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Applied Data Science for Innovation

Learn how to solve challenging business problems and drive innovation by mastering the essentials of machine learning with one of the industry’s leading data science experts.

About this microcredential

With the advent of a new digital era, businesses are facing increasingly challenging and complex problems. Smart organisations that can successfully leverage value from their data gain unparalleled competitive advantages – with machine learning widely used across many industries, such as financial services, healthcare and telecommunications.

Co-designed by renowned academics and industry partners from the UTS Master of Data Science and Innovation program, and delivered by one of the industry’s leading experts, Anthony So, this interactive course will teach you how to design and implement innovative machine learning solutions to solve complex real-world problems.

You will learn essential machine learning techniques in depth, such as regression, classification, and clustering. Featuring a uniquely transdisciplinary approach to learning, this microcredential will also introduce you to agile project management, entrepreneurship and data citizenship that will prepare you to tackle complex problems in the future, 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 to become well versed in implementing, optimising and maintaining machine learning solutions that can disrupt industries and change people’s lives for the better.

Whether you’re keen to unlock new career opportunities or help your organisation develop more strategic, innovative initiatives, this microcredential will help you master the latest thinking and best industry practices in data science and become an invaluable contributor in whatever field you work in.

Key benefits of this microcredential

This microcredential aligns with the 4 credit point subject, Applied Data Science for Innovation (36113) in the Master of Data Science and Innovation. 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.


Anthony explains the concept and intuition behind all algorithms very well and clearly. The weekly assignments are well paced, and gradually lead me to more and more advanced areas. The detailed and constructive feedback is tailored to me and my progress, which is something that other online classes I’ve done could not provide. I highly recommend this course to people who would like to get into ML field with none or little prior knowledge.

Kai-Ping Wang, Senior Software Engineer at Sandstone Technology


Full price: $2,648 (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 the microcredential:

Introduction to Python programming

  • Installation and setup
  • Data structures
  • Conditions and loops.

Running machine learning projects

  • Different types of learning
  • Machine learning approaches for tackling business problems
  • Machine learning and Agile methodology.

Exploring data

  • Descriptive statistics
  • Visualising data
  • Identifying and fixing issues
  • Feature engineering.

Machine learning algorithms

  • Univariate and multivariate linear regression
  • Logistics regression
  • K-Nearest Neighbors
  • Decision tree
  • Random forest
  • K-means.

Optimising machine learning

  • Model evaluation
  • Assessing underfitting and overfitting
  • Hyperparameter tuning
  • Model interpretation.

Deploying machine learning solutions

  • Lifecycle of machine learning models
  • Machine learning pipelines and artefacts
  • Machine learning as a service.

Course delivery

This course offers in a series of weekly, interactive online sessions facilitated by a leading industry expert. Each session consists 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:

  • Understand the different elements of machine learning
  • Manage a machine learning project end-to-end
  • Analyse datasets and propose relevant approaches
  • Assess model performance and make recommendations
  • Train machine learning models
  • Communicate and present results.


Assessment task one - Machine learning project

  • Type - project
  • Group work - individual
  • Weight - 60%.

Assessment task two - Hackathon

  • Type - report
  • Group work - group and individually assessed
  • Weight - 40%.



  • 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).


  • Exposure to programming, data analysis or statistics.


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. 


17 November




6 wks


Avg 14 hrs p/w

Meet the expert

Anthony So

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 Head of Data Science of one of the fastest-growing startups in the APAC region.

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

Wed 17 Nov 2021 -
Wed 22 Dec 2021
Expert: Anthony So, Associate Professor Tony Huang
  • This course consists of weekly 3hr evening classes over 6 weeks plus approx 2-3hrs weekly self-directed online learning activities.
  • Online
  • 6 sessions

Enrolments close Wednesday, 10 November 2021.

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