WM9A5-15 Digital & Data Science Consultancy
Introductory description
Digital and Data Science technologies have grown significantly in the last decade in terms of both organisational adoption and their importance to operational practices. Equally, however, such technologies have grown in complexity and sophistication. As a consequence, many organisations can struggle to identify the opportunities available to them through digitalisation and machine learning, and to effectively implement and optimise these solutions.
In such a climate, there is an increasing demand for eBusiness professionals who have both a thorough technical understanding of digital/data technology, and the ability to effectively communicate with key stakeholders to build comprehensive requirements and design successful implementations. These professionals can be external to the organisation (e.g. management consultancy firms), but there is also similar demand for staff to act as internal-consultants.
Module aims
The principal aim of the module is to give participants exposure to the varied workload and challenges associated with delivering digital and data science projects in the real world. Alongside the taught curricula, participants will engage in a hands-on simulation of a consultancy project to incorporate all of the key elements and milestones - from client requirements and requirements illication, through to data exploration and initially analyses, before delivering a final, full project plan and timeline.
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.
What is Digital & Data Science Consultancy?
- Business Analysis and Requirements a. Business Analysis b. Systems Thinking c. Requirements Gathering and Elicitation
Project Scoping and Engagement Management
- Project Scoping and Design
- Project Management for Consultancy Projects
- Digital Solutions for Consultancy Projects
Solution Design and Implementation
- Determining the Problem/Solution Space
- Decision Science & Multi-Criteria Decision Analysis
- Data analysis and solution matching
- Solution Implementation
- Change Management
Capstone Project a. Industry Case Study b. Digital Consultancy Simulation c. Client Presentations
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a comprehensive understanding of digital and data science consultancy concepts
- Interpret and evaluate complex organisational problems and requirements
- Critically analyse advanced data technologies and evaluate their suitability to specific use cases
- Demonstrate a sound conceptual understanding of the forefront of digital & data science consultancy practice and their application in real-world scenarios
- Critically evaluate the systematic and operational risks associated with digital transformations and develop comprehensive mitigation strategies
Indicative reading list
View reading list on Talis Aspire
Interdisciplinary
A mixture of technology/computing topics, statistics, and business topics
International
Topics are of high international demand
Subject specific skills
Digital transformation, multi-criteria decision analysis, soft systems and decision analysis, consultancy practice, project planning
Transferable skills
Consultancy skills, project management, communication skills, teamwork
Study time
| Type | Required |
|---|---|
| Lectures | 14 sessions of 1 hour 30 minutes (14%) |
| Seminars | 10 sessions of 1 hour 30 minutes (10%) |
| Practical classes | 6 sessions of 1 hour 30 minutes (6%) |
| 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 A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Retrospective | 20% | 30 hours | No |
|
A presentation/demonstration of the achieved work and a reflection on the project plan and how it can be improved |
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| Post Module Assingment | 60% | 45 hours | Yes (extension) |
|
A business-style report discussing core topics in digital & data science and consultancy |
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| Creation of online project tools | 20% | 30 hours | No |
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Development of online tools such as Kanban boards, wikis and similar to support the project plan |
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Assessment group R
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Post Module Assingment | 100% | Yes (extension) | |
|
A business-style report discussing core topics in digital & data science and consultancy |
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Feedback on assessment
Verbal feedback for in-module element. Written feedback and annotated scripts for post-module element
Courses
This module is Optional for:
- Year 1 of TWMS-H1S4 Postgraduate Taught e-Business Management (Full-time)