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.
Scheduling
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.
