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Applications for September 2021 will open on November 6, 2020
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MDS Vancouver

UBC’s Vancouver campus Master of Data Science program covers all stages of the value chain, with an emphasis on the skills required to apply meaning to data. Over 10 months, you will learn how to extract data for use in experiments, how to apply state-of-the-art techniques in data analysis, and how to present your findings effectively to domain experts.

Program Benefits

Highlights Across All MDS Programs:

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

Highlights Specific To Vancouver Campus Option:

  • Curriculum designed by combined computer science and statistics experts with input from local industry
  • Courses are taught by joint faculty from computer science and statistics departments to give students a broader skill set
  • A cosmopolitan city, sprawling campus, and a cohort of up to 100 students, offer an engaging, culturally enriched university experience
  • Strong connections with industry partners in public and private sectors, start-ups, and leading tech companies offer a wide range of networking/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
Program design and data manipulation with Python. Overview of data structures, iteration, flow control, and program design relevant to data exploration and analysis. When and how to exploit pre-existing libraries.

Tomas Beuzen
Computing Platforms for Data Science

DSCI 521
How to install, maintain, and use the data scientific software “stack”. The Unix shell, version control, and problem solving strategies. Literate programming documents.

Joel Östblom
Programming for Data Manipulation

DSCI 523
Program design and data manipulation with R. Organizing, filtering, sorting, grouping, reformatting, converting, and cleaning data to prepare it for further analysis.

Tiffany Timbers
Descriptive Statistics and Probability for Data Science

DSCI 551
Fundamental concepts in probability including conditional, joint, and marginal distributions. Statistical view of data coming from a probability distribution.

Aaron Berk

Block 2 (4 weeks, 4 credits)

Data Visualization I

DSCI 531
Exploratory data analysis. Design of effective static visualizations. Plotting tools in R and Python.

Joel Östblom
Algorithms and Data Structures

DSCI 512
How to choose and use appropriate algorithms and data structures to help solve data science problems. Key concepts such as recursion and algorithmic complexity (e.g., efficiency, scalability).

Mike Gelbart
Statistical Inference and Computation I

DSCI 552
The statistical and probabilistic foundations of inference, developed jointly through mathematical derivations and simulation techniques. Important distributions and large sample results. Methods for dealing with the multiple testing problem. The frequentist paradigm.

Tiffany Timbers
Supervised Learning I

DSCI 571
Introduction to supervised machine learning. Basic machine learning concepts such as generalization error and overfitting. Various approaches such as K-NN, decision trees, linear classifiers.

Varada Kolhatkar

Block 3 (4 weeks, 4 credits)

Regression I

DSCI 561
Linear models for a quantitative response variable, with multiple categorical and/or quantitative predictors. Matrix formulation of linear regression. Model assessment and prediction.

Feature and Model Selection

DSCI 573
How to evaluate and select features and models. Cross-validation, ROC curves, feature engineering, and regularization.

Varada Kolhatkar
Data Science Workflows

DSCI 522
Interactive vs. scripted/unattended analyses and how to move fluidly between them. Reproducibility through automation and containerization.

Tiffany Timbers
Databases and Data Retrieval

DSCI 513
How to work with data stored in relational database systems. Storage structures and schemas, data relationships, and ways to query and aggregate such data.

Simon Goring, Tomas Beuzen

Winter: January - April

Block 4 (4 weeks, 4 credits)

Regression II

DSCI 562
Useful extensions to basic regression, e.g., generalized linear models, mixed effects, smoothing, robust regression, and techniques for dealing with missing data.

Supervised Learning II

DSCI 572
Introduction to numerical optimization (e.g., gradient descent). Neural networks and deep learning.

Tomas Beuzen
Statistical Inference and Computation II

DSCI 553
Bayesian reasoning for data science. How to formulate and implement inference using the prior-to-posterior paradigm.

Data Visualization II

DSCI 532
How to make principled and effective choices with respect to marks, spatial arrangement, and colour. Analysis, design, and implementation of interactive figures. How to provide multiple views, deal with complexity, and make difficult decisions about data reduction.

Joel Östblom

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

Communication and Argumentation

DSCI 542
How to interpret and present data science findings to a variety of audiences. Written and spoken presentation skills.

David Laing, Mike Gelbart
Unsupervised Learning

DSCI 563
How to find groups and other structure in unlabeled, possibly high dimensional data. Dimension reduction for visualization and data analysis. Clustering, association rules, model fitting via the EM algorithm.

Varada Kolhatkar
Collaborative Software Development

DSCI 524
How to exploit practices from collaborative software development techniques in data scientific workflows. Appropriate use of abstraction, the software life cycle, unit testing / continuous integration, and packaging for use by others.

Tiffany Timbers
Experimentation and Causal Inference

DSCI 554
Statistical evidence from randomized experiments versus observational studies. Applications of randomization, e.g., A/B testing for website optimization. Methods for dealing with the multiple testing problem.

Block 6 (4 weeks, 4 credits)

Privacy, Ethics, and Security

DSCI 541
The legal, ethical, and security issues concerning data, including aggregated data. Proactive compliance with rules and, in their absence, principles for the responsible management of sensitive data. Case studies.

Joel Östblom
Advanced Machine Learning

DSCI 575
Advanced machine learning methods, with an undercurrent of natural language processing (NLP) applications. Bag of words, recommender systems, topic models, natural language as sequence data, Markov chains, and RNNs for text synthesis. An introduction to popular NLP libraries in Python.

Varada Kolhatkar
Spatial and Temporal Models

DSCI 574
Model fitting and prediction in the presence of correlation due to temporal and/or spatial association. ARIMA models.

Tomas Beuzen
Web and Cloud Computing

DSCI 525
How to use the web as a platform for data collection, computation, and publishing. Accessing data via scraping and APIs. Using the cloud for tasks that are beyond the capability of your local computing resources.

Spring: May - June

Capstone Project (8-10 Weeks, 6 credits)

Capstone Project

DSCI 591 (MDS Vancouver) / DATA 599 (MDS Okanagan)
A mentored group project based on real data and questions from a partner within or outside the university. Students will formulate questions and design and execute a suitable analysis plan. The group will work collaboratively to produce a reproducible analysis pipeline, project report, presentation and possibly other products, such as a dashboard.

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