Module overview
Predictive modelling offers a lot of benefits to organisations: it can help them to improve their business decisions and which in turn will have a huge impact on their business and its profits. As a result, there is a huge demand for persons with predictive modelling skills in various organisations across the globe. In this module, you will learn techniques to determine patterns and to make predictions about future trends from the data, including regression modelling, clustering analysis and Principal Component Analysis (PCA).
Linked modules
Pre-requisites: MANG1047 or MANG1019
Aims and Objectives
Learning Outcomes
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- use your analytic skills in problem solving.
- self-manage the development of learning and study skills;
- plan and control effectively for successful completion of a personal workload;
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- interpret results of different types of predictive models and explain the value of the results in different business contexts.
- use predictive analytic techniques to handle a variety of business problems;
- apply a range of regression modelling and forecasting techniques using real world problems;
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- criteria for choosing the appropriate predictive techniques for particular problems.
- a range of statistical techniques for modelling and predicting future trends;
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- apply predictive modelling concepts in real world situations;
- predict the future trends in data.
Syllabus
What is predictive modeling? How to build a predictive model and techniques (algorithms)
An overview of regression analysis, the correlation coefficient
Simple linear regression
Estimation and prediction using regression equation
Multiple linear regression; making predictions
Logistic regression, logit model
Introduction to Time Series
Clustering analysis: data mining tool; concepts, definitions (k-means)
One-way analysis of variance (ANOVA)
Classification trees
Learning and Teaching
Teaching and learning methods
The module will be delivered in a lecture-based environment followed by intense tutorials conducted on small groups. The lectures will be interactive which will be based on active learning approach. The concepts of predictive modelling will be elaborated with the help of some statistical package output. Moreover, in the tutorials, students will have a chance to apply the concepts taught in lectures in an applied setting while most of the time working in small groups.
There will be 16 lectures of one hour each supplemented by 20 hours of tutorials.
Type | Hours |
---|---|
Completion of assessment task | 60 |
Revision | 12 |
Tutorial | 20 |
Lecture | 16 |
Wider reading or practice | 30 |
Preparation for scheduled sessions | 12 |
Total study time | 150 |
Resources & Reading list
Textbooks
Albright S. and Winston W.L. (2017). Business Analytics: Data Analysis & Decision Making (6th Ed). Cengage.
Kuhn M. and Johnson K (2018). Applied Predictive Modelling. Springer.
Hastie T., Tibshirani R. and Friedman J (2008). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.
Urdan, T.C. (2010). Statistics in Plain English (3rd Edition). Taylor & Francis Group.
Evans, J. R. (2013). Business Analytics. Methods, Models and Decisions. Pearson Education.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
Class participation
- Assessment Type: Formative
- Feedback: •Verbal formative feedback will be given by the lecturer/tutor throughout the module during lectorials and during one-to-one sessions during office hours. •Written feedback will also be provided by the lecturer/tutor through email communications. •Peer-to-peer feedback will also be key for developing and supporting learning objectives. •Students’ knowledge will be used formative tests.
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Examination | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Examination | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Examination | 100% |
Repeat Information
Repeat type: Internal & External