R Programming for Data Science Certification

Module 1: Introduction to R for Data Science

• R programming overview
• Data science applications
• R ecosystem
• RStudio environment
• Analytical workflow
• R packages

Module 2: R Programming Fundamentals

• Variables
• Data types
• Operators
• Expressions
• Functions
• Basic syntax

Module 3: R Data Structures

• Vectors
• Lists
• Matrices
• Arrays
• Factors
• Data frames

Module 4: Control Flow & Functions

• Conditional statements
• For loops
• While loops
• Apply functions
• Custom functions
• Functional programming concepts

Module 5: Importing & Exporting Data

• CSV files
• Excel data
• Text files
• JSON concepts
• Database data
• Exporting results

Module 6: Data Manipulation with dplyr

• select()
• filter()
• arrange()
• mutate()
• summarise()
• Data manipulation pipelines

Module 7: Data Transformation with tidyr

• Tidy data principles
• pivot_longer()
• pivot_wider()
• Separating columns
• Combining columns
• Reshaping datasets

Module 8: Data Cleaning

• Missing values
• Duplicate records
• Incorrect values
• Data type conversion
• String cleaning
• Data validation

Module 9: Exploratory Data Analysis

• Dataset profiling
• Summary statistics
• Distribution analysis
• Relationship analysis
• Pattern identification
• Insight generation

Module 10: Data Visualization with ggplot2

• Grammar of graphics
• ggplot()
• Aesthetics
• Geometries
• Labels
• Themes

Module 11: Advanced Data Visualization

• Histograms
• Box plots
• Scatter plots
• Faceting
• Multi-variable visualization
• Data storytelling

Module 12: Statistics with R

• Descriptive statistics
• Probability concepts
• Statistical distributions
• Sampling
• Variability
• Statistical interpretation

Module 13: Hypothesis Testing

• Null hypotheses
• Alternative hypotheses
• P-values
• T-tests
• Chi-square tests
• Statistical significance

Module 14: Correlation & Regression

• Correlation analysis
• Simple linear regression
• Multiple regression
• Model coefficients
• Predictions
• Regression diagnostics

Module 15: Classification with R

• Classification concepts
• Logistic regression
• Decision trees
• Probability predictions
• Classification metrics
• Model comparison

Module 16: Feature Engineering

• Feature creation
• Categorical variables
• Scaling
• Transformation
• Feature selection
• Dataset preparation

Module 17: Machine Learning with R

• Machine learning workflow
• Training datasets
• Testing datasets
• Model fitting
• Predictions
• Model selection

Module 18: Clustering & Segmentation

• Unsupervised learning
• K-Means
• Hierarchical clustering
• Cluster evaluation
• Segmentation
• Cluster visualization

Module 19: Time-Series Analysis

• Time-series data
• Trends
• Seasonality
• Moving averages
• Forecasting concepts
• Time-series visualization

Module 20: Model Evaluation & Optimization

• Accuracy
• Precision
• Recall
• F1 score
• Cross-validation
• Hyperparameter concepts

Module 21: Reproducible Data Science with R

• R scripts
• R Markdown concepts
• Project organization
• Package management
• Reproducibility
• Analytical reporting

Module 22: Practical R Data Science Project

• Dataset preparation
• Data cleaning
• Exploratory analysis
• Visualization
• Model development
• Results presentation