Module
Business Analytics and Research Skills
Module description
Data is the cornerstone of many businesses. The ability to make good business decisions relies on the availability, precision and application of data in a number of forms. For many enterprises, data and analysis is the business as they sell solutions, insights and intelligence to their clients and customers. This module builds your knowledge and understanding of business analytics; the main principles, ideas and approaches relating to the term; and the major issues associated with using analytics in practice. Although the module will increase your confidence with data, mainly quantitative in nature, the module makes no assumptions of your prior knowledge of, or training in, research methods.
Full module specification
| Module title: | Business Analytics and Research Skills |
|---|---|
| Module code: | BEMM389 |
| Module level: | M |
| Academic year: | 2023/4 |
| Module lecturers: |
|
| Module credit: | 15 |
| ECTS value: | 7.5 |
| Pre-requisites: | None |
| Co-requisites: | None |
| Duration of module: |
Duration (weeks) - term 1: 0 Duration (weeks) - term 2:12 Duration (weeks) - term 3:0 |
Module aims
The aim of this module is to develop your knowledge and skills in business analytics and the broader research skills that underpin them. Specifically, we will cover:
- The different types, roles and contexts of business analytics
- The main principles and practices behind different types of quantitative analytics
- Research design as the foundation for analytics and decision-making
- Data visualisation and infographics and their roles in communicating business analytics
ILO: Module-specific skills
- 1. Demonstrate understanding of the principles behind, and relative merits, of a range of analytical tools to source, process and evaluate business-related information
- 2. Explain recent trends in, and drivers for, the use and popularity of business analytics in organisational management and decision-making
- 3. Deploy in the correct context, different types of analytics and particular techniques to derive insights and inform business decision-making
- 4. Communicate analytics data and their meaning through visualisations and infographics to appropriate business audiences and stakeholders
ILO: Discipline-specific skills
- 5. Apply spreadsheet software to derive insights from analytics to address defined business challenges
- 6. Employ presentation software for visually reporting, and communicating the meaning of, findings from analytics on defined business issues
ILO: Personal and key skills
- 7. Conceptualise critical issues in business through alternative analytical prisms
- 8. Demonstrate enhanced technological and digital literacy
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 23 | 127 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching activities | 3 | Module introduction and basic content (in-class and online) |
| Scheduled learning and teaching activities | 10 | Whole cohort teaching events ('lectures') |
| Scheduled learning and teaching activities | 10 | Group workshops |
| Guided independent study | 50 | Preparation and consolidation (reading) prior to and after lectures and workshops |
| Guided independent study | 37 | Practice use of software and concepts from additional exercises and examples, and from curated online learning resources |
| Guided independent study | 40 | Assignment preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Individual quiz on key ideas | Max. 20 questions. 20 mins, available before reading week | 1-4 | Numeric (score). 50% indicative pass mark |
| Outline plan for assessed report | One page | 3,5-8 | Electronic/Verbal |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 70 | 0 | 30 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Multiple choice questions | 30 | 90 minutes, 20 questions | 1,2,3,7 | Electronic, written comments |
| Individual report | 70 | 2000 words | 3,4,5,6,8 | Electronic, written comments |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Multiple choice questions (30%) | Multiple choice questions | 1,2,3,7 | Referral/Deferral period |
| Individual report (70%) | Individual report (2000 words) | 3,4,5,6,8 | Referral/Deferral period |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to submit a further assessment as necessary. If you are successful on referral, your overall module mark will be capped at 50%.
Syllabus plan
Whilst the precise content may vary from year to year, it is envisaged that the syllabus will cover all or some of the following topics:
• Business analytics, business intelligence and decision-making: recent trends and future challenges
• Foundations of business analytics: research design, data processing and quality assurance
• Descriptive, diagnostic and predictive analytics
• Moving beyond the model: meaning and interpretation of analytics
• Communicating business analytics: data visualisation and infographics
• The potentials and pitfalls of particular analytics tools (e.g., Excel / SPSS / R / Python)
Indicative learning resources - Basic reading
- Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis & decision making. Cengage, 7th Edition
- Ledolter, J. (2013). Data mining and business analytics with R. Hoboken, NJ: Wiley
- Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons
Module has an active ELE page?
Yes
Indicative learning resources - Web based and electronic resources
None
Indicative learning resources - Other resources
None
Origin date
04/11/2019
Last revision date
16/05/2023