Postgraduate Course: Data and Analytics for Leaders (MBA) (CMSE11465)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course gives business students a familiarity with the breadth and depth of data-driven decision making and data analytics across business functions with special emphasis on integrating these practices to drive strategic imperatives. Students on this course will not specialise in detailed aspects of data management or analysis, but will focus on how to drive value from data, understand ethical considerations in data, as well as learn techniques for managing an organisation's preparedness for leveraging data to drive strategic decisions. |
| Course description |
Introduction
What is data?
What are analytics?
What senior leaders need to know
How we develop data
The principles of market research: external and internal
Basic techniques
Quals and quants
Functional use of data 1 - Marketing
Using databases
Understanding customers and customer profitability
Using data to develop the optimum marketing mix
Functional use of data 2 - Operations
Using data to optimise processes
Resource allocation
Aligning process to customer
Functional use of data 3 - HR
People analytics - using data to optimise hiring, career progression and employee satisfaction
Functional use of data 4 - Finance
Understanding how to interpret financial statements and reports.
Reading between the lines.
Data and strategy (1)
How can data inform strategic decisions?
For example, benefits/costs/risks of entering a new market, developing a cost reduction strategy, pursuing an acquisition strategy
Data and strategy (2)
Understanding the links between different elements of data - what are the relationships? Introduction to regression analysis. Balanced Scorecard approach
Presenting data to make the case
Simple data visualisation techniques
Bringing everything together
Simulation
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is only available to students on the MBA and EMBA programmes, or through our Executive Education programme. |
Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 5 (Sem 2) and beyond |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 32,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
66 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Feedback |
Formative feedback will be provided on all submitted coursework. In the first instance, feedback will be in writing. Additional feedback will be provided in-person if requested.
As per University guidelines, all feedback will be returned within 3 weeks of the submission date.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate an understanding of the role of data and analytics across a range of business disciplines to apply directly to strategic decisions.
- Assess the quality of data and its role in specific analytic techniques (e.g. descriptive, predictive, and prescriptive).
- Reflect critically on the ethical and workplace implications for the increased uses of data in business
- Communicate creatively and critically with expert and lay audiences regarding outcomes of data analysis projects through both visual and written methods
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Reading List
There will be one core texts for this course, indicative texts may include:
¿ Provost, F., & Fawcett, T. (2016). Data science for business (second edition). O¿Reilly Media.
¿ O¿Neil, C. (2017). Weapons of math destruction (underscores ethical issues in data) Penguin.
As well as book chapters from:
¿ West, D. M. (2018) The Future of Work: Robots, AI and Automation. Brookings Institute.
¿ Eisen, J.P., & (2016). People analytics in the era of big data.
However, given the range of the topics covered and the constantly changing state of things, book chapters, journal articles and research and media reports will be widely used.
All required readings will be available online. Students should be familiar with the University Library's electronic journals system. In addition, students will be expected to keep up-to-date with developments in the area through news media and business and organisational websites.
Examples of Relevant Peer-Reviewed Journals
Marketing ¿ Journal of Marketing Analytics
Strategy - European Journal of Business and Management
HR/OB ¿ International Journal of Human Resource Management
Operations Management ¿ European Journal of Operations Management
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Additional Information
| Graduate Attributes and Skills |
This course provides covers attributes relating to both UEBS graduate mindsets and skills.
Mindset Attributes cover enquiry and lifelong learning, personal development, and outlook and engagement.
Specifically:
Students will be introduced to cutting edge topics that in their nature include research and enquiry and require students to engage with the material to derive analytic insights.
Graduate skill attributes cover research, communication, personal autonomy, personal effectiveness, and communication: Undertake ethically and socially responsible evaluations of policy and practice. Students on the course will:
Research business problems through engagement with results of data analysis as independent learners.
Increase their familiarity and comfort with data analytics in business to encourage further engagement on the topic.
Communicate creatively and substantively with a variety of stakeholders.
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| Keywords | Not entered |
Contacts
| Course organiser | Mr Julian Rawel
Tel: (0131 6)50 8067
Email: |
Course secretary | Miss Alana Borthwick
Tel:
Email: |
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