Programming using R
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Programming using R


R programming language is popular among statisticians and data miners for developing statistical software and data analysis. R Training has picked up a lot of popularity in recent years and there is a lot of demand for R Language Training.

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 structures in R
  • Understand how to create and manipulate dates in R
  • Use the tidyverse collection of packages to manipulate dataframes
  • Write user-defined R functions
  • Write Loop constructs in R
  • Use the apply family of functions to iterate functions across data
  • Expand iteration and programming through the Purrr package
  • Reshape data from long to wide and back to support different analyses
  • Perform merge operations with R
  • Understand split-apply-combine (group-wise operations) in R
  • Identify and deal with missing data
  • Manipulate strings in R
  • Understand basic regular expressions in R
  • Understand base R graphics

Target Audience

  • Ideal for graduates with 0 – 3 years of experience & degrees in B. Tech, B.E and B.Sc. IT Or Any Computer-Related program

Prerequisites

  • Knowledge of statistics theory in mathematics.
  • You should have solid understanding of statistics in mathematics.
  • Understanding of various type of graphs for data representation.
  • Prior knowledge of any programming.

Duration

  • If you have experience in any programming language, it takes 7 days to learn R programming spending at least 3 hours a day. If you are a beginner, it takes 3 weeks to learn R programming. In the second week, learn concepts like how to create, append, subset datasets, lists, join.

Course Outline

Module 1: Introduction to Data Analytics

Objectives:

  • This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data and Information.
  • ou can also learn how R can play an important role in solving complex analytical problems.
  • This module tells you what is R and how it is used by the giants like Google, Facebook etc.
  • Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.

Topics:

  • Business Analytics, Data, Information
  • Understanding Business Analytics and R
  • Compare R with other software in analytics
  • Install R
  • Perform basic operations in R using command line
  • Learn the use of IDE R Studio
  • Use the ‘R help’ feature in R

Module 2: Introduction to R programming

Objectives:

  • This module starts from the basics of R programming like datatypes and functions.
  • In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
  • You will also learn how to apply the ‘join’ function in SQL.

Topics:

  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors

Module 3: Data Manipulation in R

Objectives:

  • In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
  • Thus using and exploring the popular functions required to clean data in R.

Topics:

  • Data sorting
  • Find and remove duplicates record
  • Cleaning data
  • Recoding data
  • Merging data
  • Slicing of Data
  • Merging Data
  • Apply functions

Module 4: Data Import techniques in R

Objectives:

  • This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
  • This module teaches you various data importing techniques in R.

Topics:

  • Reading Data
  • Writing Data
  • Basic SQL queries in R
  • Web Scraping

Module 5: Exploratory Data Analysis

Objectives:

  • In this module, you will learn that exploratory data analysis is an important step in the analysis.
  • EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.

Topics:

  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing graphs

Module 6: Overview of Machine Learning techniques

Objectives:

  • This module touches the base Statistics, Machine learning techniques used in the Industry and will cover case studies.

Topics:

  • Standard deviation
  • Outlier
  • Linear regression
  • Multiple regression
  • Logistic regressions
  • Correlation

Module 7: Project Work

  • 1 Real-time project

Accreditations

 

Contact Information

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P. O. Box 21857 - 00100 Nairobi

Mob: +254 780 342 333, +254 202 246145, 2246154 

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