The big data concentration 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.

CURRICULUM

The current curriculum of the big data concentration 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.

SPECIAL LAB ACCESS

Big Data students enjoy a strong connection to SFU’s Big Data Initiative. Students receive special access to lab space at SFU’s Big Data Hub, located at the Burnaby campus. The newly renovated lab features sophisticated, state-of-the-art computer hardware and software to help you find value in data. With breakout spaces nearby, students can collaboratively work while instructors and TAs can conduct impromptu discussions.

As part of the Hub, students can access hands-on training workshops, attend events featuring big data thought leaders, and connect with a larger community of people engaging in big data questions and approaches. Learn more about SFU’s Big Data Hub at www.sfu.ca/big-data.

COURSEWORK

The layout below shows the recommended course options. For the full list of course options, please see the official calendar entry for the Professional Master of Science in Computer Science.

Core Courses (12 CREDITS)

All students complete the required core courses as laid out below:

  • CMPT 726 Machine Learning
  • CMPT 756 Distributed and Cloud Systems

At least two of:

  • CMPT 713 Natural language Processing
  • CMPT 741 Data Mining
  • CMPT 767 Visualization
  • CMPT 770 Parallel and Distributed Computing
  • IAT 814 Visualization and Visual Analytics
  • STAT 852 Modern Methods in Applied Statistics*

* STAT 652 Statistical Learning and Prediction can be used in place of STAT 852 with permission of the School.

Lab courses (12 CREDITS)

The mandatory lab courses provide hands-on learning of various models, algorithms, and software related to Big Data. Students will take the following two lab courses for 6 credits each.  Only students enrolled in the big data concentration of the Professional Master's Program are permitted to register in these courses:

  • CMPT 732 Programming for Big Data 1
  • CMPT 733 Programming for Big Data 2

ELECTIVE Courses (3 CREDITS)

Students must complete one elective (typically 3 credits) from the following list of courses:

  • CMPT 727 Statistical Machine Learning
  • CMPT 728 Deep Learning
  • CMPT 729 Reinforcement learning
  • CMPT 763 Biomedical Computer Vision
  • CMNS 815 Communication Theories in Technology and Society
  • A special topics course in Computing Science:  CMPT 829, 886, 889, 980, 981, 982, 983, 984, 985
  • Other courses with permission of the School

Course outlines for SFU's Computing Science courses can be found here. For all other outlines, please go here.

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