Module 1 – Introduction to Data Science

• Introduction to Data Science
• What is Data Science?
• Data Science lifecycle
• Role of a Data Scientist
• Data Science vs Data Analytics
• Types of data
• Real-world applications of Data Science
• Data Science workflow

Module 2 – Introduction to R Programming

• Introduction to R
• Features and applications of R
• Installing R and RStudio
• RStudio interface
• R scripts and console
• Variables and constants
• Basic R syntax
• Comments and coding practices

Module 3 – R Programming Fundamentals

• Data types in R
• Numeric and character data
• Logical values
• Operators
• Variables and assignments
• Input and output
• Basic mathematical operations
• Type conversion

Module 4 – Data Structures in R

• Vectors
• Lists
• Matrices
• Arrays
• Factors
• Data Frames
• Creating and accessing data structures
• Data manipulation using R

Module 5 – Control Statements and Functions

• if and else statements
• Nested conditions
• for loops
• while loops
• break and next
• Creating functions
• Function arguments
• Return values
• User-defined functions

Module 6 – Data Import and Export

• Importing CSV files
• Reading Excel data
• Importing text files
• Working with JSON data
• Exporting data
• Connecting to databases
• Handling large datasets
• Data input/output practices

Module 7 – Data Cleaning and Preprocessing

• Introduction to data cleaning
• Missing values
• Duplicate records
• Handling incorrect data
• Data type conversion
• Outlier identification
• Data transformation
• Data filtering
• Preparing clean datasets

Module 8 – Data Manipulation Using R

• Selecting rows and columns
• Filtering data
• Sorting data
• Creating new columns
• Grouping data
• Aggregating data
• Merging datasets
• Reshaping data
• Introduction to dplyr and tidyr

Module 9 – Exploratory Data Analysis

• Introduction to EDA
• Understanding datasets
• Descriptive statistics
• Mean, median and mode
• Variance and standard deviation
• Correlation
• Data distributions
• Identifying trends and patterns

Module 10 – Data Visualization with R

• Introduction to data visualization
• Base R graphics
• Bar charts
• Histograms
• Pie charts
• Scatter plots
• Box plots
• Line charts
• Introduction to ggplot2
• Creating professional visualizations

Module 11 – Statistics for Data Science

• Statistical fundamentals
• Probability concepts
• Sampling
• Population and sample
• Normal distribution
• Correlation and covariance
• Hypothesis testing
• Confidence intervals
• Statistical interpretation

Module 12 – Machine Learning with R

• Introduction to Machine Learning
• Machine Learning workflow
• Training and testing datasets
• Supervised learning
• Unsupervised learning
• Features and target variables
• Model training
• Prediction and evaluation

Module 13 – Regression Analysis

• Introduction to regression
• Simple Linear Regression
• Multiple Linear Regression
• Model building in R
• Prediction using regression
• Regression evaluation
• Residual analysis
• Practical regression project

Module 14 – Classification Techniques

• Introduction to classification
• Logistic Regression
• Decision Trees
• K-Nearest Neighbors
• Naive Bayes concepts
• Random Forest
• Model training and prediction
• Classification evaluation

Module 15 – Clustering and Unsupervised Learning

• Introduction to clustering
• K-Means clustering
• Hierarchical clustering
• Cluster analysis
• Selecting the number of clusters
• Visualizing clusters
• Customer segmentation
• Practical clustering applications

Module 16 – Model Evaluation and Optimization

• Training and validation
• Accuracy
• Precision
• Recall
• F1-score
• Confusion matrix
• Mean Squared Error
• Cross-validation
• Model comparison
• Improving model performance

Module 17 – Advanced Data Science with R

• Working with large datasets
• Advanced data manipulation
• Advanced visualization
• Time-series data concepts
• Basic predictive analytics
• Data reporting
• Automated analysis
• Reproducible data analysis

Module 18 – R Packages and Data Science Tools

• Installing and managing R packages
• Tidyverse
• dplyr
• tidyr
• ggplot2
• readr
• lubridate
• caret concepts
• Package-based Data Science workflow

Module 19 – Practical Data Science Projects

• Sales data analysis
• Customer data analysis
• Student performance analysis
• House price prediction
• Customer segmentation
• Sales forecasting
• Classification project
• Data visualization dashboard
• Business data analysis project

Module 20 – Final Data Science Project & Assessment

• Project problem identification
• Data collection
• Data cleaning and preprocessing
• Exploratory data analysis
• Visualization
• Statistical analysis
• Machine Learning model development
• Model evaluation
• Project report preparation
• Final project presentation and assessment