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Advanced Data Analytics

Designed for professionals with some familiarity working with data, this microcredential combines state-of-the-art research and practical techniques in data analytics, covering the knowledge and capacity to initiate and conduct data mining research and development projects.

About this microcredential

Advanced Data Analytics develops skills in data classification and prediction through practical activities in decision tree induction, classification by support vector machine, ensemble methods and random forest, classification accuracy and identifying issues in prediction.

Building from the Data Analytics Foundations microcredential, this course enables an exploratory data visualisation and evaluation of results.

Key benefits of this microcredential

  • Build on your foundational data analytics skills without focusing on heavy maths or coding – this course uses a visual open-source platform (KNIME) to demonstrate and practice key concepts and models for participants without a programming background.
  • Learn to evaluate and choose between key machine learning methods and models through practical exercises.
  • Complete as a self-contained course, or as a potential pathway to future postgraduate study.

This microcredential aligns with the 2 credit point subject in the Master of Information Technology (C04295).

This microcredential may qualify for recognition of prior learning at this and other institutions.

Who should do this microcredential?

This microcredential is accessible to participants with some background in data or those who have completed the Data Analytics Foundations microcredential.

UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education.    


Full price: $1,595 (GST-free)*

*Price subject to change. Please check price at time of purchase. 

Enrolment conditions

COVID-19 response 

Additional course information

Course outline

This microcredential has been designed to provide you with an applied introduction to the field of data analytics, and an orientation to its different usages. It has been designed by the UTS Faculty of Engineering and Information Technology, leveraging the Faculty's unique expertise in the area of artificial intelligence.

In this course you will meet and work with a dedicated course facilitator, who supports your learning and engagement with the teaching resources designed by the Lead Academic and a team of experts in the Faculty of Engineering and Information Technology.

There are six modules, each featuring self-study materials and facilitated online sessions.

1. Decision trees

In this module, you will cover supervised and unsupervised machine learning, the classification and prediction process, decision trees and entropy and information gain.

2. Evaluating classifiers

In this module, you will learn how to evaluate a classifier through studying the confusion matrix and related measures, ROC curves and bias and variance decomposition.

3. K-NN algorithm

In this module, you are introduced to another classification and regression method. Throughout this module, you also will learn how to validate the outcomes of your models and will be introduced to some key concepts in the training of a model.

4. Ensemble methods and random forest

An ensemble method is a learning algorithm that consolidates several machine learning models into one model. This approach constructs the decisions by training multiple classifiers and then classifies new data points by taking a vote of each of the classifier predictions. By pooling the predictions of multiple machine learning algorithms, the outcome is generally a better prediction than using a single algorithm.

5. Support vector machines

In this module, you will learn about kernel methods and Support Vector Machines (SVMs). To understand the concept of an SVM, you first need to know some other basic terms which will be covered in this module.

6. Neural networks

In this module, you will be introduced to the concept of neural networks, their history and types and training algorithms of neural networks.


This course is delivered in a scheduled format over ten weeks.

Each week (during weeks 1 to 8) you will participate in an online session where you will have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.

Weeks nine and ten are planned to give you time to complete the final assignment, with support and scheduled Q&A sessions available.

Course delivery

This microcredential includes weekly, live, one-hour online workshops facilitated by an expert UTS academic supporting self-study and online learning activities. Case studies of real-world business operations will be used to illustrate applications of data mining techniques.

The workshop sessions focus on hands-on experience in data mining and data analytics tools, and the understanding and interpretation of the results. Regular formative quizzes throughout the microcredential will allow participants to gauge their progress.

Course learning objectives

Upon successful completion of this microcredential you will be able to apply data mining, pattern discovery, and analysis skills in predictive analytics of data sets.

You will be able to select an appropriate classifier or approach for a predictive analytics task and explain your approach and results to a peer.


The graded assessment task is an individual predictive analytics task. A set problem is to be solved through deployment of an allocated classifier and other methods of participants’ own choosing.

A written report describing the solution method and results is required to be submitted.

Length: 2,000 words

In order to pass the microcredential, participants must achieve an overall mark of 50% or more.



  • To complete this online course, you will need a personal computer with adequate internet access and sufficient software and bandwidth to support web conferencing. You will also require an operating system with a web browser compatible with Canvas, Zoom,Microsoft Teams and the open-source software, KNIME Analytics Platform.


09 May




10 wks


Avg 5 hrs/wk

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Have a question?

Lead Academic

Professor Paul Kennedy

Professor Paul Kennedy
Deputy Head of School, Teaching and Learning

Paul has a PhD in computing science. He joined UTS in 1999 and is Director of the Biomedical Data Science Laboratory in the UTS Centre for Artificial Intelligence. This centre is a strategic investment area within the university and has been externally evaluated as one of the top two ranked research groups in the university.

The mission of the Biomedical Data Science Laboratory is to use knowledge of the infrastructure to support decision making in biomedicine, most notably by assisting clinicians and biologists in cancer diagnosis and treatment.

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Book a session

Tue 09 May 2023 -
Tue 11 Jul 2023
Expert: Professor Paul Kennedy
  • Online via Zoom
  • Online
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