Module
Introduction to 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.
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.
Full module specification
| Module title: | Introduction to Business Analytics |
|---|---|
| Module code: | BEM2031 |
| Module level: | 2 |
| Academic year: | 2023/4 |
| Module lecturers: |
|
| Module credit: | 15 |
| ECTS value: | 7.5 |
| 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 |
| Co-requisites: | |
| Duration of module: |
Duration (weeks) - term 1: 0 Duration (weeks) - term 2:11 Duration (weeks) - term 3:0 |
Module aims
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
ILO: Module-specific skills
- 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
- 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
- 7. Critical and reflective thinking.
- 8. Demonstrate effective independent study and research skills
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 |
|---|---|---|
| Workshops | 11 x 2 hours | 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 |
| 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 |
|---|---|---|---|
| Practical (take-home) coursework exercise | Practical (take home) coursework exercise - approx. 4 hours in duration | 1-8 | August resit period |
| Individual report | Individual report - 3000 words | 1-8 | August resit period |
Syllabus plan
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
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/
Module has an active ELE page?
Yes
Origin date
23/03/2018
Last revision date
16/03/2022