Training on R Programming for Data Science

Module 1: Introduction to R Programming

• Overview of R and its ecosystem
• Installation and setup of R and RStudio
• Basic R syntax and data types
• Introduction to R packages

Module 2: Data Manipulation with dplyr

• Importing and exporting data
• Data cleaning and preparation
• Using dplyr for data manipulation
• Filtering, selecting, and summarizing data

Module 3: Data Visualization with ggplot2

• Introduction to data visualization principles
• Creating static visualizations with ggplot2
• Customizing plots (colors, labels, themes)
• Creating multi-layered visualizations

Module 4: Exploratory Data Analysis (EDA)

• Principles of exploratory data analysis
• Using R to explore data distributions and relationships
• Identifying trends and outliers
• Documenting and interpreting findings

Module 5: Statistical Analysis

• Introduction to descriptive and inferential statistics
• Hypothesis testing and confidence intervals
• Using R for t-tests, chi-squared tests, and ANOVA
• Practical applications of statistical analysis

Module 6: Introduction to Machine Learning

• Overview of machine learning concepts
• Types of machine learning: supervised vs. unsupervised
• Building a simple linear regression model in R
• Evaluating model performance

Module 7: Advanced Machine Learning Techniques

• Introduction to classification algorithms (e.g., logistic regression, decision trees)
• Model evaluation techniques (confusion matrix, ROC curves)
• Implementing models using the caret package
• Hands-on project: Building a classification model

Module 8: Time Series Analysis

• Introduction to time series data and its characteristics
• Time series decomposition and forecasting
• Using R for time series analysis
• Practical examples and applications

Module 9: Text Mining and Natural Language Processing (NLP)

• Overview of text mining and its applications
• Preprocessing text data in R
• Basic NLP techniques using R
• Hands-on project: Analyzing text data

Module 10: Capstone Project and Course Wrap-Up

• Hands-on project: Applying learned skills to a real-world dataset
• Presentations of group projects
• Course review and key takeaways
• Next steps for continued learning in R and data science