EEB125 Winter 2025 Course Syllabus#

Teaching Team#

Teaching assistants:

Course Schedule#

Lecture:

Day

Time

Location

Wednesday

1:10pm - 3:00pm

WI 1017

Labs:

Section

Day

Time

Location

TA

TUT0101

Thursday

12:10pm - 1:00pm

SS 2114

Michelle Su

TUT0102

Thursday

12:10pm - 1:00pm

SS 2111

Meng Yuan

TUT0103

Thursday

12:10pm - 1:00pm

RL 14190

Alan Bui

Note: Labs begin on Thursday, January 16 (the second week of class).

Course Description#

Life and physical scientists increasingly use big and complex datasets to answer questions about society and the natural world. In this course, students will develop introductory programming knowledge and data acumen to explore topics drawn from biology, chemistry, physics, and psychology. Students will learn to create and run computer programs, organize ideas using data to communicate clearly to others, break a complex problem into simpler parts, apply general data science principles to specific cases, distinguish causation from correlation and coincidence, and negotiate tradeoffs between different computational and statistical approaches.

Course Learning Objectives#

By the end of this course, you will 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 using Python and provide an oral and written interpretation of the results.

  • Identify appropriate uses of computational and statistical methods to answer scientific 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 to computing, Python, and Jupyter notebooks

2

Jan. 15

Data types and working with text

3

Jan. 22

Learning from data

4

Jan. 29

Modeling data, frequency distributions

5

Feb. 5

TBD

6

Feb. 12

Midterm test

–

Feb. 19

Reading week

7

Feb. 26

Introduce final project; introduction to data science packages

8

March 5

Exploratory data analysis, describing and visualizing data

9

March 12

Tests of statistical significance

10

March 19

Confidence intervals and Estimation

11

March 26

Statistical learning

12

April 2

Course wrap up

Evaluation#

Assessment

Weight

Due Date/Date Held

Weekly homework

25%

Tuesdays 11:59pm

Midterm test

20%

Feb 12 1:10-3:00pm

Final project proposal

10%

March 10 11:59pm

Final project

15%

April 4 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 and complete assigned homework exercises for the week. There will be 10 labs and homeworks in total.

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. The overall purpose of the labs is for you to gain experience using Python in a Jupyter notebook to compute and visualize data under the guidance of a teaching assistant. Labs will be designed to help you review course material and get started on the weekly homework exercises. While lab attendance is not graded, we strongly recommend attending all labs to help you stay on track throughout the semester, and get help if you need it.

You’ll then have additional time after the lab to complete your assigned homework, and submit it by Tuesday 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 online, 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 10, and the final project will be due on April 4. One of the goals of the project proposal is to give you feedback that can be incorporated into your final project.

Final Exam#

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#

Late weekly homework assignments will not be accepted for any reason, since we plan to post answers soon after the due date. If you do not submit a homework by the deadline then your grade on will be 0. NB: the two lowest lab and homework 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 17). If you miss both the scheduled midterm and the makeup midterm then you will receive a 0 for the midterm.

Final Project#

Late project proposals will not be accepted. Late final projects will be accepted upto 24 hours after the deadline, but there will be a penalty of 10%.

If you submit a late proposal then the teaching team cannot guarantee feedback in a timely fashion which means that you may have less time to incorporate your feedback into your final submission.

Final Exam#

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

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 re-submit.

How to communicate with your instructors#

Questions about course material or organization, such as,

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

  • How do I do question 3?

  • When is the term test?

should be posted on the discussion forum (Piazza) 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 e-mail your instructor. Use your utoronto.ca e-mail account to ensure that your message doesn’t automatically go to a Junk folder and include your full name and student number.

Use of ChatGPT / Generative AI#

Students may use artificial intelligence tools for creating an outline for an assignment, but the final sumitted assignment must be original work produced by the individual student alone.

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.

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: mailto:accessibility.services@utoronto.ca or https://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 computation and data science interesting, challenging, and fun. In order for classroom sessions and labs to be effective, prepare by learning about the weeks concepts through completing the recommended problems and readings.