Professional Certificate in R Programming

The Professional Certificate in R Programming & Applied Data Analytics is a comprehensive 12-week (72-hour) live online program designed to turn beginners and professionals into proficient R programmers capable of solving real-world data problems. Delivered through live instructor-led training, 1:1 personalized mentorship, hands-on projects, and structured learning paths, this course provides a solid foundation in the R programming language and its practical application in modern data analytics.

This program — offered by Handson System, a leader in skill-based analytics education — covers everything from R fundamentals to advanced statistical analysis, data wrangling, visualization, functional programming, and scalable big-data techniques using R.

Throughout the course, learners engage with real-world datasets, perform end-to-end data analysis, and build fully functional R scripts to extract insights, create visualizations, and communicate findings effectively.

Key Learning Outcomes

By the end of the program, learners will:

• Gain complete mastery of the R programming language

• Apply data cleaning, preprocessing, wrangling, and manipulation techniques

• Perform exploratory data analysis (EDA) and generate statistical insights

• Conduct hypothesis testing, ANOVA, and regression modeling

• Build impactful visualizations using ggplot2

• Work with dplyr, tidyr, data.table, and modern R packages

• Handle large datasets and write efficient R programs

• Build real-world, end-to-end data analysis projects

Curriculum Overview

Module 1: Introduction to R Programming

• R & RStudio interface

• Basic syntax & data structures (vectors, lists, matrices, data frames)

• Data types, OOP concepts in R

• Built-in functions, operators, and workflows

Module 2: Data Manipulation with R

• Data import/export

• Data cleaning & preprocessing

• dplyr & tidyr for wrangling

• Working with dates & times

Module 3: Exploratory Data Analysis (EDA)

• Summary statistics, distribution analysis

• Correlations, scatter plots

• Histograms, density plots, box plots

Module 4: Statistical Analysis with R

• Hypothesis testing (t-test, chi-square)

• ANOVA

• Linear regression

• Non-parametric tests

Module 5: Data Visualization with ggplot2

• Basic plots (scatter, line, bar)

• Aesthetics & themes

• Faceting, layering, advanced visualizations

Module 6: Advanced R Programming

• Functional programming

• Big data manipulation (data.table)

• Writing scalable, efficient code

• Debugging & error-handling

Final Project: Real-World R Analytics Case Study

• Identify a dataset & problem statement

• Perform cleaning, EDA, modeling

• Create visualizations & present insights

• Deliver a complete analytics report

Program Features (as per your provided data)

• Live online instructor-led classes

• 1:1 personalized mentorship

• Hands-on projects & case studies

• Peer networking & group learning

• Self-paced video access

• Dedicated learning management support

• Placement assistance: resume review, job alerts, interview guidance

Industries learners come from include IT (50%), Healthcare (28%), BFSI (12%), and Consulting (10%).