Course

Unit I

Data Science Process & Data Handling in R 

• Introduction to the Data Science lifecycle with a real-world case study. 
• Loading datasets in R using read.csv(), read.table(), readxl, and data() functions. 
• Exploratory Data Analysis (EDA) using summary statistics and basic plots. 
• Data visualization using ggplot2 for univariate and bivariate analysis. 
• Data cleaning and preprocessing: handling missing values, outliers, and data transformation. 
• Data management in R: data frames, merging datasets, subsetting, and reshaping data. 

Unit II

Modeling Methods 

• Implement Supervised Learning models: 
  • Linear Regression
  • Logistic Regression
• Implement Classification algorithms: 
  • Decision Trees
  • k-Nearest Neighbors (k-NN)
• Implement Unsupervised Learning techniques: 
  • k-Means clustering
  • Hierarchical clustering
• Implement Ensemble Models: 
  • Random Forest
  • Boosting techniques
• Model evaluation and comparison using accuracy, precision, recall, RMSE, and cross-validation.

Unit III

Delivering Results & Visualization 

• Create effective visualizations using ggplot2, lattice, and base R graphics. 
• Generate statistical summaries and dashboards using R Markdown. 
• Document and deploy data science results using reports and presentations. 
• Mini Project: End-to-end data science workflow—from data loading to model building and result reporting. 

Course Description  

The course is intended to develop the student’s knowledge and abilities of how R programming can be used for data analysis and visualization. This course is also intended to get the idea of how it can be applied in various machine learning tasks. 

 Course Objectives 

• The main objective is to provide information on R studio environment.
• It focuses on the basic commands and its syntax. 
• Focuses on how to do exploratory data analysis using R programming 
• Apply R programming on various ML models and its performance evaluation. 

Course Outcomes 

CO-PO Mapping 

• “R for Data Science”, Hadley Wickham and Garett Grolemund, , O’Reilly, 2017
• “Data Mining for Business Analytics: Concepts, Techniques and Applications in R”, GalitShmueli, et al, Wiley India, 2018.
• “Practical Data Science with R”, Nina Zumel and John Mount, Dreamtech/Manning, 2014