Who should attend
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.
About the course
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 course
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 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 is structured into five modules. Each module includes self-study materials and facilitated online sessions. The five modules are:
- 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.
- 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.
- 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.
- 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.
- 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.
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 Technology (C04406). This microcredential may qualify for recognition of prior learning at this and other institutions.
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.