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Applied Data Analytics

Designed for data science practitioners, or as a follow-up to our Advanced Data Analytics microcredential, this course focuses on the implementation and evaluation of the data mining and knowledge discovery process. Participants will be able to explore the current data analytics research landscape and focus on the deployment of results and industry applications.

About this course

Applied Data Analytics will develop the autonomy of participants to plan and implement a data mining project using the cross-industry standard process for data mining, known as CRISP-DM (the most common approach to data mining). From pre-processing, the deployment of results, representing patterns as rules, functions and cases, model deployment and industry applications, this practical, problem-based course will demonstrate analytics expertise and the professional communication of analytics.

Course outline

More information on course content

During this course, you will meet and work with a dedicated course facilitator who will support your learning and engagement with teaching resources designed by the lead academic and team of experts from the Faculty of Engineering and IT.

The course is structured into five modules. Each module comprises self-study materials and facilitated online sessions. The five modules and key topics covered are:

  • Module 1 - Feature selection

This module introduces feature selection in data pre-processing. Participants will learn to pre-process noisy and unreliable data sets to remove irrelevant or redundant information and explore feature ranking to aid data-driven decision making.

  • Module 2 - Hyper-parameter optimisation

This module covers parameter optimisation models to minimise error and maximise accuracy. We will cover approaches such as brute force, hill climbing, random search and grid search.

  • Module 3 - Knowledge discovery process

Participants will learn about intelligent data management and data mining project development. We will cover the process of whiteboarding data products, feature engineering and systems engineering in data mining.

  • Module 4 - Research landscape in data analytics

This module introduces the current landscape in data analytics research. Key topics we will explore include the challenges of big data and big data architectures, information provenance and intelligent systems and computational intelligence.

  • Module 5 - Industrial applications

In this module we will apply and evaluate the models and approaches learnt through this course, via examples and case studies from a range of sectors, including, but not limited to, healthcare, agriculture, telecommunications, education, finance and transportation.

This course is delivered in a scheduled format over ten weeks.

Each week (during weeks 1 to 8) you will participate in an online session where you will have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.

Weeks nine and ten are planned to give you time to complete the final assignment, with support and scheduled Q&A sessions provided.

Key benefits of this microcredential

  • Learn to evaluate, implement and communicate professionally on best-practice and research-driven approaches to data analytics projects
  • Highly industry-focused; cover examples from a range of sectors, focusing on data-driven decision making
  • Complete as a self-contained course, or as a potential pathway to future postgraduate study.

This microcredential aligns with the two-credit point subject, Applied Data Analytics (42823) in one of the following postgraduate offerings:

  • Graduate Certificate of Professional Practice (C11298)
  • Graduate Diploma of Professional Practice (C06136)
  • Master of Professional Practice (C04404)
  • Graduate Certificate of Technology (C11301)
  • Graduate Diploma of Technology (C06137)
  • Master of Technology (C04406).

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

Who is this course for?

This course is suitable for professionals from a wide range of sectors and backgrounds, who have completed our Data Analytics for Foundations and Advanced Data Analytics microcredentials or elsewise have professional experience in the field.

UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education.

Course information

Course learning objectives

Upon successful completion of this course, you will be able to implement a data mining project in a business environment.   


The graded assessment task is a data mining project using enterprise-specific or open-source data. The project is self-directed, with the support of the academic. It is an iterative process, including business understanding, data understanding, data preparation, modelling, evaluation and deployment. A written report will be required to be submitted, describing the plan, approaches deployed, results and your reflection on the in-class oral feedback of the approach.

Length: 2,000 words      

Teaching and learning strategies

This course includes weekly, live, one-hour online tutorials and one-hour weekly Q&A sessions facilitated by an expert UTS academic supporting self-study and online learning activities.

Case studies of real-world business illustrate applications of data mining techniques. The workshop sessions focus on hands-on experience in data mining and data analytics tools and understanding and interpretation of the results.

Regular formative quizzes throughout the microcredential will allow participants to gauge their progress.              

Enrolment requirements

This microcredential is available to participants who have completed our Data Analytics Foundations and Advanced Machine Learning microcredentials, or who can demonstrate an equivalent level of professional experience or prior study.

Participant requirements and equipment

To complete this online course, you will need a personal computer with reliable Internet access, web conferencing capability and an operating system with a web browser compatible with the Canvas LMS. You will need to be able to run a currently-supported version of KNIME or Python. 



01 February




10 wks


5 hrs p/w

Book a session

Mon 01 Feb 2021 -
Fri 09 Apr 2021
  • 1 February - 9 April 2021
  • Online
Mon 05 Apr 2021 -
Fri 11 Jun 2021
  • 5 April - 11 June 2021
  • Online
Mon 07 Jun 2021 -
Fri 13 Aug 2021
  • 7 June - 13 August 2021
  • Online
Mon 02 Aug 2021 -
Fri 08 Oct 2021
  • 2 August - 8 October 2021
  • Online
Mon 04 Oct 2021 -
Fri 10 Dec 2021
  • 4 October - 10 December 2021
  • Online
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