Business Analytics and Research Skills
| Module title | Business Analytics and Research Skills |
|---|---|
| Module code | BEMM389 |
| Academic year | 2020/1 |
| Credits | 15 |
| Module staff | Professor Joshua Ignatius (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 100 |
|---|
Module description
Making accurate causal claims is fundamental to successful business decisions. This module will teach you how to design, source and analyse both quantitative and qualitative business data to make fully informed decisions about the strategic management of an organisation or business. We will use interactive lecture and case study materials to build real world scenarios that will help you develop your abilities to use business analytics techniques to their maximum effect. In this module we will cover topics such as data collection, survey, experimental design and analytical skills to address the challenges facing business.
Internationalisation: the module will draw on recent scholarship in the areas of data and analytics published by researchers internationally (the UK, Europe, the United States) and case studies based on a variety of national contexts.
Employability: the module will offer an opportunity to acquire knowledge and develop analytical skills for a broad range of areas, e.g. strategy, planning and marketing.
Module aims - intentions of the module
The module aims to enhance your ability to model a business problem, as well as to collect, structure and analyse data for generating managerial implications.
Specifically we will consider:
- How to collect structured and unstructured data.
- How to conduct survey and launch an experimental design protocol.
- How to model business problems and derive solutions through data analytics approaches.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. critically evaluate current approaches used for collection, management, communication and analysis of structured and unstructured data, and how these results can support informed decision-making;
- 2. apply counterfactual reasoning to the analysis of a specific business challenge;
- 3. demonstrate familiarity with analytical tools available for the analysis of structured and unstructured data and use these to find, derive and evaluate information;
- 4. apply survey or experimental techniques to address challenges facing an organisation.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 5. design surveys or experimental approaches for addressing business challenges;
- 6. demonstrate the use of appropriate analytical techniques for identified business problems.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. critically reflect upon challenges within the analytics field;
- 8. demonstrate effective independent study and research skills.
Syllabus plan
- Introduction to decision making models and data analytics
- Correlation and Causality
- Data Collection Processes
- Conducting Surveys and Experiments
- Introduction to analytical tools, including R / Python
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 24 | 126 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activity | 3 | Online module introduction and basic underpinning theoretical content |
| Scheduled Learning and Teaching Activity | 11 | Whole cohort lecture |
| Scheduled Learning and Teaching Activity | 10 | Small group workshop |
| Guided Independent Study | 40 | Preparatory reading prior to workshops and lectures |
| Guided Independent Study | 40 | Practice use of software and concepts from additional exercises and examples |
| Guided Independent Study | 46 | Assignment preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Review of individual performance on group exercises | During workshops / tutorials | 1-8 | Verbal |
| Outline plan for assessed report | One page | 1-8 | Electronic/Verbal |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Practical (take-home) coursework exercise | 30 | 4 hours | 1-6, 8 | Electronic, written comments |
| Individual Report | 70 | 2000 words | 1-8 | Electronic, written comments |
| 0 |
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 |
|---|---|---|---|
| Practical (take-home) coursework exercise | Practical (take-home) coursework exercise (4 hours) | 1-6, 8 | August resit period |
| Individual Report | Individual Report (2000 words) | 1-8 | August resit period |
Indicative learning resources - Basic reading
- 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.
Indicative learning resources - Web based and electronic resources
- Kleinberg, S. (Ed.). (2019). Time and Causality Across the Sciences. Cambridge University Press. (Available as E-book through Encore)
| Credit value | 15 |
|---|---|
| Module ECTS | 15 |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 04/11/2019 |
| Last revision date | 09/09/2020 |
