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MEEN41380

Academic Year 2024/2025

Industrial Data Analytics (MEEN41380)

Subject:
Mechanical Engineering
College:
Engineering & Architecture
School:
Mechanical & Materials Eng
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Javad Zeinali
Trimester:
Autumn
Mode of Delivery:
Online
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module provides insights into data analytics fundamental concepts, techniques, and applications in an industrial environment. It is designed for Professional Learners and Engineering Management students to acquire fundamental skills to effectively engage in and manage data analytics projects. An important objective of this module is to explain key steps in data analytics.

About this Module

Learning Outcomes:

On successful completion of this subject the student will be able to:
1. Analyse real-time and historical data.
2. Demonstrate an in-depth understanding of predictive data analytics lifecycle and stages.
3. Demonstrate an understanding of different data analytics approaches.
4. Define main steps needed in data preprocessing.
5. Select appropriate data visualisation technique.
6. Select the most appropriate data analytics approaches tailored to the available dataset and project challenge.
7. Provide data analytics solution to address industry data-related challenges.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Specified Learning Activities

24
Autonomous Student Learning

72
Total

120

Approaches to Teaching and Learning:
Lectures; Group work; Critical thinking and writing; Problem-based learning; Self-directed learning; Presentation; Debate;

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Solution Overview Week 5 Graded No

30

 No
Assignment(Including Essay): Article Review Week 8 Graded No

20

 No
Individual Project: Technical Report or Presentation Week 12 Graded No

50

 No

Carry forward of passed components
Yes
 

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, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Kelleher, J. D., Mac Namee, B., & D'arcy, A. (2020). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT press.

Nelson, G. S. (2018). The analytics lifecycle toolkit: A practical guide for an effective analytics capability. John Wiley & Sons.

Han, J., Kamber, M. & Pei, J. (2011), Data mining: concepts and techniques, 3rd edn, Elsevier Science, Burlington.
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