at their home institution and by a lead course instructor.
Course Team: tba
Course Topics May Include*:
- What are data? What is data science?
- Data science and society; ethical issues in data science
- Simulating problems
- Developing theories with data
- Data visualization (using ggplot or other R pkg) and presentation (semi-log and log-log plots)
- Data processing
- Linear regression (MoLS)
- Mapping geospatial data
- Data transformation: Filter, arrange, select, summarize, mutate & group
- Exploratory data analysis: Examining variation, addressing missing values, covariation, patterns and models
- Social network analysis
- Data frames, Tibbles, and tidy data
- Relational data and Functions
- Vectors and Iteration
- Data Modeling
- Basic coding (working directories, reading input and saving output, running program piecemeal vs all at once, commenting, variable naming)
- Introduction to statistics
- Presenting analyses of data (for example, LaTeX, Powerpoint, Tableau, R Markdown)
* A menu of topics and materials will be developed over time for each course offering to draw on.