Course: EES1137 Quantitative Applications for Data Analysis (Winter 2025)

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General

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- Select activity Course Description
  
  EES1137 Quantitative Applications for Data Analysis (Winter 2025)
  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.
  Enseignants: Alexey Fedoseev, Erik Spence
  Date de début: : 7 janv. 2025
  Date de fin: : 3 avr. 2025
  Nombre de crédits - calcul scientifique: 8
  Nombre de crédits - science des données: 28
  Événements:
  Assignment 1 is due - jeudi 16 janvier, 23:59
  Assignment 2 is due - jeudi 23 janvier, 23:59
  Assignment 3 is due - jeudi 30 janvier, 23:59
  Une liste complète des événements du cours peut être trouvée ici ici.
- Select activity Software requirements
  
  Software requirements Page
- Select activity Student forum
  
  Student forum
Select section Lectures

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Lectures
- Select activity 1. Introduction to the Linux command line I
  
  1. Introduction to the 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 System - Git
  
  8. Version Control System - Git File
- Select activity 9. Coding Best Practices
  
  9. Coding Best Practices File
- Select activity 10. Distributions
  
  10. Distributions File
- Select activity 11. Hypothesis tests
  
  11. Hypothesis tests 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 and functions in Python
  
  15. Loops 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
Select section Recordings

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Recordings
- 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
Select section Assignments

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Assignments
- Select activity Assignment 1
  
  Assignment 1
  
  Opened: Thursday, 9 January 2025, 11:00 AM
  
  Due: Thursday, 16 January 2025, 11:59 PM
- Select activity Assignment 2
  
  Assignment 2
  
  Opened: Thursday, 16 January 2025, 11:00 AM
  
  Due: Thursday, 23 January 2025, 11:59 PM
- Select activity Assignment 3
  
  Assignment 3
  
  Opened: Thursday, 23 January 2025, 11:00 AM
  
  Due: Thursday, 30 January 2025, 11:59 PM
- Select activity Assignment 4
  
  Assignment 4
  
  Opened: Thursday, 30 January 2025, 11:00 AM
  
  Due: Thursday, 6 February 2025, 11:59 PM
- Select activity Assignment 5
  
  Assignment 5
  
  Opened: Thursday, 6 February 2025, 11:00 AM
  
  Due: Thursday, 13 February 2025, 11:59 PM
- Select activity Assignment 6
  
  Assignment 6
  
  Opened: Thursday, 27 February 2025, 11:00 AM
  
  Due: Thursday, 6 March 2025, 11:59 PM
- Select activity Assignment 7
  
  Assignment 7
  
  Opened: Thursday, 6 February 2025, 11:00 AM
  
  Due: Thursday, 13 March 2025, 11:59 PM
- Select activity Assignment 8
  
  Assignment 8
  
  Opened: Thursday, 13 February 2025, 11:00 AM
  
  Due: Thursday, 20 March 2025, 11:59 PM
- Select activity Assignment 9
  
  Assignment 9
  
  Opened: Thursday, 20 March 2025, 11:00 AM
  
  Due: Thursday, 27 March 2025, 11:59 PM
- Select activity Assignment 10
  
  Assignment 10
  
  Opened: Thursday, 27 March 2025, 11:00 AM
  
  Due: Thursday, 3 April 2025, 11:59 PM
- Select activity Assignment 11
  
  Assignment 11
  
  Opened: Thursday, 3 April 2025, 11:00 AM
  
  Due: Thursday, 10 April 2025, 11:59 PM

Topic outline

General

EES1137 Quantitative Applications for Data Analysis (Winter 2025)

Lectures

Recordings

Assignments