Introduction to Critical Data Science (shared course)

In Summer 2019 …

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

Syllabus and FAQ: See course gateway

Learning Objectives:

  1. Familiarity and expertise in basic coding (R/RStudio).
  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 co-taught by instructors from LACOL schools 
  • 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: see course gateway

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.