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Students will learn the basic fundamentals of coding, which will then evolve into learning methods for discovering patterns in data through the use of effective data visualization tools, exploratory data analysis, and modeling techniques. They will gain experience working with real-world messy data, and use data tidying and data wrangling techniques to make the data suitable for exploration. Students will be required to reformulate discipline-specific research questions into data science questions. An emphasis will be placed on creating reproducible and shareable work. The course will highlight the importance of and uses of version control software (GitHub) for individual and collaborative work. The course will focus on the R statistical computing language.

Typically offered in Fall and Spring