Statistical Analysis with R
( 25 Modules )

Module #1
Introduction to Statistical Analysis
Overview of statistical analysis, importance of R, and setting up R environment

Module #2
Data Types and Structures in R
Introduction to R data types, vectors, matrices, data frames, and lists

Module #3
Data Visualization
Introduction to data visualization in R, using plots and charts to explore data

Module #4
Descriptive Statistics
Measures of central tendency, variability, and data summarization

Module #5
Introduction to Probability
Basic concepts of probability, events, and probability distributions

Module #6
Discrete Probability Distributions
Bernoulli, Binomial, and Poisson distributions

Module #7
Continuous Probability Distributions
Uniform, Normal, and Exponential distributions

Module #8
Sampling Distributions
Sampling distributions, central limit theorem, and confidence intervals

Module #9
Hypothesis Testing
Introduction to hypothesis testing, types of tests, and test statistics

Module #10
Confidence Intervals
Construction and interpretation of confidence intervals

Module #11
Regression Analysis
Simple and multiple linear regression, coefficients, and inference

Module #12
Model Diagnostics
Assumptions of linear regression, residuals, and model validation

Module #13
Time Series Analysis
Introduction to time series, components, and forecasting methods

Module #14
Analysis of Variance (ANOVA)
One-way and two-way ANOVA, F-tests, and post-hoc analysis

Module #15
Non-Parametric Tests
Wilcoxon rank-sum test, Kruskal-Wallis test, and Friedman test

Module #16
Cluster Analysis
Introduction to cluster analysis, hierarchical and k-means clustering

Module #17
Principal Component Analysis (PCA)
Introduction to PCA, dimension reduction, and feature extraction

Module #18
Working with Large Datasets
Efficient data manipulation and analysis with large datasets

Module #19
Data Wrangling and Cleaning
Handling missing values, data transformation, and data normalization

Module #20
R Markdown and Reproducible Research
Creating reports and documents with R Markdown

Module #21
Data Visualization with ggplot2
Advanced data visualization with ggplot2

Module #22
Survival Analysis
Introduction to survival analysis, Kaplan-Meier estimator, and Cox regression

Module #23
Generalized Linear Models (GLMs)
Introduction to GLMs, logistic regression, and Poisson regression

Module #24
Machine Learning with R
Introduction to machine learning, k-NN, decision trees, and random forests

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Statistical Analysis with R career

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