Simon Fraser University
Engaging the World

Course Outlines

Fall 2026 - CMPT 732 G100

Big Data Lab I (6)

Class Number: 4911

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 9 – Dec 7, 2026: Mon, 10:30 a.m.–12:20 p.m.
    Location: TBA

  • Instructor:

    Gregory Baker

  • Prerequisites:

    This course is only available to students enrolled in the master of science in big data program.

Description

CALENDAR DESCRIPTION:

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.

Department Graduate Notes:

The following are default policies in the School of Computing Science. Please check your course syllabus whether the instructor has chosen a different policy for your class, otherwise the following policies apply.
 
  • Students must attain an overall passing grade on the weighted average of exams in the course in order to get a C- or higher.
  • All student requests for accommodations for their religious practices must be made in writing by the end of the first week of classes, or no later than one week after a student adds a course. After considering a request, an instructor may provide a concession or may decline to do so. Students requiring accommodations as a result of a disability can contact the Centre for Accessible Learning (caladmin@sfu.ca).

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.