Launching in the 2021-22 academic year, UBC's Data Science Minor is an interdisciplinary program that enables students to gain the skills necessary to perform data science tasks in conjunction with the skills 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 program administered through the Faculty of Science. This program is open to any UBC-Vancouver undergraduate student.
Prerequisites for admission to the minor
A minimum grade of 68% in DSCI 100 and a minimum grade of 68% in (a) one of CPSC 110, CPSC 107, CPSC 103, EOSC 211, MATH 210, PHYS 210, ECON 323, COMM 337, APSC 160 or (b) any CPSC course numbered 200 or higher.
Students applying for the Data Science minor must already have a primary specialization and have year 2 or 3 standing in the most recent winter session (e.g. 2020 Winter session for the May 2021 deadline).
Program requirements for the Data Science Minor
- Data Science: DSCI 100.
- Statistical Inference: STAT 201.
- Programming: Either (a) one of CPSC 203, CPSC 210, CPEN 221 or (b) one of MATH 210, ECON 323 and one of CPSC 107, CPSC 110. For most non-CS majors, we recommend CPSC 103 followed by CPSC 203.
- Calculus: One of MATH 100, MATH 102, MATH 104, MATH 110, MATH 120, MATH 180, MATH 184, SCIE 001.
There are 6 courses (18 credits) of upper-level requirements. The requirements are:
- Statistical inference: STAT 301
- Machine learning: CPSC 330
- Four of the following six options:
- Reproducible data science: DSCI 310
- Data visualization: DSCI 320
- Cloud computing and big data: CPSC 416
- Databases: One of CPSC 368, CPSC 304, COMM 437
- Ethics for data science: CPSC 430
- Discipline-specific data science courses: one of COMM 335, COMM 365, COMM 414, COMM 415, CPSC 322, CPSC 340, CPSC 406, ECON 398, ECON 425, EOSC 442, EOSC 410, INFO 419, LING 342, MATH 441, MATH 442, MICB 405, MICB 425, PHYS 410, PSYC 359, STAT 406, STAT 447B, STAT 450.
New Data Science courses
DSCI 310 Reproducible and trustworthy workflows for data science
Data science methods to automate the running and testing of code and analytic reports, manage data analysis software dependencies, package and deploy software for data analysis, and collaborate with others using version control.
- Credits: 3
- Pre-reqs: DSCI 100 and either (a) one of CPSC 203, CPSC 210 or CPEN 221 or (b) MATH 210 and one of CPSC 107, CPSC 110.
DSCI 320 Visualization for data science
Analysis, design, and implementation of static and interactive visual representations. Visualization literacy. Data communication. Exploratory Data Analysis. Application of theoretical principles to visualization development.
- Credits: 3
- Pre-reqs: STAT 201 and one of CPSC 203, CPSC 210, or CPEN 221.
CPSC 368 Databases in Data Science
Overview of relational and non-relational database systems. Role and usage of a database when querying data. Topics include data modelling, query languages, and query optimization.
- Credits: 3
- Pre-reqs: One of CPSC 203, CPSC 210, CPEN 221.
As an example path, consider a student in Psychology who is interested in the Data Science minor. This example student might take the following courses:
- DSCI 100
- CPSC 103
- CPSC 203
- STAT 201
- MATH 102
- STAT 301
- CPSC 330
- CPSC 368
- DSCI 310
- DSCI 320
- PSYC 359
Program learning outcomes
- Identify and collect data necessary to answer a given research question through sampling and/or through extracting data from pre-existing sources (relational databases, html web pages, web APIs, etc)
- Manipulate messy, ill-formed data to extract meaningful insights.
- Map and apply an appropriate data analysis approach to a given research question and the data at hand.
- Select data science methods to work with diverse data types across diverse subject-area domains.
- Build statistical models that are appropriate given the distribution(s) of the data, and appropriately quantify uncertainty of resulting estimates and predictions.
- Apply fundamental programming principles in the data analysis process to make analysis code readable, modular, accurate and scalable.
- Communicate results of data science experiments to diverse audiences through data visualizations, written work and oral presentations.
- Employ best practices for collaboration for projects that involve both code and people.
- Perform and communicate results from analyses that are fair, equitable and honest.
- Employ workflows that facilitate reproducible and transparent data analyses.
Frequently Asked Questions
View the program's FAQ page on GitHub.
Contact the Data Science Minor program
You can reach out to the program via a GitHub Issue.
For questions about the Data Science Minor program requirements, you may email the Data Science Minor Advisor.
Advisor office hours: Thursday 11AM - 12PM: See details here.
Applications are due May 31, 2021 for admissions into the minor for the 2021 intake. In future years, our application deadline will align with other UBC Science minor deadlines (May 15). If the minor is over-subscribed, a randomization process will be used. To promote academic diversity in the program, we are restricting the program's makeup to a maximum of 20% from any one home department.
When applying to the Data Science minor program, students must also be sure to complete any requirements for admission into a minor program from their home faculty. For example, the Faculty of Science students must fill out the form for "Science Minor: Minor in another Science specialization. A link to this form is provided within the data science minor application form. If you have already applied to the Data Science minor but are a Science student that still needs to fill out this form, you can also obtain this link by emailing the Data Science Advisor.
Applications for 2021/2022 academic year are now closed and will reopen in spring 2022 for 2022/2023.