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Data Analytics Foundations

$ 1,595.00


17 September




10 wks


Avg 5 hrs/wk

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Lead Academic

Professor Paul Kennedy

Professor Paul Kennedy
Deputy Head of School, Teaching and Learning

Paul has a PhD in computing science. He joined UTS in 1999 and is Director of the Biomedical Data Science Laboratory in the UTS Centre for Artificial Intelligence. This centre is a strategic investment area within the university and has been externally evaluated as one of the top two ranked research groups in the university.

The mission of the Biomedical Data Science Laboratory is to use knowledge of the infrastructure to support decision making in biomedicine, most notably by assisting clinicians and biologists in cancer diagnosis and treatment.

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Designed for professionals who are new to working with data, this microcredential covers the fundamentals of data analysis. Understand the value and power of data, master key concepts and terminology, explore clustering and analysis techniques and begin analysing and visualising a range of data sets.

About this microcredential

Data Analytics Foundations introduces participants to the significance and language of data analytics for business and society. The participant will be introduced to the cross-industry standard process for data mining (CRISP-DM), the most common approach to data mining.

This microcredential offers practice in the foundations of data analytics, including identifying data set and attribute types, data preparation and cluster analysis. Advanced techniques for clustering will help develop skills in identifying problems for cluster analysis and a range of approaches to address these limitations. Applying these data analytics techniques enables interpretation of a data set and visual data exploration.

This course uses the KNIME analytics platform which is suitable for non-coders.

Key benefits of this microcredential

  • Get started in data science without the heavy maths or coding – this course uses a visual, open-source platform (KNIME) to demonstrate and practice key concepts and models for those without a programming background.
  • Learn both important context and models and how to apply key techniques with practical exercises.
  • Complete as a self-contained course, or as a potential pathway to future postgraduate study.

This microcredential aligns with the 2-credit point subject, Data Analytics Foundations (42821) in the Master of Information Technology (C04295).

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 accessible to professionals from a wide range of sectors and backgrounds who are new to working with data.

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


Full price: $1,595.00 (GST-free)*

*Price subject to change. Please check price at time of purchase. 

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 has been designed to provide you with an applied introduction to the field of data analytics, and an orientation to its different usages. It has been designed by the UTS Faculty of Engineering and Information Technology, leveraging the Faculty's unique expertise in the area of artificial intelligence.

In this course, you will meet (virtually) and work with a dedicated course facilitator, who will support your learning and engagement with the teaching resources prepared by the academic team and the lead academic.

This course uses the KNIME analytics platform which is suitable for non-coders.

This course is structured into five modules. Each module includes self-study materials and facilitated online sessions.

Module 1 - Introduction to data analytics

  • In this module, you are introduced to the basics of data analytics. In the weekly live and online sessions, we will review the material and unpack how data analytics can be further applied.

Module 2 - Know your data

  • In this module, we will go through the definition of data, types of data, instances and attributes of the dataset, data quality issues and methods of data collection.

We will also learn about the standard process of data mining in detail with a real-world business scenario.

Module 3 - Data pre-processing

  • In this module, you are introduced to the concept of pre-processing, its techniques, and the effect of pre-processing in transforming the data into a more understandable form.

Module 4 - Data exploration and visualisation

  • Data visualisation represents data in a graphical form that is easy to understand as "a picture is worth a thousand words". It is particularly useful when we are trying to understand data, its trends and outliers.
  • Data visualisation allows sharing unbiased representations of data which can be particularly helpful in making recommendations to stakeholders.

Module 5 - Data clustering

  • In the first part of this module, you will be introduced to the clustering concept, methods, requirements and types of clustering. In the second part, you will be introduced to one of the most famous of clustering methods, K-Means Clustering. 


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.

Course delivery

This microcredential includes weekly, live, one-hour online workshops facilitated by an expert UTS academic supporting self-study and online learning activities. Case studies of real-world business operations will be used to illustrate applications of data mining techniques. The workshop sessions focus on hands-on experience in data mining, data analytics tools and understanding and interpretation of the results. Regular formative quizzes throughout the microcredential will allow participants to gauge their progress.

Course learning objectives

Upon successful completion of this microcredential you will be able to apply pre-processing, transformation and visualisation to business data sets.

You will be able to interpret a contextualized data set and explain your application of data analytics approaches to a peer.


The graded assessment task is an individual written assessment on data exploration and preparation. This assignment includes reflection on individual practical work on data visualisation, exploration and preparation (pre-processing and transformation) for data analytics with artefacts (screen shots/outputs) of the practical work submitted in the report.

Length: 2,000 words

In order to pass the microcredential, participants must achieve an overall mark of 50% or more.



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, Zoom and the open-source software, KNIME Analytics Platform.


There are no specific prerequisites for this microcredential.

UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education. Course admission requisites may apply for future entry into awards courses.

Book a session

Tue 17 Sep 2024-
Tue 26 Nov 2024
Expert: Professor Paul Kennedy
  • This course is delivered fully online, through a combination of live Zoom/Microsoft Teams virtual classes (2 x 1 hr sessions per week) and self-paced study.
  • Online

Tutorials will be scheduled for one-hour sessions every Wednesday afternoon, and live Q&A sessions every Friday afternoon.

Final dates and times for live sessions will be confirmed with registered learners prior to the course starting.

Note that recordings of all live sessions will be made available for reference.

Enrolments are scheduled to close on Saturday, 7 September 2024, 11.59pm (AEST).


Acknowledgement of Country

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.