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

The module framework for each module will be made available to students before the start of the module lectures lectures on the learning platform SUNLearn. These contain all important information about the module content, structure, deadlines. etc.

The assessment structure for the data science modules offered by the Industrial Engineering department is listed below. Note: Modules offered by other departments can have a different structure. The departments which offer modules are listed in the timetable.

The data science modules will consist of five formal assessment opportunities – a pre-block assignment, a formative assessment opportunity during the lecture block week, and three post-block assignments. Each of these assessment opportunities 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 block week. 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. The lecture block week requires your full attention for the entire week. This is followed by 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

Note to our students: this list will be updated in Jan 2024 when our new lecturers arrive. Do not buy any books yet!

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