Data Analysis using R

Data analysis is a crucial skill in today's data-driven world, and R is a powerful programming language and environment widely used for statistical computing and graphics. This course aims to equip participants with the fundamental knowledge and practical skills necessary to perform data analysis tasks efficiently using R.

Objective/Purpose of the Course:

• Introduce participants to the basics of R programming language and environment.
• Teach participants how to import, manipulate, clean, and explore data using R.
• Familiarize participants with statistical analysis techniques and methods using R.
• Enable participants to create visualizations to effectively communicate insights from data.
• Provide hands-on experience with real-world datasets and practical data analysis for different domain like social science, biological science, finance, marketing etc.

Target Population / Learners / Candidates:  

• Pursuing/passed bachelor’s degree(honours or general) in science, social science, commerce who has a course in data analysis/statistics/programming in their curriculum.
• Pursuing/passed B. Tech/B.E in any discipline of engineering who has interests in the data analysis/statistics/programming.
• Pursuing/Passed master’s degree and want to build their career in data science/analytics.
• Research scholars who need data analysis to complete their research work.
• Any working professional who wants to sharpen their data analysis skill for current or future job role.
• Faculties who are engaged in teaching/research in similar fields.

Content:

• Introduction to R environments, Basic Syntax, Vector, Matrix, Sequence, Import-Export, Data Cleaning and pre-processing

• dplyr package and different verbs for data analysis, ggplot2 package and building graphs like Box Plot, Scatter Plot, Contour Plot etc, Building probability distribution in R.
  

• Hypothesis Test, Case study problems - t –test, anova, Post hoc analysis, chi-square, F-test etc and their implementation in R.
  
• Corelation and its use in R, Case study-Simpl;e Linear regression, Multiple Linear Regression and their implementation in R. Introduction to advance methods of statistical learning.

Duration:

• Classes will be twice in a week.
• Each Class Duration is of 2 hours.
• Total number of classes are 16(2 months)
• Total Course Hour 16X2 = 32 Hours