WM382-15 Business Analytics & Visualisation
Introductory description
With the ever growing mass of digital data available in every business, large amount of information remains hidden in the data. This information can now be extracted with the help of data analysis (descriptive analytics) to provide a sound and objective support for forecasting predictions (predictive analytics) and related decisions (prescriptive analytics) in business. Business analytics & visualization is the art and science of extracting information from business data and presenting it with suitable graphical tools to provide a sound and objective basis to the decision process.
Module aims
This module aims to provide students with the ability to create mathematical models of realistic managerial situations and use them to support the decision process. The module is also intended to give enough foundational concepts, so that students will be in a position to discuss with experts in the field about latest advances and their possible application to solve managerial situations
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
DESCRIPTIVE ANALYTICS - Introduction to basic business analytics with Excel; Data manipulation
PREDICTIVE ANALYTICS - Forecasting Methods & Measures of Forecast Error
PRESCRIPTIVE ANALYTICS - Linear programming
Learning outcomes
By the end of the module, students should be able to:
- Perform preliminary statistical analysis of data and evaulate results.
- Consolidate key insights and predictions by modelling time series data.
- Identify prescriptive analytics optimization problems in different settings.
- Communicate solutions effectively and efficiently to a critical audience of non-specialists.
Indicative reading list
Sharda, R. (2018) Business intelligence: managerial perspective. Fourth, Global edition.
Harlow, England: Pearson.
Cox, D. R. and Donnelly, C. A. (2011) Principles of applied statistics. Cambridge:
Cambridge University Press.
Ross, S. M. (2021) Introduction to probability and statistics for engineers and scientists.
Sixth edition. London, United Kingdom: Academic Press.
View reading list on Talis Aspire
Subject specific skills
Students will be able to demonstrate a high competency level in:
Data Analysis;
Data Visualisation;
Statistical Analysis;
Advanced MS-Excel Usage;
Transferable skills
Team Working;
Communication skills ;
Problem solving;
Study time
| Type | Required |
|---|---|
| Lectures | 18 sessions of 1 hour (20%) |
| Seminars | 6 sessions of 1 hour (7%) |
| Practical classes | 6 sessions of 1 hour (7%) |
| Work-based learning | 32 sessions of 1 hour (36%) |
| Online learning (scheduled sessions) | 3 sessions of 1 hour (3%) |
| Private study | 25 hours (28%) |
| Total | 90 hours |
Private study description
Explore advanced Microsoft Excel and related software suites for Business Intelligence applications.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Business analytics and visualisation portfolio assessment | 100% | 60 hours | Yes (extension) |
|
The portfolio assessment will consist of written reports and projects involving data analysis problems related to exploratory analysis, visualization, predictive and prescriptive analytics. |
|||
Feedback on assessment
Feedback will be given as appropriate to the assessment type:
– verbal formative feedback on lab activities related to in-module assessment.
– written summative feedback on post module assessments.
Courses
This module is Core for:
- Year 3 of DWMS-H652 Undergraduate Digital and Technology Solutions (Data Analytics) (Degree Apprenticeship)