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Big Data
The master of science in big data program engages students in developing deep knowledge and practical skills in specialized areas of computer science. The program trains computational specialists who can construct models, develop algorithms, and write software using state-of-the-art graduate-level knowledge and techniques. Students take instructional and lab courses, in a cohort, and complete a co-op through SFU's co-op program, allowing them to tackle real-world scientific, engineering, and socioeconomic problems and gain valuable project management experiences while expanding their network of industrial contacts. This full-time master’s program is suitable for students with a strong aptitude for computer science, or other quantitative fields, such as engineering and mathematics.
Admission Requirements
A student must satisfy the university admission requirements for a master's program as stated in Section 1.3.6a of the Graduate Admission section of the SFU calendar, and the student must hold a bachelor's degree, or equivalent in computer science or a related field, with a minimum cumulative grade point average (GPA) of 3.00 (on a scale of 0.00 - 4.33) or the equivalent. Alternatively, a minimum GPA of 3.33/4.33 on the last 60 units of undergraduate courses will also meet the GPA requirements for admission to the program.
The School's Graduate Admissions Committee may recommend, at its discretion, admission to the professional master's program to exceptional students without an undergraduate degree in computer science or a related field.
Students who do not meet the minimum university requirements may be recommended as conditional or qualifying students as per Graduate General Regulation (GGR) 1.3.8 or 1.3.9.
For further information on conditional or qualifying admission requirements, please contact the Program Coordinator.
Program Requirements
This program consists of course work, and co-op or graduate project for a minimum of 30 graduate units.
The program requires students to maintain a minimum 3.0 CGPA throughout their graduate career.
Students complete all of
Machine Learning is the study of computer algorithms that improve automatically through experience. Provides students who conduct research in machine learning, or use it in their research, with a grounding in both the theoretical justification for, and practical application of, machine learning algorithms. Covers techniques in supervised and unsupervised learning, the graphical model formalism, and algorithms for combining models. Students who have taken CMPT 882 (Machine Learning) in 2007 or earlier may not take CMPT 726 for further credit.
The first of two lab courses that are part of the master of science in big data. This lab course aims to provide students with experience needed for a successful career in big data in the information technology industry. Students will earn core concepts of artificial intelligence and data engineering to work with large, or otherwise complex, data sources. Specifically, this includes statistics and data visualization, data pipeline engineering, and modelling. Many of the assignments will be completed on publicly available, massive data sets giving students hands-on experience with cloud computing, streaming data, and scalable computation - algorithms and software tools needed to master programming for big data. Prerequisite: This course is only available to students enrolled in the master of science in big data program.
The second of two lab courses that are part of the master of science in big data. This lab course aims to provide students with experience needed for a successful career in big data in the information technology industry. Students will learn core concepts of artificial intelligence and applied data science. Specifically, this includes data analytics, advanced statistics and data visualization, deep learning, and anomaly detection. Many of the assignments will be completed on publicly available, complex data sets giving students experience with algorithms and software tools needed to master programming for big data. Prerequisite: CMPT 732.
Students will learn principles and techniques for processing various data types at real-world scale using distributed and cloud computing resources. Fundamentals of approximation and distributed algorithms will be covered. Handling of large-scale image and video datasets, massive graphs, as well as structured and unstructured text datasets will be studied. Designing and building robust software systems using multicore processors, processor accelerators (e.g., Graphics Processing Units) and cloud resources will be introduced.
and an additional nine units of graduate courses in computing science
and one of
This course is the first term of work experience in the School of Computing Science Co-operative Education Program for graduate students. Units of this course do not count towards computing science breadth requirements. Graded on a satisfactory/unsatisfactory basis. Prerequisite: 12 units of CMPT coursework at the 700-level or higher with a CGPA of at least 3.0. Department Consent is required for enrollment.
| Section | Instructor | Day/Time | Location |
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| G100 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| G200 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| G300 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| G400 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| G500 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| G600 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| G700 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| G800 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| I100 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| I200 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| I300 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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| I400 |
May 6 – Aug 2, 2024: Mon, Tue, Wed, Thu, Fri, 8:00 a.m.–8:00 p.m.
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Graded on a satisfactory/unsatisfactory basis. Prerequisite: Permission of the Graduate Program Chair.
Co-op
All students are required to apply for a co-op. With assistance from the co-op coordinator for this program, students will be expected to find a suitable industry partner. Students may complete one or two terms of co-op. The latter option is in place to satisfy requests from our industrial partners for continuity and to carry out a large-scale project. Students are required to enroll in at least one of the program courses in the term following their co-op.
A co-op is an integral part of this program. However, it is offered on a competitive basis.
In the event that a student is unable to secure a co-op during the summer term, they will be required to go on academic break since no courses will be offered. The student will be able to apply for a co-op in the subsequent term or, if unsuccessful, will be required to undertake additional course work. In consultation with the program director, the student may complete a graduate project in their final term to fulfil program requirements.
Program Length
Students are expected to complete the program requirements in four terms.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.