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Overview

Today's organisations operate on a global scale and collect huge volumes of data from multiple sources. Management recognises the need to learn how to gather and manage data, transform it into new insights, and translate those insights into effective front-line and strategic actions in order to have a better understanding … For more content click the Read More button below. This course will provide you with advanced business analytics methods for Predictive Analytics.  The methodologies related to Predictive Analytics are classified into Supervised, Unsupervised, and Reinforcement Learning. While our main focus is on employing supervised and reinforcement learning methods for predictive analytics projects, analysts may also utilize unsupervised learning techniques during a predictive analytics project to gain better understanding of the data and accelerate the predictive modelling process.  You will implement these methodologies using Python, which is widely used thanks to its large toolkit in data processing and analysis. Business case studies will be introduced alongside the technical aspects of Python coding. 

Conditions for Enrolment

Prerequisite: INFS2822

Delivery

In-person - Standard (usually weekly or fortnightly)

Multimodal - Standard (usually weekly or fortnightly)

Please note that the above delivery information is an indication on how this course may be delivered within the year.  Delivery mode(s) for each term can be viewed on other sources such as myUNSW and class timetable site.

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Fees

TypeAmount
Commonwealth Supported Students (if applicable)$1119
Domestic Students$6420
International Students$6420

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Commonwealth Supported Students:

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Where a Commonwealth Supported fee is displayed it does not guarantee that such places are available.

Pre-2019 Handbook Editions

Access past handbook editions (2018 and prior)
Pre-2019 Handbook Editions