This course provides a foundation in programming for analytics using the R programming language. Topics covered include fundamentals of the R programming language (operators, data types, objects, functions, conditionals, loops, strings, testing, debugging, Monte Carlo methods) as well as techniques for working with data in R (projects & directories, packages, file input/output, data structures, data wrangling, and data visualization). 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 help practice problem solving skills, coding skills, and data science skills. 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.
For more details, please see the course website.
This course was inspired by many other courses / resources that cover similar material - see the course about page for more details.