[su_tab title = “Description”]
In this course you will learn how to do programming in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
[su_tab title = “Program Structure”]
The course will cover the following material each week:
- Week 1: Overview of R, R data types and objects, reading and writing data
- Week 2: Control structures, functions, scoping rules, dates and times
- Week 3: Loop functions, debugging tools
- Week 4: Simulation, code profiling
3-5 hours per week
Part Time/Fulltime: – Part Time
Course Start Date: 31-Oct-2016
[su_tab title = “Eligibility”]
Some familiarity with programming concepts will be useful as well basic knowledge of statistical reasoning
[su_tab title = “Faculty”]
- Roger D. Peng: – Johns Hopkins University
- Jeff Leek: – Johns Hopkins University
- Brian Caffo: – Johns Hopkins University
[su_tab title = “Contact”]
[su_tab title = “AV Review”]
Probably one of the best courses to join, if you want to start learning R part time. The course is relatively light on weekly engagement (3 – 4 hours / week). The course provides good introduction to basics of R, reading and writing data from R, Control structures & functions in R along with loop and code profiling.
You will need to take up a more advanced course before you start building your predictive models and perform your visualizations on R.