Course: EES1137 Quantitative Applications for Data Analysis (Winter 2022)

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Topic outline

General

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- EES1137 Quantitative Applications for Data Analysis (Winter 2022)
  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 have to enrol through Acorn/ROSI.
  This course is part of the EES graduate program and will be taught online this semester.
  Teachers: Alexey Fedoseev, Erik Spence
  Start date: 11 Jan 2022
  End date: 7 Apr 2022
  Scientific Computing Credits: 8
  Data Science Credits: 28
  Events:
  Lecture 1 - Tuesday, 11 January, 10:30 AM » 12:00 PM
  Lecture 2 - Thursday, 13 January, 11:00 AM » 12:30 PM
  Lecture 3 - Tuesday, 18 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 the Linux Shell I File
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  2. Introduction to the 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
- File
  
  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 II 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 the Linux shell I File
- File
  
  2. Introduction to the Linux shell 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 - git File
- File
  
  9. Coding best practices File
- File
  
  10. Distributions File
- File
  
  11. Hypothesis testing File
- File
  
  12. Linear models File
- File
  
  13. Visualization in R File
- File
  
  14. Introduction to Python File
- File
  
  15. Loops and function in Python File
- File
  
  16. NumPy, SciPy and Visualization File
- File
  
  17. Introduction to Machine Learning File
- File
  
  18. Resampling File
- File
  
  19. Monte Carlo methods File
- File
  
  20. Classification II 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
- Assignment
  
  Assignment 2
- Assignment
  
  Assignment 3
- Assignment
  
  Assignment 4
- Assignment
  
  Assignment 5
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  Assignment 6
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  Assignment 7
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  Assignment 8
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  Assignment 9
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  Assignment 10
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  Assignment 11

November 2024

Topic outline

General

EES1137 Quantitative Applications for Data Analysis (Winter 2022)

Lectures

Recordings

Assignments