MEEN41380 Industrial Data Analytics

Academic Year 2023/2024

This module provides insights into data analytics fundamental concepts, techniques/tools, and applications in an industrial environment. It is designed for 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.

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Curricular information is subject to change

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 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 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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Article Review Throughout the Trimester n/a Graded No

20
Group Project: Group Technical Report and Presentation Week 12 n/a Graded No

50
Assignment: Solution to A Challenge Throughout the Trimester n/a Graded No

30

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
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Nelson, G.S. 2018, The Analytics Lifecycle Toolkit: A Practical Guide for an Effective Analytics Capability, John Wiley & Sons, Incorporated, Newark.

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

Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl 2018;2017;, Data mining for business analytics: concepts, techniques, and applications in R, Wiley, Newark.

McKinney, W. 2012, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly Media Inc, Sebastopol.
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Autumn
      
Lecture Offering 1 Week(s) - 1, 4, 8, 12 Fri 13:00 - 14:50
Lecture Offering 1 Week(s) - 2, 3, 5, 6, 7, 9, 10, 11 Fri 16:00 - 17:50
Autumn