R Programming for Statistics & Analytics

Overview

A hands-on 4-week program introducing learners to R programming for data analysis and statistics. The course focuses on practical applications of R for data wrangling, visualization, and statistical modeling, preparing participants for both academic and industry use cases.

Introduction to R and RStudio
Data import, wrangling, and cleaning with R
Descriptive statistics and data exploration
Data visualization using ggplot2
Inferential statistics (t-tests, correlation, chi-square)
Regression and basic predictive modeling
Reporting and reproducible analysis with R Markdown

Week 1: Introduction & Data Handling

Setting up R and RStudio
Data types, structures, and basic commands
Importing and cleaning datasets
Introduction to tidyverse

Week 2: Descriptive Statistics & Visualization

Summary statistics (mean, median, variance)
Data exploration with dplyr
Data visualization with ggplot2 (bar charts, scatterplots, histograms)

Week 3: Inferential Statistics

Hypothesis testing (t-tests, chi-square)
Correlation analysis
Simple data reporting in R

Week 4: Regression & Mini Project

Linear regression basics
Multiple regression introduction
R Markdown for reproducible reports

Final mini-project: analyze a dataset and present results

Level:        Beginner
Duration:     4 - 6 weeks
Fee:

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R Programming for Statistics & Analytics

Week 1: Introduction & Data Handling

Week 2: Descriptive Statistics & Visualization

Week 3: Inferential Statistics

Week 4: Regression & Mini Project

Vision Statement:

Mission Statement: