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  • Applied Data Science for Innovation
MICROCREDENTIAL

Applied Data Science for Innovation

Enrol now
$ 1,529.00

START DATE

04 June

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 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

This interactive course will teach you how to design and implement innovative machine learning solutions to solve complex real-world problems. 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.

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.

This course has been co-designed by renowned academics and industry partners from the UTS Master of Data Science and Innovation program. 

Key benefits of this microcredential

This microcredential aligns with the 2-credit point subject, Applied Data Science for Innovation (36124) 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.

 

The concepts and intuition behind all algorithms were explained very 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

Price

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

Special price: $1,529 (GST-free)*

This course is set at full price for the duration of our Black Friday sale 12:01am 25/11/2024 to 11:59pm 08/12/2024. Use SAVE20 at checkout to save 20% off selected courses. See https://open.uts.edu.au/campaign-tcs/

*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 data science and machine learning.
  • Introduction to exploratory data analysis
  • Data visualisation
  • Regression analysis
  • Classification analysis
  • Model serving.

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

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%.

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

Exposure to programming, data analysis or statistics.

Discounts

Discounts are available for this course as follows: 

  • UTS alumni/students 10% discount with discount 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 code 

  • 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 discount code?" prompt and click on the blue "Apply" button 
  • Verify your discount 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. 

Book a session

Wed 04 Jun 2025-
Wed 09 Jul 2025
Expert: Dr Ayesha Ubaid
  • Online via Zoom. 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 04 June 2025
    6pm-8pm
    Online via Zoom
    Wed 11 June 2025
    6pm-8pm
    Online via Zoom
    Wed 18 June 2025
    6pm-8pm
    Online via Zoom
    Wed 25 June 2025
    6pm-8pm
    Online via Zoom
    Wed 02 July 2025
    6pm-8pm
    Online via Zoom
    Wed 09 July 2025
    6pm-8pm
    Online via Zoom
$ 1,529.00
Error occurred while adding to cart

Enrolments close on Wednesday 21 May 2025 11.59pm AEST. There may be assessments due in the week after the last session.

Wed 13 Aug 2025-
Wed 17 Sep 2025
Expert: Dr Ayesha Ubaid
  • Online via Zoom. 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 13 August 2025
    6pm-8pm
    Online via Zoom
    Wed 20 August 2025
    6pm-8pm
    Online via Zoom
    Wed 27 August 2025
    6pm-8pm
    Online via Zoom
    Wed 03 September 2025
    6pm-8pm
    Online via Zoom
    Wed 10 September 2025
    6pm-8pm
    Online via Zoom
    Wed 17 September 2025
    6pm-8pm
    Online via Zoom
$ 1,529.00
Error occurred while adding to cart

Enrolments close on Wednesday 30 July 2024 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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