Simon Fraser University
Engaging the World

Course Outlines

Summer 2025 - MSE 981 G100

Industrial Big Data Analytics (3)

Class Number: 4524

Delivery Method: In Person

Overview

  • Course Times + Location:

    May 12 – Aug 8, 2025: Mon, 6:00–8:50 p.m.
    Surrey

Description

CALENDAR DESCRIPTION:

Data is the lifeblood of the smart factory. Provides students with hands-on experience in big data analytics. Students in this course learn about life cycle of big data analytics for Industry 4.0 from data collection to data preparation to data mining. As a result, they are empowered with the skill of handling massive, heterogeneous manufacturing data in highly distributed environments of Industry 4.0.

COURSE DETAILS:

This course provides students with skills to turn big industrial data into actionable information by using predictive analytics. In this process students learn to answer four questions related to a smart factory using the right tools and techniques: what happened, why did it happen? what will happen and what actions to follow. With this goal, the course covers three modules: Industrial Big Data Management, Industrial Big Data Predictive Analytics, and industry-specific challenges and solutions. In the data management module, data gathering, storage, selection, transformation, preparation, and cleaning approaches are presented. The predictive analytics module covers a broad range of methods in unsupervised learning, supervised learning and semi supervised learning focusing on deep learning methods. The last module discusses challenges and solutions in predictive analytics for industrial data with a focus on cloud-based solutions. Examples are heterogeneous data integration, missing data imputation, learning from imbalanced data, and anomaly detection. Students will work with real-word industrial data during the semester and are assigned weekly practical assignments to gain hands-on experience. A course project lets students work in teams to enhance their skills in addressing immediate real-world problems and improve interpersonal skills through experiential learning.

COURSE-LEVEL EDUCATIONAL GOALS:

  1. Develop foundational knowledge of Big Data systems and programming. Understand big data collection, integration, and storage.
  2. Learn the required skill set for Big Data modeling and management systems.
  3. Understand basic concepts in Big Data integration and processing using Hadoop and Spark platforms.
  4. Design machine learning models and algorithms with Big Data including unsupervised learning, supervised learning and semi supervised learning focusing on deep learning methods.
  5. Develop a complete Big Data system using best practices such as design thinking methodology

Grading

  • Class Participation+ Activities 5%
  • Assignment #1 10%
  • Assignment #2 10%
  • Assignment #3 10%
  • Mid-Term Exam 25%
  • Quizzes 15%
  • Final Project 25%

NOTES:

The course has a strong analytical, applied and experiential orientation. Students in the course will gain hands-on experience with major Big Data tools and machine learning algorithms and their real-world applications. Core concepts and some introductory applications during the semester will be explored. The course starts from the foundations and gradually builds up to modern techniques. Since the greatest way to learn a Big Data management systems and machine learning algorithm is programming and doing experiments with it, the assignments are designed to implement the course concepts, and experimentation to test the algorithms on available dataset.

REQUIREMENTS:

  • Programming knowledge (Python preferred), fundamental statistics, and understanding of databases.
  • Weekly assignments, participation in class activities, midterm exam and a final team project.

Materials

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

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.