Course: EES1137 Quantitative Applications for Data Analysis (Winter 2024)

Topic outline

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- EES1137 Quantitative Applications for Data Analysis (Winter 2024)
  In this course data analysis techniques utilizing the Python and R languages will be introduced, as well as the basics of programming and scientific computing. The goal of this course is to prepare graduate students for performing scientific data analysis. Successful students will learn how to use statistical inference tools to gain insight into large and small data sets, as well as be exposed to cutting-edge techniques and best practises to store, manage and analyze (large) data. Topics include: Python and R programming, version control, automation, modular programming and scientific visualization.
  Students willing to take the course as part of their graduate program must enrol through Acorn/ROSI.
  Teachers: Alexey Fedoseev, Erik Spence
  Start date: 9 Jan 2024
  End date: 4 Apr 2024
  Scientific Computing Credits: 8
  Data Science Credits: 28
  Events:
  EES1137: Lecture 1 - Tuesday, 9 January, 10:00 AM » 12:00 PM
  EES1137: Lecture 2 - Thursday, 11 January, 11:00 AM » 12:00 PM
  EES1137: Lecture 3 - Tuesday, 16 January, 10:00 AM » 12:00 PM
  A full list of course events can be found here.
- Page
  
  Software Requirements Page
- Forum
  
  Student forum
Lectures
- File
  
  1. Linux command line I File
- File
  
  2. Linux command line II File
- File
  
  3. Introduction to R File
- File
  
  4. Vectors and data frames File
- File
  
  5. Lists, loops and conditional statements in R File
- File
  
  6. Functions in R File
- File
  
  7. Scripts File
- File
  
  8. Version Control System - Git File
- File
  
  9. Distributions File
- File
  
  10. Coding Best Practices File
- File
  
  11. Hypothesis tests File
- File
  
  12. Visualization in R File
- File
  
  13. Linear models File
- File
  
  14. Introduction to Python File
- File
  
  15. Loops and functions in Python File
- File
  
  16. NumPy, SciPy and Visualization File
- File
  
  17. Introduction to Machine Learning File
- File
  
  18. Resampling File
- File
  
  19. Classification II File
- File
  
  20. Feature selection File
- File
  
  21. Unsupervised learning File
- File
  
  22. Monte Carlo methods File
- File
  
  23. Neural networks File
- File
  
  24. Ensemble models File
Recordings
- File
  
  1. Linux command line I File
- File
  
  2. Linux command line II File
- File
  
  3. Introduction to R File
- File
  
  4. Vectors and data frames File
- File
  
  5. Loops and conditionals File
- File
  
  6. Functions File
- File
  
  7. Scripts File
- File
  
  8. Version control File
- File
  
  9. Distributions File
- File
  
  10. Coding Best Practices File
- File
  
  11. Hypothesis tests File
- File
  
  12. Visualization in R File
- File
  
  13. Linear models File
- File
  
  14. Introduction to Python File
- File
  
  15. Loops and functions in Python File
- File
  
  16. NumPy, SciPy and Visualization File
- File
  
  17. Introduction to Machine Learning File
- File
  
  18. Resampling File
- File
  
  19. Classification II File
- File
  
  20. Feature selection File
- File
  
  21. Unsupervised learning File
- File
  
  22. Monte Carlo methods File
- File
  
  23. Neural networks File
- File
  
  24. Ensemble models File
Assignments
- Assignment
  
  Assignment 1
  
  Opened: Tuesday, 16 January 2024, 11:00 AM
  
  Due: Tuesday, 23 January 2024, 11:59 PM
- Assignment
  
  Assignment 2
  
  Opened: Tuesday, 30 January 2024, 11:00 AM
  
  Due: Tuesday, 6 February 2024, 11:59 PM
- Assignment
  
  Assignment 3
  
  Opened: Tuesday, 6 February 2024, 11:00 AM
  
  Due: Tuesday, 13 February 2024, 11:59 PM
- Assignment
  
  Assignment 4
  
  Opened: Tuesday, 13 February 2024, 11:00 AM
  
  Due: Tuesday, 27 February 2024, 11:59 PM
- Assignment
  
  Assignment 5
  
  Opened: Tuesday, 27 February 2024, 11:00 AM
  
  Due: Tuesday, 5 March 2024, 11:59 PM
- Assignment
  
  Assignment 6
  
  Opened: Tuesday, 5 March 2024, 11:00 AM
  
  Due: Tuesday, 12 March 2024, 11:59 PM
- Assignment
  
  Assignment 7
  
  Opened: Tuesday, 12 March 2024, 11:00 AM
  
  Due: Tuesday, 19 March 2024, 11:59 PM
- Assignment
  
  Assignment 8
  
  Opened: Tuesday, 19 March 2024, 11:00 AM
  
  Due: Tuesday, 26 March 2024, 11:59 PM
- Assignment
  
  Assignment 9
  
  Opened: Tuesday, 26 March 2024, 11:00 AM
  
  Due: Tuesday, 2 April 2024, 11:59 PM
- Assignment
  
  Assignment 10
  
  Opened: Tuesday, 2 April 2024, 11:00 AM
  
  Due: Tuesday, 9 April 2024, 11:59 PM
- Assignment
  
  Assignment 11 - Makeup
  
  Opened: Thursday, 4 April 2024, 11:00 AM
  
  Due: Thursday, 11 April 2024, 11:59 PM

Topic outline

General

EES1137 Quantitative Applications for Data Analysis (Winter 2024)

Lectures

Recordings

Assignments