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MICROCREDENTIAL

Applied Data Science for Innovation (online)

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 course

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, the course will also introduce you to agile project management, entrepreneurship and data citizenship that will best 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 course 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.

Course outline

The course will cover the following content:

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 learning objectives

By the end of the course 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.

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 is this course for?

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

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

Course information

Teaching and learning strategies

This course is offered in a series of six weekly, three hour interactive online sessions facilitated by a leading industry expert. Each session consists of a mix of course 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 their learnings on real-world business use cases.

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

Assessment criteria

Assessment task: Machine Learning Project

  • Type: Project
  • Group work: Individual
  • Weight: 50%.

Assessment task: Hackathon

  • Type: Report
  • Group work: Group and individually assessed
  • Weight: 50%.

Minimum requirements

  • Participants must achieve at least 50% of the course’s total marks and complete all assessments.
  • This course is designed for participants with some knowledge of programming, data analysis or statistics.

Please contact us at td.learning@uts.edu.au if you have any questions about this course or requirements.

Discount

A discount of 10% is available to UTS alumni or UTS staff enrolling in this microcredential. If you’re eligible for this discount, please ensure you have provided your UTS Student or Staff ID number in your UTS Open Profile (under 'A bit about you').

When signing up for the session, use the relevant voucher code to apply the discount to your cart:

  • UTS Student / Alumni: TDIalumni  (If you have forgotten your student ID number, please contact us at td.learning@uts.edu.au)
  • UTS Staff: TDIstaff

Please note that discounts cannot be combined and only one discount can be applied per person per course session.

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