This course provides a foundation in programming for analytics using the R programming language with a comparison to Python. Topics covered include fundamentals of programming (operators, data types, objects, functions, conditionals, loops, strings, testing, and debugging) as well as techniques for working with data sets in R and Python (file input/output, data structures, data wrangling, data visualization, external packages, and reproducible reporting). Emphasis will be on producing clear, robust, and reasonably efficient code using top-down design, informal analysis, and effective testing and debugging. Students will primarily work on individual programming assignments to develop skills in computational problem solving, writing code, and working with data. Students will be assessed through quizzes, homework assignments, and exams. Teaching will involve interactive lectures with plenty of time spent live coding and working on practice problems in class. This course assumes no prior programming experience and is an ideal preparation for higher level courses in data analytics.

This course was inspired by many other courses / resources that cover similar material. In addition to my own developed material, I have also modified material from other courses and workshops - a full list of sources can be found in the LICENSE page.

This course was originally created by Dr. John Paul Helveston at the George Washington University for the Spring 2020 semester.