Introduction to Business Analytics
| Module title | Introduction to Business Analytics |
|---|---|
| Module code | BEM2031 |
| Academic year | 2023/4 |
| Credits | 15 |
| Module staff | Dr Alison Harper (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 180 |
|---|
Module description
This module will explore the role of information and analytics in supporting the development of strategies, and the practical techniques managers can use to design effective information flows.
Information is the lifeblood of business. Companies that manage information effectively can improve efficiency, be more responsive to market opportunities, achieve competitive advantage and operate more sustainably. As businesses drive towards sustainable strategies, they are looking for better information to guide decisions. A critical next step is to build information systems and data analytics capabilities that will turn raw data into actionable insights. This will enable companies to more effectively identify which actions are achieving their goals, detect risk or opportunity early, evaluate possible outcomes, allocate resources to achieve greatest returns, and measure the true impact of products.
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 those pursuing careers in planning and analytics.
Module aims - intentions of the module
The module aims to enhance your understanding of the application of data in organisations, and to start the process of building your capability in designing, structuring, and analysing data.
Specifically we will consider:
- How businesses use data to build, understand and report on their activities
- How to apply current concepts in data and analytics to real examples
- The use of ‘Design Thinking’ to create information management systems
- The initial tools for analysing numbers and text
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 commercial, operational and sustainability data, and how this data is used to support decision-making.
- 2. Apply Design Thinking techniques to the analysis of a specific business challenge and use these to identify required information flows.
- 3. Use data visualisation techniques to share original content and insight with a general management audience
- 4. Demonstrate familiarity with analytical tools available for the analysis of numerical and textual data and use these to find, derive and evaluate information.
- 5. Discuss current developments and thinking in the information management industry, specifically around big data management, analytics, cloud and visualisation techniques.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 6. Describe key terms and concepts in data and information management and be able to apply these to a typical business situation
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. Critical and reflective thinking.
- 8. Demonstrate effective independent study and research skills
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:
- Introduction to key concepts in data and analytics and their application to business
- Practical aspects of data management
- Applications of analytics
- Introduction to analytical tools
- Introduction to data visualisations
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activities | 22 | Lecture / Workshop |
| Guided Independent Study | 20 | Lecture / Workshop |
| Guided Independent Study | 40 | Preparatory reading prior to workshops and lectures |
| Guided Independent Study | 20 | Practice use of software and concepts from additional exercises and examples |
| Guided Independent Study | 24 | Individual reading and study time for development of report critique. |
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 | n/a | Verbal/written (General written feedback) |
| Outline plan for assessed report | One page | n/a | 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 | Approx. 4 hours duration | 1-8 | Electronic, written comments |
| Individual report | 70 | 3000 words | 1-8 | 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 |
|---|---|---|---|
| Practical (take-home) coursework exercise | Practical (take home) coursework exercise - approx. 4 hours in duration | 1-8 | Referral/deferral period |
| Individual report | Individual report - 3000 words | 1-8 | Referral/deferral period |
Re-assessment notes
Deferral – if you have been deferred for any assessment you will be expected to submit the relevant assessment. 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 40%) you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of referral will be capped at 40%.
Where there are practical reasons why the original form of assessment on a module cannot be replicated for referral or deferral purposes, an alternative form of assessment must be used. Examples of when this approach is justified include where the original assessment relied on fieldwork, group work, access to specialist equipment, or input from visiting staff; or where the process of assessment throughout the module was intricate, involving many assessments. The method of reassessment should address as many of the module’s intended learning outcomes as is possible.
Indicative learning resources - Basic reading
A full reading pack is supplied to students for this module (on ELE)
Recommended book:
Provost, F. and Fawcett, T. (2013) Data Science for Business. Beijing: O'Reilly.
Seeing Theory. https://seeing-theory.brown.edu/
Web based and electronic resources:
R for Data Science: https://r4ds.had.co.nz/
R: https://www.r-project.org/
R-Studio: https://www.rstudio.com/products/rstudio/download/
R Swirl https://swirlstats.com/
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | BEE1025 Statistics for Business and Management or BEE1022 Introduction to Statistics or BEA1012 Introduction to Statistics for Accountants or MTH1004 Probability, Statistics and Data or MTH2006 Statistical Modelling and Inference |
| NQF level (module) | 5 |
| Available as distance learning? | No |
| Origin date | 23/03/2018 |
| Last revision date | 24/08/2023 |
