MATH 3007 Predictive Modelling

Credit Points 10

Legacy Code 301034

Coordinator Yi Guo Opens in new window

Description In this information age, business and science depend on accurate predictions to make informed decisions. Machine learning is the process of allowing a computer to learn from data, which at its heart is used in making these important decisions. This unit provides students with the knowledge and practice required to implement and effectively use these predictive models such as Neural Networks and Support Vector Machines. Students will use the Python programming language throughout this unit.

School Computer, Data & Math Sciences

Student Contribution Band HECS Band 1 10cp

Check your HECS Band contribution amount via the Fees page.

Level Undergraduate Level 3 subject

Pre-requisite(s) For students not enrolled in 3734 Bachelor of Data Science 3769 Bachelor of Data Science or 3770 Bachelor of Applied Data Science - MATH 1028 Statistical Decision Making or MATH 1003 Biometry or MATH 1030 Statistics for Business

Co-requisite(s) Students in Bachelor of Data Science or Bachelor of Applied Data Science must be enrolled in MATH 1033 Thinking About Data


The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.

Item Length Percent Threshold Individual/Group Task
Online Quizzes 40 minutes (per Quiz) 20 N Individual
Intra-session Exam 2 hours 30 Y Individual
Applied Project 1: Computer based Assignment - Data Analysis task 1000 words 10 N Group
Applied Project 2: Computer based Assignment - Data Analysis task 2000 words 40 N Group

Teaching Periods