MATH 2740 Mathematics of Data Science

Some information about the course (Fall 2023)

Julien Arino (julien.arino@umanitoba.ca)

Department of Mathematics & Data Science Nexus
University of Manitoba

Canadian Centre for Disease Modelling
NSERC-PHAC EID Modelling Consortium - CANMOD, OMNI/RÉUNIS & MfPH

Foreword

• "Numerical dates" are in the form YYYY-MM-DD (e.g, today is 2022-09-08)
• Times are 24h
• Units are SI

In case you want to know, some of the slides in the course are html5 generated from markdown files using marp. Others are pure . All (including source code) are available on GitHub here

Getting in touch

• julien.arino@umanitoba.ca

• Please use your myumanitoba email address. Use a tag such as [MATH 2740] in your subject line, if you want to be read..

• There's an entry in the university address book with a phone for me.. don't bother!

• I am physically present on campus on lecture days and some other days, but do not come to see me outside of office hours without an appointment!

  • Office hours: TR 14:00-16:00

• My office hours are for both courses I teach (MATH 2740 and MATH 4370/7370) and might get busy. If things do get busy, I will limit each person to 10 minutes at a time, so come with your questions prepared!

Course website - UMLearn

• All information about the course is posted on UMLearn
• It is your responsibility to check the UMLearn site regularly: all announcements about the course is made there as News items
• (Remember to hit the link at the top of the page that says MATH-2740-A01 - Mathematics of Data Science, sometimes UMLearn takes you directly to Content, which is not where the announcements are)

Lectures

• TR 11:30-12:45 in 204 Armes

• Videos for the course as I taught it in 2021 are available on as a YouTube playlist. There is no guarantee that that the content will be the same this year, but there will be commonalities for sure

Tutorials

• It is strongly recommended to attend tutorials, as this is where you will review some of the mathematical content

• Tutorials are as follows

Evaluation through assignments only

• Evaluation is through assignments only (no tests, no final examination!)
• One assignment per week (posted Friday at 12:00, due the following Friday at 12:00, except Friday before reading week, which is due the Friday after reading week)
• Assignments will be mathematical OR (not XOR) computational. If both, (assignment complete both parts are handed back)
• The mark will consist of the average of the marks on the best 10 assignments
• In the mathematical part, it is possible that not all questions will be marked. If you do not submit an answer to a question that is marked, you will receive zero for that question
• There will be no tolerance for late assignments and there will not be any make-up for missed assignments: any assignment not returned by the deadline will result in a mark of zero

Self-declaration of absences

• You can self-declare an absence of less than 120 hours (5 days) instead of providing a doctor's note
• If a self-declared absence overlaps with the due date and time of Friday at 12:00
  • send me the form and submit the simplified form prior to the deadline
  • I will modify your deadline for that assignment so you can still submit on time
• I will not accept self-declarations after the deadline: if at 12:00 Friday, I have not received a self-declaration form XOR the assignment, you get a mark of zero on that assignment
• Self-declarations are intended for very occasional and unforeseen circumstances I will not accept more than two during the term

Computer work

• Being able to use computers is an integral part of being a data scientist, so in this course, we use computers a lot
• The two main languages in data science are R and Python. Typically, R is used more by people in Stats, while Python is more CS
• There is great value in both and knowing both is a plus, but for simplicity, here we use R. Computer assignments will need to be handed back in R (Python 0)

Returning assignments

• Mathematical part of the assignment goes to Crowdmark
  • Ensure legibility
  • Answer questions in order
• Computer part of the assignment goes to UMLearn
  • R language only (Python 0)
  • Needs to be a jupyter notebook (.ipynb) or an RMarkdown file (.Rmd)
  • Single file
  • Can submit several times but only the latest file will be used
• In both cases, explain what you are doing. Math or code without explanation will lose marks
• If an assignment has both a mathematical and a computer part, the assignment is complete if and only if both parts are handed back. Incomplete assignment 0

Academic dishonesty

• Feel free to discuss work with others, but solutions must be your own !
• Markers will be on the lookout for this
• Paraphrasing my computer code = academic dishonesty !
• stack overflow is a fantastic resource but if you use a solution from there, cite it (in a notebook, that's easy)
• ChatGPT, GitHub Copilot, etc. are not your friends in this course..

Jupyter notebooks on syzygy.ca

• To facilitate computer work, we will use R within jupyter notebooks on syzygy.ca
• I will provide a whole lecture on using jupyter notebooks and syzygy.ca, for now just know that this is a development environment that runs on the web and to which you have access as U of M students
• I am also allowing the return of computer assignments as RMarkdown (Rmd) files. The lecture on jupyter will also cover this
• There is a page on UMLearn on how to connect to syzygy.ca, how to install R or jupyter notebooks on your computer