Business Analytics in Practice
| Module title | Business Analytics in Practice |
|---|---|
| Module code | BEM2039 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Livio Fenga (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 12 |
| Number students taking module (anticipated) | 60 |
|---|
Module description
This module will introduce the key concepts and ideas about how business analytics is used in practice. It will highlight the importance of data to organisations and how it can be used to improve firm performance in relation to competitors, customers, employees and processes. The module will introduce you to how data and analytic methods can be used to support an organisation’s overall strategy. The module will also consider ethical, regulatory and security issues associated with data and its use within a business environment
Module aims - intentions of the module
Business analytics, data science, digital economy, big data – with buzzwords coming and going it can be a complex and confusing picture, and difficult to know where to start. This module aims to introduce key topics that you’ll need to explore in more depth as you proceed on your ‘business analytics journey’.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. identify and apply appropriate analytics methods and tools to a range of business situations;
- 2. demonstrate appreciation of contemporary issues in management and use of data - including ethics, governance, and change management.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. communicate effectively through oral presentations and written reports, presenting methodologies and findings in a way that is appropriate to the intended audience.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 4. demonstrate a collaborative mind-set: Our graduates are enterprising and motivated individuals who are able to actively collaborate and effectively communicate within a range of diverse settings;
- 5. demonstrate a global outlook: Our graduates are engaged and prepared for the demands of global business and society;
- 6. demonstrate an ethical ethos: Our graduates understand the social, financial and environmental factors that can impact on corporate sustainability and are able to make decisions openly and responsibly;
- 7. epitomise a critical thinker: Our graduates have a commercial awareness that enables them to critically analyse, conceptualise and evaluate the challenges facing business.
Syllabus plan
The following content will be covered during the course
- ‘The fourth industrial revolution’, analytics and data science in business
- Data ethics, governance and professional responsibility
- Analytics and data science processes and frameworks
- Introduction to key data science & modelling concepts
- Introduction to tools commonly used in analytics and data science
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 36 | 114 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activity | 36 | Scheduled lectures and workshops |
| Guided Independent Learning | 60 | Background and preparatory reading |
| Guided Independent Learning | 54 | Preparation for assessments |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| In-class quizzes and exercises | In-class | 1-5 | Verbal |
| Group presentation | In-class | 1,2,5,6 | Verbal |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 60 | 0 | 40 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Group presentation (video) | 40 | 15 minutes | 1,2,3,4,5,6 | Written |
| Individual coursework | 60 | 2000 words | 1,2,3,7 | Written |
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 |
|---|---|---|---|
| Group presentation | Individual presentation 15 mins (40%) | 1,2,3,4,5,6 | August/September Reassessment Period |
| Individual coursework | Individual coursework (2000 words) (60%) | 1,2,3,7 | August/September Reassessment Period |
Indicative learning resources - Basic reading
Guided reading will be provided via module ELE pages in advance of scheduled lectures and workshops. The following resources, available online via the University Library, are indicative of the type and level of information you should expect to review.
Marr, B. (2015) Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. Wiley
Ford, M. (2018) Architects of intelligence: the truth about AI from the people building it. Packt Publishing
Marr, B. (2017) Data strategy: how to profit from a world of big data. Kogan Page.
Provost, F & Fawcett, T. (2013) Data science for business. O’Reilly.
You should also expect to review materials from academic journals such as Management Science (INFORMS) and Journal of Business Analytics (Taylor and Francis), and publications representative of current analytics practice within industry and government.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 5 |
| Available as distance learning? | No |
| Origin date | 06/01/2020 |
| Last revision date | 01/01/2022 |
