Undergraduate students interested in data science may apply to either the major specialization in data science, or the data science minor in conjunction with another undergraduate specialization.
Specializations
Major: Data Science (DSCI)
The departments of Computer Science and Statistics jointly offer a major specialization in data science. This major specialization equips students with the knowledge and skills to ethically and transparently apply modern and broadly applicable data science methods. Its graduates are professionals who can effectively communicate complex data analyses while upholding fairness and security in their work with a commitment to continual learning and ethical conduct in the ever-evolving field of data science.
Learning Outcomes
At the successful completion of this program students will be able to:
1. Select and integrate appropriate data science methods to work with a range of data types spanning multiple subject-area domains.
2. Explain, expand and derive the underlying mathematical and statistical principles of key data science methods and algorithms.
3. Apply, assess and compare the performance of a variety of models.
4. Conduct and evaluate data science methods while considering fairness, transparency, and ethical considerations.
5. Apply skills for transporting, storing, governing and curating data responsibly.
6. Select appropriate datasets to answer analytic questions.
7. Employ modern AI tools, while understanding their power and limitations.
8. Formulate relevant questions to better understand the data science problems that need solving.
9. Design and conduct data analyses, and document processes, in a manner that is reproducible.
10. Interpret results of data science analyses considering the context of the problem, limitations of the data collected, as well as biases in the algorithms/models used. 11. Write efficient computer code to meet the requirements of a variety of data science problems.
12. Communicate results of data analyses clearly and effectively to a range of audiences.
13. Select and create appropriate data visualizations to effectively communicate results.
14. Communicate written ideas, arguments, and analyses, accurately and effectively.
15. Present logical arguments orally.
16. Conduct oneself in a manner that is fair, equitable, ethical and honest.
17. Evaluate the privacy, and security issues involved in accessing and working with data.
18. Employ collaborative practices when working on projects that involve both code and people.
19. Reflect on current data science practices and recognize the importance of continual learning in data science.
Specialization Requirements
| First Year | |
| CPSC_V 103 | 3 |
| DSCI_V 100 | 3 |
| MATH_V 100 (or 180 or 120 or 110)1 | 3 |
| MATH_V 101 (or 121) | 3 |
| SCIE_V 113 | 3 |
| Additional Communications Requirement2 | 3 |
| Electives1 | 12 |
| Total Credits | 30 |
| Second Year | |
| DSCI_V 200, 220, 221 | 11 |
| MATH_V 200, 221 | 6 |
| STAT_V 201 | 3 |
| Electives1 | 10 |
| Total Credits | 30 |
| Third and Fourth Year | |
| CPSC_V 330 or 340 | 3 |
| CPSC_V 368 | 3 |
| DSCI_V 310, 320, 430 | 9 |
| STAT_V 301, 302, 305, 443 | 12 |
| Any Upper level MATH_V, STAT_V, CPSC_V or DSCI_V3 | 12 |
| Data Science Depth Requirement4 | 6 |
| Electives | 15 |
| Total Credits | 60 |
| Total Credits for Degree | 120 |
1 Students are permitted to move elective credits between years. Students who take courses in MATH_V with extra credits will require fewer elective credits in later years. Elective credits together with required courses must fulfill the Faculty of Science’s: | |
2 For a full list of acceptable courses see Communication Requirement. | |
3 At least 6 credits must be 400-level or higher. | |
4 Students must choose from a list of courses. See the Data Science website for more detail. | |
Co-op Option
Students can apply to participate in co-op in a manner that suits their schedule and meets the requirements of the co-op program.
Minor in Data Science
The Data Science Minor is an interdisciplinary specialization that enables students to gain the skills necessary to perform data science tasks in conjunction with the skills that they learn in their major. In this Minor, students gain an understanding of key data science concepts such as how to program using data, use statistics on data, and how to use machine learning and statistical models. The Minor in Data Science is an interdisciplinary and interdepartmental undergraduate specialization administered through the Faculty of Science.
Admission to the Minor in Data Science: Students must apply to enter the Minor in Data Science through a process administered jointly by the Departments of Computer Science and Statistics. Applications are accepted once per year, in spring. Applicants must have their home faculty approval to join the minor. See Faculty of Science Minor Options. The application can be accessed at the Data Science Minor website.
This minor consists of 33 credits, of which 18 must be at the 300-level or above.
Lower-Level Requirements
- Data Science: 3 credits of DSCI_V 100.
- Statistical Inference: 3 credits of STAT_V 201.
- Pre-requisites for required upper-level courses
- Programming: 6 credits given by the prerequisites for CPSC_V 330. For most non-CS majors, we recommend CPSC_V 103 followed by CPSC_V 203.
- Math: 3 credits given by the prerequisites for STAT_V 301.
Upper-Level Requirements
18 credits selected as follows:
- STAT_V 301
- CPSC_V 330
- One of DSCI_V 430, CPSC_V 430
- Three of the following five options:
- DSCI_V 310
- DSCI_V 320
- CPSC_V 416
- One of CPSC_V 368, CPSC_V 304, COMM_V 437
- One of COMM_V 335, COMM_V 365, COMM_V 414, COMM_V 415, COMM_V 475, CPSC_V 322, CPSC_V 340, CPSC_V 406, CPSC_V 447, ECON_V 398, ECON_V 425, EOSC_V 440, EOSC_V 410, EOSC_V 354, INFO_V 419, LING_V 342, MATH_V 441, MATH_V 442, MICB_V 405, MICB_V 425, NSCI_V 303, PHYS_V 410, PSYC_V 359, STAT_V 406, STAT_V 447B, STAT_V 450