Course: MSC1090 Introduction to Computational Biostatistics with R (Fall 2023)

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General

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- Select activity Course Description
  
  MSC1090 Introduction to Computational Biostatistics with R (Fall 2023)
  The goal of this course is to prepare graduate students to perform scientific data analysis using the R programming language. Successful students will learn how to use statistical inference and machine-learning tools to gain insight into data sets, as well as be introduced to techniques and best practises for storing, managing and analyzing data. Topics will include: R programming, version control, modular programming, coding best practices, data analysis, machine learning and scientific visualization.
  Classes will be held Tuesdays and Thursdays, 9:00-10:30am, in SS1085.
  Students willing to take the course as part of their graduate program must enrol through Acorn. This course is part of the IMS graduate program.
  Enseignants: Alexey Fedoseev, Erik Spence
  Date de début: : 12 sept. 2023
  Date de fin: : 30 nov. 2023
  Nombre de crédits - calcul scientifique: 8
  Nombre de crédits - science des données: 28
  Événements:
  MSC1090 lecture 1 - mardi 12 septembre, 09:00 » 10:30
  MSC1090 lecture 2 - jeudi 14 septembre, 09:00 » 10:30
  MSC1090 lecture 3 - mardi 19 septembre, 09:00 » 10:30
  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 activity Announcements
  
  Announcements Forum
- Select activity Course Evaluation
  
  Course Evaluation Page
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 the Linux command line II
  
  2. Introduction to the 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. ANOVA
  
  12. ANOVA File
- Select activity 13. Linear models
  
  13. Linear models File
- Select activity 14. Generalized linear models
  
  14. Generalized linear models File
- Select activity 15. Resampling
  
  15. Resampling File
- Select activity 16. Classification I
  
  16. Classification I File
- Select activity 17. Unsupervised learning
  
  17. Unsupervised learning File
- Select activity 18. Classification II
  
  18. Classification II File
- Select activity 19. Neural networks
  
  19. Neural networks File
- Select activity 20. Visualization
  
  20. Visualization File
- Select activity 21. Ensemble methods
  
  21. Ensemble methods File
- Select activity 22. File I/O
  
  22. File I/O
- Select activity 23. Parallel Computing in R
  
  23. Parallel Computing in R File
- Select activity 24. High-Performance Computing in R
  
  24. High-Performance Computing in R 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 the Linux command line II
  
  2. Introduction to the Linux command line II File
- Select activity Introduction to R
  
  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
  
  5. Lists, loops, and conditional statements File
- Select activity 6. Functions
  
  6. Functions 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 tests
  
  11. Hypothesis tests File
- Select activity 12. ANOVA
  
  12. ANOVA File
- Select activity 13. Linear models
  
  13. Linear models File
- Select activity 14. Generalized linear models
  
  14. Generalized linear models File
- Select activity 15. Resampling
  
  15. Resampling File
- Select activity 16. Classification I
  
  16. Classification I File
- Select activity 17. Unsupervised learning
  
  17. Unsupervised learning File
- Select activity 18. Classification II
  
  18. Classification II File
- Select activity 19. Neural networks
  
  19. Neural networks File
- Select activity 20. Visualization
  
  20. Visualization File
- Select activity 21. Ensemble methods
  
  21. Ensemble methods File
- Select activity 22. File I/O
  
  22. File I/O
- Select activity 23. Parallel Computing in R
  
  23. Parallel Computing in R File
- Select activity 24. High-performance R
  
  24. High-performance R File
Select section Assignments

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Assignments
- Select activity Assignment 1
  
  Assignment 1
  
  Opened: Thursday, 14 September 2023, 10:00 AM
  
  Due: Thursday, 21 September 2023, 11:59 PM
- Select activity Assignment 2
  
  Assignment 2
  
  Opened: Thursday, 21 September 2023, 10:00 AM
  
  Due: Thursday, 28 September 2023, 11:59 PM
- Select activity Assignment 3
  
  Assignment 3
  
  Opened: Thursday, 28 September 2023, 10:00 AM
  
  Due: Thursday, 5 October 2023, 11:59 PM
- Select activity Assignment 4
  
  Assignment 4
  
  Opened: Thursday, 5 October 2023, 10:00 AM
  
  Due: Thursday, 12 October 2023, 11:59 PM
- Select activity Assignment 5
  
  Assignment 5
  
  Opened: Thursday, 12 October 2023, 10:00 AM
  
  Due: Thursday, 19 October 2023, 11:59 PM
- Select activity Assignment 6
  
  Assignment 6
  
  Opened: Thursday, 19 October 2023, 10:00 AM
  
  Due: Thursday, 26 October 2023, 11:59 PM
- Select activity Assignment 7
  
  Assignment 7
  
  Opened: Thursday, 26 October 2023, 10:00 AM
  
  Due: Thursday, 2 November 2023, 11:59 PM
- Select activity Assignment 8
  
  Assignment 8
  
  Opened: Thursday, 2 November 2023, 10:00 AM
  
  Due: Thursday, 16 November 2023, 11:59 PM
- Select activity Assignment 9
  
  Assignment 9
  
  Opened: Thursday, 16 November 2023, 10:00 AM
  
  Due: Thursday, 23 November 2023, 11:59 PM
- Select activity Assignment 10
  
  Assignment 10
  
  Opened: Thursday, 23 November 2023, 10:00 AM
  
  Due: Thursday, 30 November 2023, 11:59 PM
- Select activity Assignment 11 - Make-up
  
  Assignment 11 - Make-up
  
  Opened: Thursday, 30 November 2023, 10:00 AM
  
  Due: Thursday, 7 December 2023, 11:59 PM

Topic outline

General

MSC1090 Introduction to Computational Biostatistics with R (Fall 2023)

Lectures

Recordings

Assignments