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About the MDS |
The University of British Columbia is a comprehensive research-intensive university, consistently ranked among the 40 best universities in the world. Since 1915, it has created an exceptional learning environment that fosters global citizenship, advances a civil and sustainable society, and supports outstanding research. Its entrepreneurial perspective encourages students, staff and faculty to challenge convention, lead discovery and explore new ways of learning.
The Department of Computer Science and Department of Statistics at UBC have internationally recognized strengths in data management, data analysis, visualization and software development. These areas are at the core of the emerging discipline known as Data Science, which focuses on the extraction of knowledge from typically large volumes of data. These two departments, both housed in the Faculty of Science, are planning a new course-based Master’s degree program, the Master of Data Science (MDS). This program will focus on utilizing descriptive and prescriptive techniques to extract and analyze data from both unstructured and structured forms and to communicate the findings of those analyses to guide prescriptive change in organizations. This program will educate students in the analysis of data for many different disciplines, such as health care, commerce, and utilities, and will help address the demand for skilled data science professionals in these areas.
Program Start Date: The first cohort of the MDS program started in September 2016.
Program Completion Time: Anticipated time for completion of the program is 10 months of full-time academic study.
Objectives and Program Learning Outcomes: By the end of the program students will be able to
- Apply a scientific approach to marshalling and exploring data, generating and testing hypotheses, designing experiments, and testing/validating methods.
- Select an appropriate data analysis approach and apply it to a new problem area in a context-appropriate manner.
- Manipulate messy, ill-formed data to extract meaningful insights.
- Appropriately select and tailor data science methods to deal with diverse data types (numeric, categorical, text, dates, graphs, etc.) across diverse subject-area domains.
- Collaborate with and communicate results of data science experiments to diverse audiences, and recommend subsequent actions to decision-makers.
- Apply fundamental statistical thinking in the data analysis process, with reference to concepts such as overfitting, confounding, bias, variability, validity, and reliability.
- Apply fundamental programming principles in the data analysis process, with particular emphasis on modularity and reproducibility.
Delivery Methods: The program consists of 24 credits of required coursework and a 6-credit capstone project. The courses will be largely face-to-face lectures, with some blended delivery, and required laboratories. The 24 credits of coursework will be in 1-credit courses to enable intensive focus on particular techniques and skills; students will enrol in either two courses simultaneously for two weeks or four courses simultaneously for four weeks. A small number of selected data sets will be consistently used across the courses, providing continuity for the students across courses. The capstone project will provide an opportunity for students to work together in groups and simulate the process of solving a domain problem on real-world data. This includes posing critical questions about data within a particular domain, making a plan, allocating responsibilities among team members, employing the skills they have learned throughout the program, and reflecting on the strengths and weaknesses of the chosen approach.