Topic outline

• Select section General

  Collapse Expand

  General

  Collapse all Expand all

  • 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.

    Teachers: Alexey Fedoseev, Erik Spence

    Start date: 12 Sep. 2023

    End date: 30 Nov. 2023

    Scientific Computing Credits: 8

    Data Science Credits: 28

    Events:

    MSC1090 lecture 1 - Tuesday, 12 September, 9:00 AM » 10:30 AM

    MSC1090 lecture 2 - Thursday, 14 September, 9:00 AM » 10:30 AM

    MSC1090 lecture 3 - Tuesday, 19 September, 9:00 AM » 10:30 AM

    A full list of course events can be found here.
  • 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

  Collapse Expand

  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

  Collapse Expand

  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

  Collapse Expand

  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