WM9G1-15 Big Data and Analytics for Industry
Introductory description
This module aims to enable participants to understand the principles, challenges and opportunities that Big Data offers to technology-led (or engineering) organisations. The focus of the module will be primarily on the management implications, rather than technical specifics of a Big Data architecture and/or analytics (both of which are introduced). Following from this, the module will also focus on the visualisation of Big Data, and of the insights derived from Big Data analytics, to support management decision making in engineering contexts.
Module aims
This module aims to enable participants to understand the principles, challenges and opportunities that Big Data offers to technology-led (or engineering) organisations. This incorporates technological developments, strategy and management, as well as analytical methods to derive insights from data at scale. Participants will get the opportunity to develop hands-on experience with the latest technology, current best practices, to critically analyse a range of business scenarios, and implement sophisticated big data and digital analytics solutions
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Big Data Technologies
- Core Concepts of Big Data
- Data Warehouse Architecture
- Big Data Architecture
Analytics - Core Concepts of Analytics
- Decision Analytics
- Predictive Analytics
- Artificial Intelligence and Machine Learning
Decision Science & Visualisation - Key Topics in Decision Science
- Visual Communication
- Data Visualisation
- Data Visualisation Software
Big Data and Visualisation in Engineering Management - Practical Simulation of the Above Topics
Learning outcomes
By the end of the module, students should be able to:
- Critically evaluate the key differences between Big Data technologies and analysis methods and traditional approaches in engineering business management
- Critically evaluate real-world engineering scenarios/case studies and devise appropriate analytical solutions.
- Demonstrate a comprehensive understanding of the core concepts of visual communication and data visualisation.
- Practically implement analytics and optimistaion techniques in real-world settings
Indicative reading list
As Above
View reading list on Talis Aspire
Interdisciplinary
A mixture of technology/computing topics and business topics
International
Topics are of high demand internationally
Subject specific skills
Big data, analytics, visualisation, artificial intelligence, automation, data architecture
Transferable skills
Computing, statistics and modelling, team work, critical analysis
Study time
| Type | Required |
|---|---|
| Lectures | 12 sessions of 1 hour (8%) |
| Seminars | 8 sessions of 1 hour (5%) |
| Supervised practical classes | 10 sessions of 1 hour (7%) |
| Online learning (independent) | 15 sessions of 1 hour (10%) |
| Assessment | 105 hours (70%) |
| Total | 150 hours |
Private study description
No private study requirements defined for this module.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | |
|---|---|---|
| Big Data Analytics Presentation | 20% | 30 hours |
|
A presentation of analyses and visualisations of various datasets and recommendations on business actions from them |
||
| Post Module Assignment | 80% | 75 hours |
|
A business-style report discussing core topics in big data and engineering management |
||
Assessment group R1
| Weighting | Study time | |
|---|---|---|
| Post Module Assignment | 100% | |
|
A business-style report discussing core topics in big data and engineering management |
||
Feedback on assessment
Verbal and written feedback for in-module element. Written feedback and annotated scripts for post-module element
Courses
This module is Optional for:
- Year 1 of TWMS-H1S3 Postgraduate Taught Engineering Business Management (Full-time)