Under Development: Introduction to Critical Data Science (shared course)

Coming in 2019 …

Introduction to Critical Data Science (co-taught course, shared digitally)

Learning Objectives:

  1. Familiarity and expertise in basic coding (R or python, Excel).
  2. Understanding of theory and application of basic concepts in statistics.
  3. Ability to write and present technical material to diverse audiences.

Course Sequence:

  • 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
  • Algorithms
  • 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.