Predictive Analytics I: Regression and combinatorial techniques

Learning Outcomes

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• predict the future trends in data.
• apply predictive modelling concepts in real world situations;

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• self-manage the development of learning and study skills;
• plan and control effectively for successful completion of a personal workload;
• use your analytic skills in problem solving.

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;

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

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.

Resources & Reading list

Textbooks

Kuhn M. and Johnson K (2018). Applied Predictive Modelling. Springer.

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.

Albright S. and Winston W.L. (2017). Business Analytics: Data Analysis & Decision Making (6th Ed). Cengage.

Hastie T., Tibshirani R. and Friedman J (2008). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

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.

Repeat Information

Repeat type: Internal & External