Ctrl+K

GGR274 Winter 2024 Course Syllabus

GGR274 Winter 2024 Course Syllabus#

Teaching Team#

Teaching assistants:

Course Schedule#

Lecture:

Day

Time

Location

Tuesday

09:10am - 11:00am

SS 2117

Tutorials:

Section

Day

Time

Location

PRA0101

Tuesday

12:10pm - 1:00pm

UC 261

PRA0201

Tuesday

1:10pm - 2:00pm

WE 75

PRA0301

Wednesday

11:10am - 12:00pm

WE 75

PRA0401

Wednesday

12:10pm - 1:00pm

MP 118

PRA0501

Wednesday

1:10pm - 2:00pm

WE 75

PRA0601

Thursday

11:10am - 12:00pm

WI 524

Course description#

Data Science is a collection of disciplines with a shared mission of using digital technologies and information to advance human society. Social scientists are increasingly working with big and complex datasets to answer questions about society. The purpose of this course is to give you a broad introduction to the many of the ways social scientists learn about society using data.

We will use the Python programming language and Jupyter notebooks for statistical computing. Tutorials will give students hands-on practice with computing, statistical reasoning, and communication.

Course learning objectives#

By the end of this course, you should be able to:

  • Write computer programs in Python to prepare data for statistical analyses and visualizations.

  • Write computer programs in Python to conduct a variety of statistical analyses.

  • Identify appropriate uses of computational and statistical methods to answer social science questions, including their strengths and limitations.

  • Clearly communicate the results of a data analysis to both technical and non-technical audiences.

Tentative weekly schedule#

Class

Week of

Topic

1

Jan. 8

Introduction (All)

2

Jan. 15

Programming in Python, Part 1: data types, variables, functions, and Jupyter notebooks (IM)

3

Jan. 22

Programming in Python, Part 2: Control flow, defining functions, and working with files (IM)

4

Jan. 29

Introduction to data wrangling using the pandas library (MM)

5

Feb. 5

Exploratory data analysis, describing and visualizing data (MM)

6

Feb. 12

Midterm test (All)

–

Feb. 19

Reading week

7

Feb. 26

Probability, Sampling, Distributions, and Simulation (MM)

8

March 4

Comparing two groups, Causality (MM)

9

March 11

Confidence intervals, Hypothesis testing, Linear Regression (CL)

10

March 18

Intro to Spatial Data Analysis, Loading spatial info, Simple maps (CL)

11

March 25

More on mapping, “Hot spot” detection, Measuring spatial autocorrelation (CL)

12

April 1

Spatial autocorrelation cont., Final projects (CL, MM)

Evaluation#

Assessment

Weight

Due Date/Date Held

Weekly homework

20%

Monday 11:59pm

Lab attendance

5%

Weekly lab and submission by Thursday 11:59pm

Midterm test

20%

Feb. 13 9:10am-11:00am

Final project proposal

10%

March 8 11:59pm

Final project

15%

April 5 11:59pm

Final exam

30%

April Final Assessment period

Weekly lab and homework#

Each week (except the week of the midterm and final week of class), you will attend your lab section to start on the homework exercises for the week, and participate in specific tutorial related activities.

There will be 10 labs and homeworks in total.

Labs will be designed to help you review course material and get started on the weekly homework exercises.

In the lab, you’ll be able to work with your classmates and your TA to practice and apply the knowledge and skills introduced in that week’s lecture.

You’ll complete a few small tasks in the lab and submit your lab Notebook during the lab session (or by 23:59 on Thursday).

You’ll then have additional time after the lab to complete your assigned homework, and submit it by Monday at 11:59pm the following week. Your submitted homework will be graded both by TAs and by automated tests to check the correctness of your code.

Note: to give you flexibility throughout the semester, your two lowest homework grades will be dropped. This includes dropping grades of 0 when you do not submit a weekly homework.

Midterm test#

The midterm test will be held during regular class time.

More details about the midterm will be posted closer to the date.

Final project#

The final project will involve students using data science methodologies to explore a scientific question and communicate the results.

A project proposal will be due on March 8, and the final project will be due on April 5. One of the goals of the project proposal is to give you feedback that can be incorporated into your final project.

Final exam/assessment#

The final exam will be held in person, scheduled by the Faculty of Arts & Science. For more information on final exams, please see the FAS Exams & Assessments webpage.

Missed assessments#

Weekly homework#

The two lowest homework grades will be excluded when calculating your final grade. Late weekly homework assignments cannot be accepted will not be accepted for any reason, since we plan to post answers soon after the due date. In other words if you do not submit a homework by the deadline then your grade on that homework will be 0.

Weekly lab attendance#

Grade of lab attendance is counted based on attendance at each lab and submission of lab on MarkUs. Attendance will be marked as present & submission (1) or absent or unsubmission (0). Your two lowest lab attendance grades will be excluded when calculating your final grade.

Midterm test#

If you miss the midterm for any reason then you will have an opportunity to write a makeup midterm sometime during the same week (i.e., before February 18). If you miss both the scheduled midterm and the makeup midterm then you will receive a 0 for the midterm.

Final exam/assessment#

For information on final exam absences and deferral requests, please consult the Faculty of Arts and Science website.

Course software and computing requirements#

You will complete all work in this course using the University of Toronto Jupyterhub, which is a web-based computing platform. You can access JupyterHub via any computer with a web browser and Internet connection, including on-campus computers like the ones found at the University’s libraries. You do not need to install any software on your computer for this course!

On JupyterHub, we’ll be using the Python programming language in a Jupyter notebook. The teaching team will be able available to help provide support in using Python and Jupyter notebooks on JupyterHub.

You are welcome to install Python and Jupyter on your computer. But, if you decide to complete course assessments on your computer outside of JupyterHub, we may not be able to provide help if you encounter any technical difficulties. In particular, this will not be an accepted reason that your homework was submitted after its due date.

Marking Concerns#

Any requests to have an assessment remarked must contain a written justification for consideration. Marking requests should be made within one week of receiving your assessment. Please note that we reserve the right to reconsider the marking of every question on your assessment when you resubmit.

How to communicate with your instructors#

Questions about course material or organization, such as:

  • How do I change the colour of my plotting symbol?

  • What is question 3 asking us to do?

  • When is the term test?

should be posted on the discussion forums or asked in person. Questions can be posted anonymously (so that the author is anonymous to other students but not to the instructors), if desired.

If your communication is private, such as “I missed the test because I was ill”, then email your instructor. If you missed a tutorial, email your TA. Use your utoronto.ca email account to ensure that your message doesn’t automatically go to a Junk folder and please include your full name, UTORid, and student number.

Academic Integrity#

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

As a general rule, we encourage you to discuss course material with each other and ask others for advice. However, it is not permitted to share complete solutions or to directly share code for anything that is to be handed in. When an assignment is required to be completed as a team, you may share solutions and code with other members of your team, but not with another team in the class. For example, “For question 2 what Python function did you use?” is a fair question; “Please show me your Python code for question 2” is not.

If you have any questions about what is or is not permitted in this course, please do not hesitate to contact your instructors.

Use of ChatGPT/ Generative AI#

You are encouraged to use generative artificial intelligence tools as you work through the assignments (weekly homework and final project) in this course. The use of any generative AI tools in your final project must be documented in an appendix. The documentation should include what tool(s) were used, how they were used, and how the results from the AI were incorporated into the submitted work. Course instructors reserve the right to ask students to explain their process for creating their assignment.

You should keep in mind not to rely on these tools as the final exam will be conducted with no access to the Internet.

Accessibility Needs#

The University of Toronto is committed to accessibility. If you require accommodations for a disability or have any accessibility concerns about the course, the class room, or course materials, please contact Accessibility Services as soon as possible: accessibility.services@utoronto.ca or http://accessibility.utoronto.ca

Your Responsibilities#

The course is designed to actively engage you in the course material. We hope you’ll find this course on social science, computation and data science interesting, challenging, and fun. In order for classroom sessions and tutorials to be effective, prepare by learning about the week’s concepts through completing the recommended problems and readings.

Contents