Core Data Science Modules

Here, we discuss aspects about only the core Data Science modules presented by the Department of Industrial Engineering.

Module Framework

The module framework for each module will be made available to students before the start of the module lectures.


The data science modules will consist of five formal assessment opportunities – a pre-block assignment, a formative assessment opportunity during the lecture block, and three post-block assignments. Each of these assessment opportunity will account for 20% of the student’s final mark. The formative assessment may consist of one or more smaller assessments which will take place during the contact session. In order to successfully pass the module, a student need to achieve a final mark of 50% or above.

The pre-block assignment will be made available to students at least two weeks before the scheduled module lectures. The due dates of the post-block assignments will be set to allow for 1-2 weeks per assignment.

Take note that each of the core data science modules has one pre-block assignment which will require 20-30 hours of work, as well as three post-block assignments which will require around 30 hours of work each. Students will have about six weeks to complete these post-block assignments.

Prescibed Textbooks

Module Prescribed textbooks
Programming in R 774 Hands-On Programming with R by G Grolemund;
R for Data Science by H Wickham and G Grolemund;
Advanced R by H Wickham
Data Science (Eng) 774 & 874 The Data Science Design Manual by Steven S Skiena
Applied Machine Learning 774 & 874 Fundamentals of Machine Learning for Predictive Data Analytics, Algorithms, Worked Examples, and Case Studies by John D Kelleher, Brian Mac Namee, Aoife D’Arcy (Second Edition), MIT Press, 2021;
Computational Intelligence: An Introduction by AP Engelbrecht (Second Edition), Wiley\& Sons, 2007 (supplementary book)
Optimisation (Eng) 774 & 874 Metaheuristics – From design to implementation by El-Ghazali Talbi, Wiley & Sons, 2009;
Operations Research: Applications and Algorithms by WL Winston (Fourth Edition), Brooks/Cole, ISBN: 978-0-534-42362-9, 2003
Big Data Technologies (Eng) 774 & 874 Supplementary materials to be provided
Data Analytics (Eng) 774 & 874 To Be Determined
Deep Learning (Industrial Engineering 874) Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville, MIT Press, 2016;
Dive into Deep Learning