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[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.

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[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

Duration:

4 weeks

3-5 hours per week

Part Time/Fulltime: – Part Time

Important Dates:

Course Start Date: 31-Oct-2016
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[su_tab title = “Eligibility”]

Some familiarity with programming concepts will be useful as well basic knowledge of statistical reasoning

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[su_tab title = “Faculty”]

• Roger D. Peng: – Johns Hopkins University
• Jeff Leek: – Johns Hopkins University
• Brian Caffo: – Johns Hopkins University

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[su_tab title = “Contact”]

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[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.

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