R Programming Syllabus

Module 01: Introduction to R Programming

  • The Importance of Statistics and Data Analysis
  • Future of Statistics
  • Interdisciplinary Applications
  • Features of R programming
  • Statistical Exploration and Visualization
  • Comparison between R and Python
  • R Advantages and Disadvantages
  • Career Opportunities

Module 02: R Programming for Data Science

  • Overview to R: History, Purpose, and Installing R and RStudio
  • Exploring RStudio Layout: Console, Script Editor, Environment, and Plots Panel
  • Basic Operations
  • Operators: Arithmetic, Relational, Logical, Assignment, Miscellaneous
  • Data Types: Integer, Numeric, Character, Logical, Complex
  • Data Structures: Vectors, Matrices, Arrays, Lists, Data Frames, Factors
  • Decision Making: If, Else, Switch Statements
  • Loops: Repeat, While, For Loops

Module 03: Data Input and Visualization

  • Data Import and Export: CSV, Excel, Web Data
  • Data Cleaning and Transformation
  • Basic Plotting: Scatter, Bar, Pie, Histograms, Line Plots
  • Introduction to Packages: ggplot2, dplyr, tidyr

Module 04: Introduction to Descriptive Statistics

  • Measures of Central Tendency: Mean, Median, Mode
  • Measures of Dispersion: Range, Variance, Standard Deviation

Module 05: Statistics with Correlation and Regression

  • Regression Analysis: Linear, Multiple, Logistic, Poisson
  • ANOVA (Analysis of Variance): One Way, Two Way
  • Hypothesis Testing: T-Tests
  • Covariance Matrix, Pearson Correlation
  • Normal Probability Plot, Q-Q Plots

Module 06: Advanced Data Visualization with ggplot2

  • Introduction to ggplot2 and Grammar of Graphics
  • Creating Advanced Plots: Scatter, Box, Violin, Heatmap, etc.

Module 07: Time Series Analysis

  • Understanding Time Series Data
  • Decomposition: Trend, Seasonality, Random
  • Forecasting Techniques: ARIMA Model, Moving Averages

Module 08: Machine Learning Models

  • Decision Tree
  • Random Forest
  • K-Means Clustering
  • Evaluation Metrics: Precision, Recall, F1-Score

Bonus: Multivariate Analysis

  • PCA (Principal Component Analysis)
  • Factor Analysis
  • Taylor Diagram