Prerequisites

• Basic computer literacy
• Fundamental mathematics knowledge
• Spreadsheet software familiarity
• Optional: Basic programming experience
• Computer with statistical software installation capabilities
• Recommended: Basic statistical understanding

8-Week R Programming Course Syllabus

Week 1: R Programming Fundamentals

• R environment installation
• Basic data types and structures
• R Studio configuration
• Basic syntax and programming concepts
• Variable assignment
• Basic arithmetic operations
• Introduction to R packages

Week 2: Data Manipulation

• Vectors and data frames
• Data importing techniques
• Data cleaning methods
• Basic statistical functions
• Data transformation
• Missing value handling
• Subsetting and indexing

Week 3: Control Structures and Functions

• Conditional statements (if-else)
• Loops (for, while)
• Function creation
• Scoping rules
• Error handling
• Custom function development
• Debugging techniques

Week 4: Data Visualization

• Base graphics
• ggplot2 library
• Statistical plotting
• Visualization techniques
• Chart customization
• Interactive graphics
• Reporting visualization

Week 5: Statistical Analysis

• Descriptive statistics
• Hypothesis testing
• Regression analysis
• Probability distributions
• Statistical modeling
• Parametric and non-parametric tests
• Data interpretation

Week 6: Advanced Data Manipulation

• Tidyverse techniques
• Data reshaping
• Advanced filtering
• Aggregation methods
• Complex data transformations
• Performance optimization
• Large dataset handling

Week 7: Machine Learning Basics

• Predictive modeling
• Classification techniques
• Clustering algorithms
• Model evaluation
• Cross-validation
• Supervised learning
• Unsupervised learning techniques

Week 8: Practical Applications and Project

• Real-world data analysis
• End-to-end project development
• Industry case studies
• Best practices
• Professional reporting
• Portfolio creation
• Career guidance

Learning Outcomes

• Comprehensive R programming skills
• Advanced data manipulation expertise
• Statistical analysis capabilities
• Professional visualization techniques
• Machine learning fundamentals
• Practical implementation knowledge
• Industry-ready data science skills

Certification

Students receive a comprehensive R Programming certification upon successful course completion.

Note: Course content may be dynamically updated to reflect emerging technological trends.