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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