Program Overview

Introduction to the Master of Data Science Program

The University of British Columbia's Master of Data Science program is a 10-month, full-time, accelerated program that covers all stages of the value chain, with an emphasis on the skills required to apply meaning to data. Over the course of the program, students will learn how to extract data for use in experiments, apply state-of-the-art techniques in data analysis, and present their findings effectively to domain experts.

Program Benefits

The Master of Data Science program offers several benefits, including:

• A 10-month, full-time, accelerated program that provides a short-term commitment for long-term gain
• Condensed one-credit courses that allow for in-depth focus on a limited set of topics at one time
• A capstone project that gives students an opportunity to apply their skills
• Real-world data sets integrated into all courses to provide practical experience across a range of domains

Highlights Specific to the Vancouver Campus Option

The Vancouver campus option offers:

• A curriculum designed by combined computer science and statistics experts with input from local industry
• A coordinated approach that blends computer science and statistics education to give students a broader skill set
• Courses taught by a core team of faculty dedicated to teaching the Master of Data Science program full-time and providing support to students during the program
• A cosmopolitan city, sprawling campus, and a cohort of up to 100 students, offering an engaging, culturally enriched university experience
• Strong connections with industry partners in public and private sectors, start-ups, and leading tech companies, providing a wide range of networking and career opportunities

Curriculum

The program structure includes 24 one-credit courses offered in four-week segments. Courses are lab-oriented and delivered in-person with some blended online content. At the end of the six segments, an eight-week, six-credit capstone project is also included, allowing students to apply their newly acquired knowledge while working alongside other students with real-life data sets.

Fall: September - December

Block 1 (4 weeks, 4 credits)

• Programming for Data Science | DSCI 511: Practical introduction to Python programming with a focus on data science
• Computing Platforms for Data Science | DSCI 521: Essential computing platforms and tools that underpin effective data science work
• Programming for Data Manipulation | DSCI 523: Program design and data manipulation with R
• Descriptive Statistics and Probability for Data Science | DSCI 551: Introduces students to the foundational probabilistic principles of statistical reasoning

Block 2 (4 weeks, 4 credits)

• Algorithms and Data Structures | DSCI 512: Sharpens algorithmic thinking and teaches students how to analyze problems and design efficient algorithms
• Data Visualization I | DSCI 531: How to (and how not to) visualize data, including graphical grammars via ggplot in R and Altair in Python
• Statistical Inference and Computation I | DSCI 552: Statistical and probabilistic foundations of inference, focusing on the frequentist paradigm
• Supervised Learning I | DSCI 571: Fundamental concepts and techniques of supervised machine learning

Block 3 (4 weeks, 4 credits)

• Databases and Data Retrieval | DSCI 513: Learn how to work with data stored in relational and NoSQL database systems
• Data Science Workflows | DSCI 522: Full lifecycle of data analysis by integrating interactive and scripted methods
• Regression I | DSCI 561: Linear models for predicting a quantitative response variable using multiple categorical and/or quantitative predictors
• Feature and Model Selection | DSCI 573: Model analysis and improvement via evaluation metrics, loss functions, feature engineering, and ensemble techniques

Winter: January - April

Block 4 (4 weeks, 4 credits)

• Collaborative Software Development | DSCI 524: Advanced practices and tooling foundational to professional, trustworthy, and scalable data science software
• Communication and Argumentation | DSCI 542: Essential communication skills for data scientists
• Regression II | DSCI 562: Builds on regression methods to cover advanced modeling techniques used in data science
• Supervised Learning II | DSCI 572: Dive into deep learning with Python and PyTorch

Block 5 (4 weeks + 1 week break, 4 credits)

• Data Visualization II | DSCI 532: Project course where each student team develops and deploys a dashboard for interactive data visualization
• Statistical Inference and Computation II | DSCI 553: Introduces the Bayesian paradigm in statistics
• Unsupervised Learning | DSCI 563: Uncovering underlying structure in data
• Spatial and Temporal Models | DSCI 574: Model fitting and prediction when data exhibit spatial and/or temporal dependence

Block 6 (4 weeks, 4 credits)

• Web and Cloud Computing | DSCI 525: Go beyond the limits of your laptop and learn to tackle large-scale data science tasks
• Privacy, Ethics, and Security | DSCI 541: Focuses on the ethical considerations of data science
• Experimentation and Causal Inference | DSCI 554: Introduces statistical methods for design of experiments and making causal inferences
• Advanced Machine Learning | DSCI 575: Explore advanced machine learning methods for natural language processing applications

Spring: May - June

Capstone Project (8-10 Weeks, 6 credits)

• Capstone Project | DSCI 591: A mentored group project based on real data and questions from a partner within or outside the university

Application Deadlines

Applications for September 2026 are now open. Application deadlines are as follows:

• International Students (Early Round – MDS Computational Linguistics only): November 30, 2025
• International Students (Early Round – MDS Okanagan only): December 15, 2025
• International Students (all programs): January 31, 2026
• Domestic Students who want to be considered for scholarships (all programs): January 31, 2026
• Domestic/US Students (all programs): April 15, 2026
• Reference Deadline: 7 days after application deadline

Testimonials

"The program has equipped me with many foundational data science skills and the ability to dive deeper into different topics on my own, like statistics, machine learning, visualization, and reproducibility." - Stephanie Ta, MDS Vancouver, Class of 2025

Acknowledgement

We acknowledge that UBC's campuses and learning sites are situated within the traditional territories of the Musqueam, Squamish, and Tsleil-Waututh and in the traditional, ancestral, unceded territory of the Syilx Okanagan Nation and their peoples.