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Advanced Data Science for Innovation

Learn how to design and implement innovative solutions to challenging, real-world business problems using advanced machine learning concepts and techniques, with one of the industry’s leading data science experts.

About this microcredential

Take the next step in learning how to design and implement innovative solutions to complex problems using state-of-the-art machine learning algorithms and data science approaches.

Co-designed by renowned academics and industry partners from the UTS Master of Data Science and Innovation program and delivered one of the industry's leading experts, Anthony So, this interactive course will allow you to leverage the latest data science approaches and best practices to transform your approach to developing data-driven insights and solutions within any organisation.

In this microcredential, you will learn advanced machine learning concepts and techniques in depth, such as machine learning pipeline, versioning, gradient boosting and neural networks. You will also gain skills that help you better manage production-ready end-to-end solutions.

Featuring a uniquely transdisciplinary approach to learning, this course will give you advanced skills in tackling complex problems, providing transferrable skills across a broad range of industries, sectors and organisations.

This dynamic, innovative approach, combined with hands-on learning and practice will help you become well versed in implementing, optimising and maintaining advanced machine learning solutions that can disrupt industries or change people’s lives for the better.

Key benefits of this microcredential

This microcredential aligns with the 4 credit point subject, Advanced Data Science for Innovation (36114) in the Master of Data Science and Innovation (C04372).

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

Who should do this microcredential?

This microcredential is suitable for anyone interested in learning more about machine learning, such as:

  • Business analysts
  • Data analysts
  • Developers
  • Entrepreneurs
  • Project managers
  • Product owners.


Full price: $2,700 (GST free)*

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

Discounts are available for this course. For further details and to verify if you qualify, please check the Discounts section under Additional course information

Enrolment conditions

COVID-19 response 

Additional course information

Course outline

This course consists of weekly 3-hour evening classes (Wednesdays, 5.30pm-8.30pm) over six weeks, with participants also needing to undertake approx. 2-3 hours weekly self-directed online learning activities.

The following content will be covered during this microcredential:

Improving machine learning results

  • Imbalanced dataset
  • Multiple data sources
  • Feature engineering.

Advanced machine learning algorithms

  • Polynomial linear regression
  • Facebook prophet
  • Gaussian mixture model
  • Xgboost
  • Neural networks and deep learning with Pytorch.

Optimising machine learning models

  • Regularisation
  • Model interpretation
  • Automatic hyperparameter tuning.

Deploying machine learning solutions

  • Experiment tracking
  • Machine learning pipelines
  • Machine learning versioning.

Course delivery

This microcredential is offered through a series of weekly online sessions facilitated by an industry expert. Each session will consist of a mix of subject presentations and hands-on experience. Participants will be able to learn the theory behind machine learning algorithms and data mining techniques followed by practical workshops, where they will apply what they've learnt to real-world business use cases.

In between sessions, participants will be required to engage in individual and collaborative online activities designed to support the understanding of the machine learning algorithms and their application.

Course learning objectives

By the end of the microcredential you will be able to:

  • Manage a machine learning project end-to-end
  • Define relevant approaches for complex situations
  • Design and run experiments for machine learning
  • Train advanced machine learning models such as Xgboost or Neural Networks
  • Optimise a machine learning model
  • Build and automate a machine learning pipeline
  • Manage a machine learning model lifecycle.


Assessment task one - Machine learning project

  • Type - project
  • Groupwork - group and individually assessed
  • Weight - 50%.

Assessment task two - Kaggle competition

  • Type - report
  • Groupwork - individual
  • Weight - 50%.

Participants must achieve at least 50% of the course’s total marks and complete all assessments.



  • To complete this online course, you will need a personal computer with reliable internet access and an operating system with a web browser compatible with the UTS Canvas Learning Management System (LMS).


  • This course is designed for participants with some knowledge of programming, data analysis or statistics. Before enrolling, participants with more limited experience may like to consider completing the microcredential ‘Applied Data Science for Innovation’, although completion of that course is not a mandatory requirement for enrolment in ‘Advanced Data Science for Innovation’.


Discounts are available for this course as follows: 

  • UTS alumni/students 10% discount with voucher code: TDIalumni 
  • UTS staff 10% discount 

Discounts cannot be combined and only one discount can be applied per person per course session. Discounts can only be applied to the full price. Discounts cannot be applied to any offered special price. 

How to enrol and obtain your UTS staff discount (UTS staff)

How to apply your discount voucher 

  • If you are eligible for a UTS alumni or student discount, please ensure you have provided your UTS student number in your UTS Open Profile (under “A bit about you”). If you are an alumni and have forgotten your UTS student number, email with your full name, UTS degree and year of commencement.  
  • Add this course to your cart 
  • Click on "View Cart" (blue shopping trolley at top right of screen). You will need to sign in or sign up to UTS Open 
  • Enter your eligible code beneath the “Have a code?” prompt and click on the blue "Apply" button 
  • Verify your voucher code has been successfully applied before clicking on the blue "Checkout" button. 


This advanced course focuses on best practices, pipeline, automation, advanced algorithms so that students are prepared for challenging and complicated ML problems. It guided me through thinking deeply into why and when to use all available tools and algorithms, and how to explain and convince stakeholders that the decision is completely data driven.

I highly recommend this course to people who have a passion for ML or want to do ML in the right way.

Kai-Ping Wang, Senior Software Engineer at Sandstone Technology


03 February




6 wks


Avg 14 hrs p/w

Meet the expert

Course Lead Anthony So

Course Lead Anthony So
Head of Data Science

Anthony is a renowned leader in Data Science. He has extensive experience in solving complex business problems using Advanced Analytics and AI in different industries including financial services, media and telecommunications. He is currently the Chief Data Officer of one of the most innovate fintech start-ups.

He is also the author of several best-selling books on Data Science, Machine Learning and Deep Learning. He has also won multiple prizes at several hackathon competitions such as Unearthed, GovHack and Pepper Money. 

Anthony holds two Master’s Degrees in Computer Science and Data Science and Innovation. 

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Associate Professor Tony Huang

Associate Professor Tony Huang
UTS Academic Lead

Tony is the course director of Master of Data Science and Innovation at UTS TD School. He is a data visualisation researcher with expertise in visual analytics and human computer interaction and designs visualisations, user interfaces and interaction methods to combine data values with human intelligence for effective data exploration, communication and decision making. He has over 150 publications, including five books.

Tony is also interested in teaching inspired research and research inspired teaching, actively exploring and practicing innovative teaching methods for optimal student learning experience and performance. He is experienced in course curriculum design and subject development. Both of Tony's teaching and research projects have been supported by the Australian Research Council and Australian Government Office for Teaching and Learning, including prestigious and competitive ARC DP and LP projects. He gives keynote/plenary speeches, invited talks and seminars at conferences and universities worldwide.

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Book a session

Thu 03 Feb 2022 -
Thu 10 Mar 2022
Expert: Course Lead Anthony So, Associate Professor Tony Huang
  • Participants will also need to undertake approx. 2-3 hours weekly self-directed online learning activities.
  • Online
  • 6 sessions, 18 hours total

Enrolments are scheduled to close on Thursday, 20 January 2022, 11.59pm (AEDT).

Thu 16 Jun 2022 -
Thu 21 Jul 2022
Expert: Course Lead Anthony So, Associate Professor Tony Huang
  • Participants will also need to undertake approx. 2-3 hours weekly self-directed online learning activities
  • Online
  • 6 sessions, 18 hours total

Enrolments are scheduled to close on Thursday, 2 June 2022, 11.59pm (AEDT).

Thu 03 Nov 2022 -
Thu 08 Dec 2022
Expert: Course Lead Anthony So, Associate Professor Tony Huang
  • Participants will also need to undertake approx. 2-3 hours weekly self-directed online learning activities
  • Online
  • 6 sessions, 18 hours total

Enrolments are scheduled to close on Thursday, 20 October 2022. 11.59pm (AEDT).

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