Topic: Recordings | EES1137 Quantitative Applications for Data Analysis (Winter 2025)

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Topic outline

- Select activity 1. Introduction to Linux command line I
  
  1. Introduction to Linux command line I File
- Select activity 2. Introduction to Linux command line II
  
  2. Introduction to Linux command line II File
- Select activity 3. Introduction to R
  
  3. Introduction to R File
- Select activity 4. Vectors and data frames
  
  4. Vectors and data frames File
- Select activity 5. Lists, loops and conditional statements in R
  
  5. Lists, loops and conditional statements in R File
- Select activity 6. Functions in R
  
  6. Functions in R File
- Select activity 7. Scripts
  
  7. Scripts File
- Select activity 8. Version control
  
  8. Version control File
- Select activity 9. Coding Best Practices
  
  9. Coding Best Practices File
- Select activity 10. Distributions
  
  10. Distributions File
- Select activity 11. Hypothesis testing
  
  11. Hypothesis testing File
- Select activity 12. Linear models
  
  12. Linear models File
- Select activity 13. Visualization in R
  
  13. Visualization in R File
- Select activity 14. Introduction to Python
  
  14. Introduction to Python File
- Select activity 15. Loops, conditions and functions in Python
  
  15. Loops, conditions and functions in Python File
- Select activity 16. NumPy, SciPy and Visualization
  
  16. NumPy, SciPy and Visualization File
- Select activity 17. Introduction to machine learning
  
  17. Introduction to machine learning File
- Select activity 18. Resampling
  
  18. Resampling File
- Select activity 19. Classification II
  
  19. Classification II File
- Select activity 20. Unsupervised learning
  
  20. Unsupervised learning File
- Select activity 21. Monte Carlo methods
  
  21. Monte Carlo methods File
- Select activity 22. High-Performance Computing in R
  
  22. High-Performance Computing in R File
- Select activity 23. Neural networks
  
  23. Neural networks File