Module
Business Analytics
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. You will be introduced to key concepts in data science for business, including fundamentals of data management, modelling, analysis and visualisation. You will consider how ‘design thinking’ can be used to effectively develop and deploy analytics systems within businesses, including the roles of descriptive, diagnostic, predictive and prescriptive systems. There are no pre-requisites but useful complementary modules to have taken in the first year include GEO1419 Introduction to Data Science.
Full module specification
| Module title: | Business Analytics |
|---|---|
| Module code: | BEP2140 |
| Module level: | 2 |
| 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 2: 11 |
Module aims
Businesses are constantly faced with making decisions and managers are expected to make the right decisions to optimise their operations. The aim of this module is to equip students with the ability to use variety of quantitative tools in analysing data to aid decision making in business. Students will explore the use of the different quantitative tools for analysing data in realistic business situations including finance, marketing, operations and other areas of business. On completion of the module, students will develop competencies in the use of analytic software particularly Excel for data analysis and data visualisations to support effective decision making.
ILO: Module-specific skills
- 1. Define business analytics (BA), related concepts and the importance of BA in a business environment
- 2. Conduct data analysis and construct data visualisations using basic and advanced excel functions to aid management decision making
- 3. Plan and execute quantitative analysis using descriptive, predictive and prescriptive analytical tools
- 4. Interpret the results of quantitative analysis and investigate potential issues
ILO: Discipline-specific skills
- 5. Critically evaluate the various factors which impact on effective management of analytics data when using descriptive, predictive and prescriptive analytical tools in a variety of business contexts
- 6. Effectively communicate findings to a variety of audiences
ILO: Personal and key skills
- 7. Example computer literacy and analytical skills
- 8. Display an understanding for decision making analysis in a business context
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 Activity | 11 | Lectures (11 x 1 hour) |
| Scheduled Learning and Teaching Activity | 11 | Tutorials (11 x 1 hour) |
| Guided Independent Study | 128 | Reading, research and assessment preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| In class quizzes and multiple choice exercises | During each class | 1-8 | Verbal in class |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 0 | 40 | 60 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Written exam | 40 | 1 hour open book exam | 1,5,8 | Written |
| Practical exam | 60 | 2 hours lab based, open book practical exam | 1-8 | Written |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 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 |
|---|---|---|---|
| Written exam (1 hour open book exam) (40%) | Written exam (1 hour open book exam) (40%) | 1,5,8 | August re-assessment period |
| Practical exam (2hr lab based, open-book practical exam) (60%) | Practical exam (2hr lab based, open-book practical exam) (60%) | 1-8 | August re-assessment period |
Syllabus plan
Topics discussed on the module include (not exclusively):
- Foundations of Business Analytics. Students will be introduced to basic concepts in business analytics including the use of basic data management and analytic software tools such as Excel.
- Descriptive Analytics. One important aim of business analytics, is to present meaningful and easy to understand information from the large data analysed. To achieve this, students will be introduced to data visualisation, descriptive statistics, probability and decision making under uncertainty.
- Predictive Analytics. Topics to be covered include times series and regression analysis, forecasting, use of spreadsheet to build and analyse data models.
- Prescriptive Analytics. Linear optimisation and its application.
- Making Decisions. Central idea of business analytics is to assist managers in decision making. Students will be introduced to the underlying philosophies, tools and techniques of decision analysis.
Indicative learning resources - Basic reading
The following books are a useful resource for this course:
- Evans, J.R., 2017. Business analytics, Global edition England: Pearson.
- Albright, S.C. and Winston, W.L., 2014. Business analytics: Data analysis & decision making. Cengage Learning.
Module has an active ELE page?
Yes
Indicative learning resources - Other resources
A more comprehensive bibliography will be available to students taking this course.
Origin date
11/05/2021
Last revision date
11/07/2021