Mini Map

Predictive analytics and machine learning

ETX3250

Synopsis

Many problems in business including sales and inventory forecasting, credit scoring, recommender systems in online commerce and fraud detection use advanced tools for data analytics. This unit covers some of the most popular tools that may include tree-based methods, boosting, bagging, support vector machines, neural networks and deep learning. The algorithmic details of each method, their implementation using popular software tools (such as R) and their application to real business problems will all be covered.

Sourced from the Monash Handbook 2026.

Quick facts

Credit points
6
Level
3
Audience
Undergraduate
Type
Coursework
School
Faculty of Business and Economics
Faculty
Department of Econometrics and Business Statistics
Handbook year
2026

Prerequisites (5)

What it unlocks (2)

Offerings (1)

  • First semesterCaulfield · BLENDED

Listed in 2 areas of study

  • Business analytics and statisticsCore units
  • Business analytics and statisticsSpecified discipline electives