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  • Advanced Data Science for Innovation
MICROCREDENTIAL

Advanced Data Science for Innovation

Enrol now
$ 1,529.00

START DATE

02 April

MODE

 Online

DURATION

 6 wks

COMMITMENT

 Avg 14 hrs/wk
Enrol now

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Meet the expert

Dr Ayesha Ubaid

Dr Ayesha Ubaid

Ayesha has a PhD in machine learning and leads industrial research across sectors, including projects for Mizzen Group, AUB Insurance and Workforce Health Assessors etc. Currently, she is collaborating with Sydney Water on the ‘Water Usage Demand Forecasting for the Greater Sydney region' using weather and seasonal data.

Ayesha is a passionate educator with extensive teaching experience in data analytics, software engineering and programming. Her teaching approach emphasises practical application to solve real-world problems and the translation of industrial research and work. Her primary interests include data analytics, machine learning, deep learning, system quality assurance & testing, architecture design, and cloud computing.

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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. 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 2-credit point subject, Advanced Data Science for Innovation (36125) in the Master of Data Science and Innovation. This microcredential may qualify for recognition of prior learning at this and other institutions.

Digital badge and certificate digital badge example for UTS Open short courses

A digital badge and certificate will be awarded upon successful completion of the relevant assessment requirements and attainment of learning outcomes of the microcredential.  

Learn more about UTS Open digital badges.

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.

Price

Full price: $1,674 (GST free)*

Special price: $1,529 (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

Course purchase is subject to UTS Open Terms and Conditions. 

COVID-19 response 

UTS complies with latest Government health advice. Delivery of all courses complies with the UTS response to COVID-19.

Additional course information

Course outline

This course consists of weekly 2-hour evening classes (6-8pm) 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:

  • Introduction to machine learning engineering
  • Advance exploratory data analysis
  • Multi class classification analysis 1
  • Multiclass classification analysis 2
  • Clustering
  • ML model deployment.

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

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.

Requirements

Mandatory

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.

Desired

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 is not a mandatory requirement.

Discounts

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)

Please contact the team at support@open.uts.edu.au in order to secure your enrolment and 10% staff discount.

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 during checkout. If you are an alumni and have forgotten your UTS student number, email support@utsopen.uts.edu.au 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. 

Contact us

For any questions on enrolment or payment, please email support@open.uts.edu.au  

If you have a specific question about course content or requirements, please email td.learning@uts.edu.au

 

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

Book a session

Wed 02 Apr 2025-
Wed 07 May 2025
Expert: Dr Ayesha Ubaid
  • This course is delivered online. Click on the underlined sessions and hours total link below to reveal specific session details.
  • Online
  • 6 sessions, 12 hours total
    Date
    Time
    Location
    Notes
    Wed 02 April 2025
    6pm-8pm
    Online via Zoom
    Wed 09 April 2025
    6pm-8pm
    Online via Zoom
    Wed 16 April 2025
    6pm-8pm
    Online via Zoom
    Wed 23 April 2025
    6pm-8pm
    Online via Zoom
    Wed 30 April 2025
    6pm-8pm
    Online via Zoom
    Wed 07 May 2025
    6pm-8pm
    Online via Zoom
$ 1,529.00
Error occurred while adding to cart

Enrolments close Wednesday 19 March 2024 11.59pm AEDT. There may be assessments due in the week after the last session.

Wed 01 Oct 2025-
Wed 05 Nov 2025
Expert: Dr Ayesha Ubaid
  • This course is delivered online. Click on the underlined sessions and hours total link below to reveal specific session details.
  • Online
  • 6 sessions, 12 hours total
    Date
    Time
    Location
    Notes
    Wed 01 October 2025
    6pm-8pm
    Online via Zoom
    Wed 08 October 2025
    6pm-8pm
    Online via Zoom
    Wed 15 October 2025
    6pm-8pm
    Online via Zoom
    Wed 22 October 2025
    6pm-8pm
    Online via Zoom
    Wed 29 October 2025
    6pm-8pm
    Online via Zoom
    Wed 05 November 2025
    6pm-8pm
    Online via Zoom
$ 1,529.00
Error occurred while adding to cart

Enrolments close Wednesday 17 September 2025 11.59pm AEST. There may be assessments due in the week after the last session.

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UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.

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