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Semester 2

Predictive Analytics for Business (BUST10145)

Subject

Business Studies

College

CAHSS

Credits

20

Normal Year Taken

3

Delivery Session Year

2023/2024

Pre-requisites

Visiting students must have completed at least 4 Business courses at grade B or above. This course cannot be taken alongside BUST08033 Business Research Methods I: Introduction to Data Analysis; BUST08032 Business Analytics and Information Systems; ECNM08016 Statistical Methods for Economics or MATH08051 Statistics (Year 2). We will only consider University/College level courses.

Course Summary

The course covers predictive analytics techniques for cross sectional and panel data to respond to the job market needs of quantitative skills. The methods studied are illustrated with empirical examples.

Course Description

This course aims at training students in the field of predictive analytics to respond to the job market needs using econometric techniques. To be more specific, this course covers five types of models: basic linear model, linear models accounting for endogeneity, panel data, models with limited dependent variables and duration models. It also covers practical issues in predictive analytics and how to address them. The course is organised around the following four main teaching blocks: - Block 1: Linear regression models for cross sectional data with and without endogeneity, and applications in business, finance and economics. - Block 2: Regression models for panel data and applications in business, finance and economics. - Block 3: Probit and logit models for discrete variables with applications in business, finance and economics. - Block 4: Duration models with applications in business, finance and economics. Teaching will take the form of weekly 2-hour class lectures and weekly 1-hour computer lab sessions. Students will learn how to use state-of-the-art predictive analytics tools in the context of practical problems faced by business managers. Some of the material covered in lectures and discussion sessions will be research-led and based on recent publications from the academic literature.

Assessment Information

Written Exam 70%, Coursework 30%, Practical Exam 0%

view the timetable and further details for this course