In Summer 2019 …
Introduction to Data Science (co-taught course, shared digitally)
Syllabus and FAQ: See course gateway
Learning Objectives:
- Familiarity and expertise in basic coding (R/RStudio).
- Understanding of theory and application of basic concepts in statistics.
- 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.
Course Team: see course gateway
Lightning Talk – Learn about this project in just 6.5 minutes!
Presented May 22, 2019 at the Bryn Mawr Blended Learning Conference
Course Topics 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)
