Postgraduate Course: Data and Analytics for Leaders (CMSE11603)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
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. |
| Course description |
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.
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
Specifically, with respect to AASCB, the course responds to calls for technology agility as demonstrated through:
1. Evidence-based decision making that integrates current and emerging technologies, including the application of statistical tools and statistical techniques, data management, data analytics and information technology throughout the curriculum as appropriate.
2. Ethical use and dissemination of data, including privacy and security of data
3. Understanding of the role of technology in society, including behavioural implications of technology in the workplace
4. Demonstration of technology agility and a ¿learn to learn¿ mindset, including the ability to rapidly adapt to new technologies.
5. Demonstration of higher-order cognitive skills to analyse an unstructured problem, formulate and develop a solution using appropriate technology, and effectively communicate the results to stakeholders.
Pre-recorded lectures and online tutorials are supported via independent directed reading
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 16,
Online Activities 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
72 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Individual assignment |
| Feedback |
A minimum of one piece of formative feedback will be provided per course. This may be offered asynchronously via discussion boards and emails, and synchronously in tutorials.
Feedback on assignments will be provided within 15 working days of submission. |
| 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
Provost, F., & Fawcett, T. (2016). Data science for business (second edition). OReilly Media.
ONeil, C. (2017). Weapons of math destruction (underscores ethical issues in data) Penguin |
Additional Information
| Graduate Attributes and Skills |
C1 Meaningful Interpersonal Interaction
C2 Effective Emotional Intelligence
C3 Authentic Leadership
C4 Ethical, Responsible and Sustainable Business Behaviour
C5 Appropriate Communication
C6 Understand and Make Effective Use of Data
C7 Creative and Entrepreneurial Practice
C8 Personal and Professional Competence
C9 Academic Excellence
C10 Intellectual Curiosity
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| Keywords | Not entered |
Contacts
| Course organiser | |
Course secretary | Ms Sarah Yaxley
Tel: (0131 6)50 3475
Email: Sarah.Yaxley@ed.ac.uk |
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