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Big Data
The graduate diploma in big data is a professional graduate program that provides a hands-on introduction to computing technology relevant to the acquisition, preprocessing, storage, manipulation, and analysis of very large data sets, including both structured and unstructured data.
大象传媒 Requirements
Applicants must satisfy the university admission requirements as stated in Graduate General Regulation 1.3 in the 大象传媒 Calendar. In addition to the university CGPA requirement, students must hold a bachelor's degree or its equivalent in computing science or a related field. Students admitted to the master of science in big data may transfer to the graduate diploma in big data at any time with permission of the graduate program committee and graduate studies.
Program Requirements
This program consists of course work for a minimum of 22 units. The program requires students to maintain a minimum 2.5 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.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Mo Chen |
Sep 3 鈥� Dec 2, 2025: Wed, 3:30鈥�4:20 p.m.
Sep 3 鈥� Dec 2, 2025: Fri, 2:30鈥�4:20 p.m. |
Burnaby Burnaby |
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.
| Section | Instructor | Day/Time | Location |
|---|---|---|---|
|
Gregory Baker |
Sep 3 鈥� Dec 2, 2025: Mon, 10:30 a.m.鈥�12:20 p.m.
|
Burnaby |
|
| G101 |
Gregory Baker |
Sep 3 鈥� Dec 2, 2025: Tue, 2:30鈥�4:20 p.m.
Sep 3 鈥� Dec 2, 2025: Thu, 2:30鈥�4:20 p.m. |
Burnaby Burnaby |
| G102 |
Gregory Baker |
Sep 3 鈥� Dec 2, 2025: Tue, 12:30鈥�2:20 p.m.
Sep 3 鈥� Dec 2, 2025: Thu, 12:30鈥�2:20 p.m. |
Burnaby Burnaby |
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. This course is only available to students enrolled in the master of science in big data program.
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 three units of graduate courses in computing science
and
Students prepare a portfolio of their works in the area of big data including work from big data lab courses and other relevant courses, as well as contributions to other projects. Graded on a satisfactory/unsatisfactory basis. Prerequisite: CMPT 733.
or
three additional units of graduate courses in computing science
* In CMPT 790, students prepare a portfolio of their works in the area of big data including completed projects and assignments from the big data lab courses and other relevant courses, as well as contributions to other projects. The portfolio is examined by at least two readers from the professional graduate programs committee.
Program Length
We expect that full-time students can complete the graduate diploma in big data in three 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.