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Master of Science in Big Data
Overview
The Master of Science in Big Data develops data architects who apply a deep knowledge of computer science to create new tools that find value in the vast amounts of information generated today. Students are well-prepared to become data scientists/programmers, data solutions architects, and chief data officers capable of offering insights that influence strategic decision-making.
The curriculum was developed and is constantly being refined using input from an advisory panel of industry leaders. Through SFU's respected co-op program, students tackle real-world challenges, gain valuable project management experience, and grow their network of industry contacts.
school School of Computing Science
work Professional Master's Program
description Master of Science in Big Data
calendar_month 16 Months
#1
CANADIAN COMPREHENSIVE UNIVERSITY
Macleans University Rankings (2025)
1st
BIG DATA PROGRAM OF ITS KIND IN CANADA
#2
IN CANADA FOR DATABASE RESEARCH
#29 in the world
CSrankings.org (2012-2022)
Curriculum
The current curriculum of the Master of Science in Big Data covers (but is not limited to) the following topics:
- Analysis of scalability of algorithms to big data.
- Data warehouses and online analytical processing.
- Efficient storage of big data including data streams.
- Scalable querying and reporting on massive data sets.
- Scalable and distributed hardware and software architectures.
- Software as a service. Cloud Computing (e.g. Amazon EC2, Google Compute Engine).
- Big data programming models: map-reduce, distributed databases, software for implementing streaming and sketching algorithms.
- Dealing with unstructured data such as images, text or biological sequences.
- Scalable machine learning methods such as online learning.
- Data mining: methods for learning descriptive and predictive models from data.
- Distributed algorithms over very large graphs and matrices.
- Social media analysis.
- Visualization methods and interactive data exploration.
Admission Requirements
Admission to our master's programs is competitive: only the best qualified applicants are offered a seat. Therefore, it is imperative that students familiarize themselves with the admission requirements in order to ensure they submit a strong application. Since conditional and qualifying admission offers are made very rarely and only in exceptional cases, students who qualify for regular admission have higher chances of acceptance than those who only qualify for conditional or qualifying admission.
Foundations:
In order for students to succeed in this program, it is expected that they have the following knowledge/experience prior to beginning their studies:
- Ability to program in Java, Python and C++. Familiarity with programming and ability to learn new languages. Students planning to take courses in computer graphics, computer vision, etc. may not need Java and C++ but need Matlab.
- Knowledge of advanced math - calculus, linear algebra and advanced statistics.
- Knowledge of data structures and algorithms, databases, and operating systems.
Regular Admission:
Regular admission is the preferred route for this program. To qualify, students must satisfy the admission requirements laid out here as well as the University's admission requirements for a master's program, as stated in SFU Graduate Regulation 1.3.6a.
STUDENTS WHO HAVE COMPLETED THEIR DEGREES IN CANADA
Students must hold a bachelor's degree or equivalent in computer science or a related field with a cumulative grade point average (CGPA) of at least 3.00/4.33 (B) or the equivalent. Alternatively, a minimum GPA of 3.33/4.33 on the last 60 credits of undergraduate courses will also meet the GPA requirements for admission to the program. All graduate work is also considered.
Students who have completed their degrees outside of Canada
Students applying to this program must hold a bachelor's degree or equivalent in computer science or a related field. Please see here for minimum equivalent qualifications, academic standing and required credential for students who have completed their degree outside of Canada. The academic requirement is listed by country.
CONDITIONAL & QUALIFYING ADMISSION:
In exceptional circumstances, a student may be admitted with lower formal qualifications when there is significant professional experience relevant to the proposed area of scholarship. Please do not contact us about waiving the requirement. Instead, use your application materials (your CV, statement of purpose, etc.) to make the case that your professional experience is relevant and should be considered along with your GPA.
The School's graduate admissions committee may also, at its discretion, offer admission to the program to exceptional students whose undergraduate degree is not in computer science or a related field. Such students typically make up for the lack of program-specific education through relevant work experience, course work, or certificates, diplomas, etc.
For more information on qualifying and conditional admission, please view Graduate Admissions Regulations 1.3.8 and 1.3.9.
Please note: Conditional and qualifying admission are offered only in exceptional circumstances.
ENGLISH LANGUAGE COMPETENCY
The language of instruction, examination and communication in our program is English. Students whose primary language is not English must meet SFU's English proficiency requirements as set out in the Graduate General Regulation 1.3.3. Applicants who have completed a degree at a recognized post-secondary institution where the language of instruction and examination is English in a country where English is the primary language are not required to submit proof of English proficiency. Please view the list of accepted countries here.
All other applicants are required to provide proof of English proficiency. For more detailed information on the requirements, please visit the Graduate Studies page on English Language Requirements.
Meet Our Students
Janet Sun
Master of Science in Big Data, Class of 2023
Previous Education:
Bachelor of Science in Statistics and Economics, University of Toronto
Previous Experience:
Data Analyst at CIBC, Toronto
To me, the excitement in data science is in its unparalleled universality – it can be applied to learn from historical data and improve future performance in almost any discipline. As everything we do generates data, there is huge potential to apply data science to improve the human condition in everything from finance, to weather, to medicine. I really like the hands-on lab courses and the co-op placement in the MPCS program because they prepared me well for working as a data scientist/big data developer in the industry.
Grace Liu
Master of Science in Big Data, Class of 2022
Previous Education:
Bachelor of Arts in Economics, National Taiwan University
Previous Experience:
Machine Learning Engineer Intern at E.SUN Bank, Taipei, Taiwan
I studied Economics because I was fascinated by the idea of explaining human behaviour using data, and I also actively participated in CS/STAT courses to improve my technical skills. In my first data science internship, I developed a daily recommender system for a dating app, SweetRing. The experience of dealing with large amounts of real-life data excited me and motivated me to obtain more professional knowledge through SFU's master's program in professional computer science, as this program best helps me achieve my career goals of building scalable data products in the future.