MEEN41330 Data Analytics for Engineers

Academic Year 2023/2024

This application- and practice-based module is designed to provide students with the toolkit to conduct data analytics projects in engineering applications. Throughout the course, students will learn fundamental techniques to effectively work with data. Students will gain hands-on experience with common data analytics tools to analyze real-world datasets.

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

1. Critically evaluate how data analytic tools and techniques can be applied to gain insights to data sets with consideration of the appropriate social contexts
2. Develop competency in data visualization and selected data analytics tools
3. Analyze reviews and assessment of published data analytics research within the local and global contexts
4. Apply written and oral communication of technical results through presentations and reports
5. Examine the ethical concerns surrounding data and critique the use of data with reference to current issues in a multicultural society

Indicative Module Content:

Some topics covered by this module include data visualisation, regression, classification, and clustering. The module will also discuss decision-making with data, emerging topics in data analytics, and ethical issues.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning






Approaches to Teaching and Learning:
Lectures, individual and group work, presentation, critical writing.
Requirements, Exclusions and Recommendations
Learning Recommendations:

Basic statistics and programming is recommended.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Project: A final data analytics project will be assigned. Each group is expected to provide a written report, a presentation, and peer assessment. Week 12 n/a Graded No


Continuous Assessment: Discussion (in class or on Brightspace) Throughout the Trimester n/a Graded No


Assignment: Applying data analytics tools on a dataset. Varies over the Trimester n/a Graded No


Journal: Journal Article Review Throughout the Trimester n/a Graded No


Essay: Critical & Reflective Writing Week 12 n/a Graded No



Carry forward of passed components
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities

How will my Feedback be Delivered?

Not yet recorded.