Worlds become data

INF312 is an upper-level undergraduate course at the University of Toronto’s Faculty of Information.



To a certain extent we are wasting our time. We have a perfect model of the world–it is the world! But it is too complicated. Because of this we must simplify the world in order for it to become data. In this course we explore how we do this, and the implications.



Thank you to the following people for generously providing comments, references, suggestions, and thoughts that directly contributed to this outline: Monica Alexander and Uzair Mirza.



Almost all content will closely follow Telling Stories with Data.

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

‘Gathering data’.

Week 7

‘Gathering data II’.

Week 8

‘Hunting data’.

Week 9

‘Cleaning data’.

Week 10

‘Store, retrieve, disseminate and protect’ and ‘share, but not too much’.

Week 11

Week 12

‘Final paper’.



Item Weight (%) Due date
Quiz 20 Weekly before the lecture
Tutorial 20 Weekly the day before the tutorial
Paper 1 25 End of Week 4
Paper 2 25 End of Week 6
Paper 3 25 End of Week 8
Paper 4 25 End of Week 10
Final Paper (initial submission) 1 Middle of Week 12
Final Paper (peer review) 4 End of Week 12
Final Paper 25 Two weeks after that

You must submit Paper 1. And you must submit the Final Paper.

‘End of’ means Sunday 11:59pm.

Beyond that, you have scope to pick an assessment schedule that works for you. We will take your best 3 of the 11 tutorials, or your best 8 of 11 quizzes for that 20 per cent—whichever results in a better grade for you (i.e. you can choose to do either quizzes or tutorials). And we will take your two best papers from Papers 1-4 for that 50 per cent (25 per cent for each). The remainder is made up of 1 per cent for submitting a draft of the Final Paper, 4 per cent for peer reviewing other people’s drafts of the Final Paper, and 25 per cent for the Final Paper.

Additional details:



Paper #1

Paper #2

Paper #3

Paper #4

Final Paper



This course covers issues in the practices of translating phenomena to data and algorithmic description. What happens, what is gained, what is lost, when things that happen in the world are recorded and made into information or recorded as a document? The course explores representation, modeling, correctness, reliability, and bias in different types of data and algorithms. We will learn about diverse topics such as cultural and algorithmic bias, challenges of big data, what happens when the world is transformed into images, what are the implications of having your social status determined by data and scores on your social media profile, and what we gain or miss when we deal with geographical information systems.

Accommodations with regard to assessment

You do not need to reveal your personal or medical information to me. I understand that illness or personal emergencies can happen from time to time. The following accommodations to assessment requirements exist to provide for those situations.

Straight-forward (will automatically apply to all students - there’s no need to ask for these):

So for those (with the exception of Paper #1), if you have a situation, then just don’t submit.

Slightly more involved:


Requests to have your work re-graded will not be accepted within 24 hours of the release of grades. This is to give you a chance to reflect. Similarly, requests to have your work re-graded more than seven days after the release of the grades will not be accepted. This is to ensure the course runs smoothly.

Inside that 1-7 day period if you would like to request a re-grade, please email with a subject line that starts with INF2178. You must specify where the marking error was made in relation to the marking guide. Your entire assessment will be re-marked and it is possible that your grade could reduce.

Plagiarism and integrity

Please do not plagiarize. In particular, be careful to acknowledge the source of code - if it’s extensive then through proper citation and if it’s just a couple of lines from Stack Overflow then in a comment immediately next to the code.

You are responsible for knowing the content of the University of Toronto’s Code of Behaviour on Academic Matters.

Academic offenses includes (but is not limited to) plagiarism, cheating, copying R code, communication/extra resources during closed book assessments, purchasing labour for assessments (of any kind). Academic offenses will be taken seriously and dealt with accordingly. If you have any questions about what is or is not permitted in this course, please contact me.

Please consult the University’s site on Academic Integrity Please also see the definition of plagiarism in section B.I.1.(d) of the University’s Code of Behaviour on Academic Matters Please read the Code. Please review Cite it Right and if you require further clarification, consult the site How Not to Plagiarize

Late policy

You are expected to manage your time effectively. If no extension has been granted and no accommodation applies, then the late submission of an assessment item carries a penalty of 10 percentage points per day to a maximum of one week after which it will no longer be accepted, e.g. a problem set submitted a day late that would have otherwise received 8/10 will receive 7/10, if that same problem set was submitted two days late then it would receive 6/10.


Papers and reports should be well-written, well-organized, and easy to follow. They should flow easily from one point to the next. They should have proper sentence structure, spelling, vocabulary, and grammar. Each point should be articulated clearly and completely without being overly verbose. Papers should demonstrate your understanding of the topics you are studying in the course and your confidence in using the terms, techniques and issues you have learned. As always, references must be properly included and cited. If you have concerns about your ability to do any of this then please make use of the writing support provided to the faculty, colleges and the SGS Graduate Centre for Academic Communication.