✅ Job-Ready Program | Placement Assistance

R Programming for Data Analysis & Data Science
Industry-Level Syllabus

Master R from basics to machine learning, data visualization, and interactive dashboards. Build real-world projects, clean messy data, and become a certified Data Analyst / Junior Data Scientist.

12 Comprehensive Modules 4 Real-Time Projects dplyr + ggplot2 + Shiny R Markdown Reporting
12
Expert Modules
60%+
Hands-on Practicals
4+
Capstone Projects
100%
Job Assistance
📘 Core Syllabus (Job-Oriented)
Module 01
Introduction to R
  • What is R & industry use (Finance, Healthcare, Retail)
  • Installation of R and RStudio
  • RStudio interface (Console, Script, Environment)
  • Basic syntax, variables, data types & operators
Practical: First R script & calculations
Module 02
Data Structures in R
  • Vectors: creation, indexing, operations
  • Lists, Matrices & Data Frames
  • Factors (categorical data)
Practical: Create & manage student dataset
Module 03
Control Statements & Functions
  • Conditional: if, else, nested if
  • Loops: for, while, break, next
  • Writing custom functions
  • Apply family: apply, lapply, sapply
Practical: Salary calculation system
Module 04
Data Import & Export
  • Import CSV, Excel, Text files
  • Reading JSON data
  • Export data to multiple formats
  • Working with large datasets (data.table basics)
Practical: Load & save real-world datasets
Module 05
Data Cleaning & Manipulation
  • Handling missing values (NA, imputation)
  • Removing duplicates, data transformation
  • Filtering, sorting, grouping & summarizing
  • Using dplyr and tidyverse (mutate, select, filter, summarise)
Practical: Clean messy dataset for analysis
Module 06
Data Visualization
  • Base R plotting (plot, hist, barplot)
  • ggplot2 introduction & grammar of graphics
  • Bar chart, line chart, histogram, scatter plots
  • Customizing charts: colors, labels, themes
Practical: Sales & performance reports
Module 07
Statistics for Data Analysis
  • Mean, median, mode, variance, SD
  • Probability basics & distributions
  • Correlation analysis (Pearson, Spearman)
  • Hypothesis testing (t-test, chi-square)
Practical: Analyze business data trends
Module 08
Machine Learning Basics in R
  • Intro to ML – supervised vs unsupervised
  • Linear regression & logistic regression
  • Classification models (decision trees, k-NN)
  • Model evaluation: accuracy, confusion matrix
Practical: Predict outcomes using dataset
Module 09
Database Integration
  • Connecting R with MySQL / SQLite
  • Running SQL queries from R (dplyr + DBI)
  • Data extraction and analysis from DB
Practical: Work with database-driven datasets
Module 10
Reporting & Dashboard
  • R Markdown (PDF, HTML, Word reports)
  • Data storytelling and reproducible research
  • Introduction to Shiny – interactive dashboards
  • Creating simple dashboards with Shiny
Practical: Build interactive report/dashboard
Module 11
Real-Time Projects
  • Sales data analysis project (trends & KPIs)
  • Customer segmentation using clustering
  • Financial analysis & forecasting
  • Trend analysis with time series
Portfolio ready projects
Module 12
Job Preparation
  • Resume building for Data Analyst profile
  • Interview Q&A: R + SQL + Statistics
  • GitHub project portfolio setup
  • Case study practice & mock interviews
🛠️ Tools & Libraries Covered
R & RStudio Excel / CSV SQL / MySQL dplyr & tidyverse ggplot2 caret (ML) R Markdown Shiny
🎯 Final Outcome (Job-Ready Skills)

What you'll achieve

  • ✅ Perform end-to-end data analysis using R
  • ✅ Clean, manipulate & visualize data professionally
  • ✅ Build basic machine learning models & evaluate
  • ✅ Create interactive dashboards (Shiny) & reports (R Markdown)

Job Roles After Course

Data AnalystJunior Data ScientistBusiness AnalystAnalytics AssociateR Programmer

Bonus (Job-Ready Edge)

✨ Live case studies • GitHub Portfolio • Resume Building • Mock Interviews • 100% Placement Assistance

Hands-on training with real datasets | Flexible batches | Industry expert mentors

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