WHAT IS R?

R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

Why R

R allows practicing a wide variety of statistical and graphical techniques like linear and nonlinear modeling, time-series analysis, classification, classical statistical tests, clustering, etc. R is a highly extensible and easy to learn language and fosters an environment for statistical computing and graphics

WHO

Today, millions of analysts, researchers, and brands such as Facebook, Google, Bing, Accenture, and Wipro are using R to solve complex issues. The applications of R are not limited to just one sector; we can see the use of R in banking, e-commerce, finance, and many more sectors. 

EXPECTATION

Write down your expectations and email us.

 

OBJECTIVES

  • Master the use of the R and RStudio interactive environment
  • Expand R by installing R packages
  • Read Structured Data into R from various sources
  • Understand the different data types in R
  • Understand the different data structures in R
  • Identify and deal with missing data
  • Manipulate strings in R
  • Understand basic regular expressions in R
  • Understand base R graphics
  • Focus on GGplot2 graphics for R for generating charts
  • Use R for descriptive statistics

 

DELIVERY AND ASSESMENT

Online or blended 

FEES AND PAYMENT MODE

  • Pay pal 
  • Mobile money
  • EFT 
  • Direct deposit to bank account 

Fees 

Account details 

Eco bank Forest Mall Kampala Account details are 

  • Account name: East African Statistics Institute
  • Account number: 7247500339

R!Camp I : Fundamentals  Of Data Analysis 

  1. Installation and set up 
  2. R data types
  3. Importing and exporting 
  4. Data management skills 
  5. Descriptive statistical analysis 
  6. Simple linear regression 
  7. Multiple linear regression 
  8. Data visualization 
  9. Time series analysis 
  10. Simulation 

R!Camp II : Advanced Data Analysis       

  1. Integrated documentation 
  2. Data wrangling 
  3. Effective visualization 
  4. Dynamic modeling