Course: EES1137 Quantitative Applications for Data Analysis (Winter 2023)

Topic outline

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- EES1137 Quantitative Applications for Data Analysis (Winter 2023)
  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: 10 Jan 2023
  End date: 6 Apr 2023
  Scientific Computing Credits: 8
  Data Science Credits: 28
  Events:
  EES1137 Lecture 1 - Tuesday, 10 January, 10:30 AM » 12:00 PM
  EES1137 Lecture 2 - Thursday, 12 January, 11:00 AM » 12:30 PM
  EES1137 Lecture 3 - Tuesday, 17 January, 10:30 AM » 12:00 PM
  A full list of course events can be found here.
- Forum
  
  Announcements Forum
- Page
  
  Software Requirements Page
- Forum
  
  Student forum
Lectures
- File
  
  1. Introduction to Linux Shell I File
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  2. Introduction to Linux Shell II File
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  3. Introduction to R File
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  4. Vectors and data frames File
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  5. Lists, loops and conditional statements in R File
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  6. Functions in R File
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  7. Scripts File
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  8. Version Control System - Git File
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  9. Coding Best Practices File
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  10. Distributions File
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  11. Hypothesis tests File
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  12. Linear models File
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  13. Visualization in R File
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  14. Introduction to Python File
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  15. Loops and functions in Python File
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  16. NumPy, SciPy and Visualization File
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  17. Introduction to Machine Learning File
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  18. Resampling File
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  19. Monte Carlo methods File
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  20. Classification File
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  21. Unsupervised learning File
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  22. Feature selection File
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  23. Neural networks File
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  24. Ensemble methods File
Recordings
- File
  
  1. Introduction to Linux Shell I File
- File
  
  2. Introduction to Linux Shell 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. Coding best practices File
- File
  
  10. Distributions File
- File
  
  11. Hypothesis tests File
- File
  
  12. Linear models File
- File
  
  13. Visualization File
- File
  
  14. Introduction to Python File
- File
  
  15. Loops and functions in Python File
- File
  
  16. Numpy, Scipy and Visualization File
- File
  
  17. Machine learning File
- File
  
  18. Resampling File
- File
  
  19. Monte Carlo methods File
- File
  
  20. Classification File
- File
  
  21. Unsupervised learning File
- File
  
  22. Feature selection File
- File
  
  23. Neural networks File
- File
  
  24. Ensemble methods File
Assignments
- Assignment
  
  Assignment 1
  
  Opened: Thursday, 12 January 2023, 12:00 PM
  
  Due: Thursday, 19 January 2023, 11:59 PM
- Assignment
  
  Assignment 2
  
  Opened: Thursday, 19 January 2023, 12:00 PM
  
  Due: Thursday, 26 January 2023, 11:59 PM
- Assignment
  
  Assignment 3
  
  Opened: Thursday, 26 January 2023, 12:00 PM
  
  Due: Thursday, 2 February 2023, 11:59 PM
- Assignment
  
  Assignment 4
  
  Opened: Thursday, 2 February 2023, 12:00 PM
  
  Due: Thursday, 9 February 2023, 11:59 PM
- Assignment
  
  Assignment 5
  
  Opened: Thursday, 9 February 2023, 12:00 PM
  
  Due: Thursday, 16 February 2023, 11:59 PM
- Assignment
  
  Assignment 6
  
  Opened: Thursday, 2 March 2023, 12:00 PM
  
  Due: Thursday, 9 March 2023, 11:59 PM
- Assignment
  
  Assignment 7
  
  Opened: Thursday, 9 March 2023, 12:00 PM
  
  Due: Thursday, 16 March 2023, 11:59 PM
- Assignment
  
  Assignment 8
  
  Opened: Thursday, 16 March 2023, 10:00 AM
  
  Due: Thursday, 23 March 2023, 11:59 PM
- Assignment
  
  Assignment 9
  
  Opened: Thursday, 23 March 2023, 12:00 PM
  
  Due: Thursday, 30 March 2023, 11:59 PM
- Assignment
  
  Assignment 10
  
  Opened: Thursday, 30 March 2023, 12:00 PM
  
  Due: Thursday, 6 April 2023, 11:59 PM
- Assignment
  
  Assignment 11
  
  Opened: Thursday, 6 April 2023, 12:00 PM
  
  Due: Thursday, 13 April 2023, 11:59 PM

Topic outline

General

EES1137 Quantitative Applications for Data Analysis (Winter 2023)

Lectures

Recordings

Assignments