Advanced Data Science Using R

Instructor-led 4-days

Course Description
This course builds advanced data science capability using R. You will work with real datasets, apply statistical modeling, build machine learning pipelines, and deploy results. Focus stays on practical workflows, reproducibility, and performance. Each module includes hands-on exercises to reinforce concepts.

Course Objectives

• Build advanced data pipelines in R 
• Apply statistical modeling and inference 
• Train and evaluate machine learning models 
• Work with large and complex datasets 
• Visualize and communicate insights clearly 
• Deploy reproducible data science workflows 

Key Takeaways

• You write efficient, production-ready R code 
• You handle messy, large-scale data 
• You build and validate predictive models 
• You automate workflows using modern R tools 
• You present results with clarity and impact 

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Module 1: Advanced R Programming Foundations

• Functional programming concepts 
• Vectorization and performance tuning
  Exercises 

1. Optimize loop vs vector code 
2. Build custom functions with error handling 
3. Benchmark performance 

Module 2: Data Wrangling at Scale

• dplyr advanced usage 
• data.table introduction
  Exercises 

4. Clean large dataset 
5. Join multiple tables 
6. Optimize memory usage 
7. Convert workflows between dplyr and data.table 

Module 3: Data Cleaning and Validation

• Handling missing and inconsistent data 
• Data validation pipelines
  Exercises 

8. Detect anomalies 
9. Impute missing values 
10. Build validation rules 

Module 4: Exploratory Data Analysis

• Pattern detection 
• Statistical summaries
  Exercises 

11. Generate summary reports 
12. Identify correlations 
13. Detect outliers 

Module 5: Advanced Data Visualization

• ggplot2 extensions 
• Interactive dashboards
  Exercises 

14. Build multi-layer plots 
15. Create interactive charts 
16. Design dashboard 

Module 6: Statistical Inference

• Hypothesis testing 
• Confidence intervals
  Exercises 

17. Run t-tests and ANOVA 
18. Build confidence intervals 
19. Interpret statistical output 

Module 7: Regression Modeling

• Linear and logistic regression 
• Model diagnostics
  Exercises 

20. Fit regression models 
21. Check assumptions 
22. Improve model fit 

Module 8: Feature Engineering

• Encoding and transformations 
• Dimensionality reduction
  Exercises 

23. Create new features 
24. Apply PCA 
25. Evaluate feature impact 

Module 9: Machine Learning Foundations

• Supervised learning workflows 
• Model evaluation metrics
  Exercises 

26. Train classification model 
27. Evaluate accuracy and recall 
28. Compare models 

Module 10: Advanced Machine Learning

• Random forest, gradient boosting 
• Hyperparameter tuning
  Exercises 

29. Train ensemble models 
30. Tune parameters 
31. Compare performance 

Module 11: Unsupervised Learning

• Clustering techniques 
• Association rules
  Exercises 

32. Perform k-means clustering 
33. Apply hierarchical clustering 
34. Generate association rules 

Module 12: Time Series Analysis

• Forecasting methods 
• Seasonal decomposition
  Exercises 

35. Build ARIMA model 
36. Forecast trends 
37. Evaluate predictions 

Module 13: Text Mining and NLP

• Text preprocessing 
• Sentiment analysis
  Exercises 

38. Clean text data 
39. Perform sentiment analysis 
40. Build document-term matrix 

Module 14: Big Data with R

• Spark integration 
• Parallel computing
  Exercises 

41. Connect to Spark 
42. Process large dataset 
43. Run parallel tasks 

Module 15: Reproducible Research

• R Markdown 
• Workflow automation
  Exercises 

44. Create reproducible report 
45. Automate analysis pipeline 
46. Version control integration 

Module 16: Deployment and Production

• APIs and Shiny apps 
• Model deployment
  Exercises 

47. Build Shiny app 
48. Deploy model API 
49. Monitor model performance 
50. Package reusable code 
51. End-to-end project deployment