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This is a free, self-paced online course.
This taster course presents the effective rollout of the National Broadband Network (NBN) Australia-wide and the joint project between the NBN Co., CSIRO Data61 and the Data Science Institute at UTS to develop a machine learning model to forecast broadband service demand. Through a case study format, participants will face a real-world problem and are presented with the solution as a learning tool in exploring data analytics.
This course reviews the difference in accuracy between the heuristic and the new machine-learning model, which achieved a 30% improvement in accuracy and greatly reduced the workload of analysts.
We explore data analytics through the case study of the NBN rollout and its perceived success; the project achieved a 96% on-time connection rate, attracting 2.3 million new customers. The team used both clustering techniques and data mining algorithms to identify cohorts of unsatisfactory customers and areas.
Previously, NBN relied on a heuristic method to guide the resource allocation to new regions, based on its past roll-out records. However, increasing customer expectations and the accelerating roll-out speed has led NBN to develop more accurate, data-driven strategies to manage the roll-out process.
This taster course uses a real-world problem to illustrate to participants the impact of effective solutions. In this course, the NBN roll-out and the impact the successful use of the new machine-learning model has on collecting and analysing data is showcased, with respect to informing evidence-based decision making.
The question is then put to participants: What lessons can they bring into their own workplaces to mirror this behaviour?
This course will provide an opportunity for participants to engage with the following learning outcomes:
This taster course is suitable for anyone who is interested in data analytics and machine learning, explored through a real-life application of the NBN company.
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START DATE |
Start anytime |
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MODE |
Online |
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DURATION |
Self paced |
Yang is an Associate Professor at the UTS Data Science Institute. He received his PhD degree in computer science from the National University of Singapore in 2004. Before Joining UTS in 2019, Yang was with Data61 (formerly NICTA), the Institute for Infocomm Research, Rensselaer Polytechnic Institute and Nanyang Technological University.
Yang’s research interests include machine learning and information fusion techniques and their applications to asset management, intelligent infrastructure, cognitive and emotive computing and computer vision. He has over 100 conference and journal publications and has received more than 10 research awards, including the Eureka Prize, iAwards and AWA Water Awards.
Learn the power and language of data analysis and get hands-on with key techniques for mining and analysing data. [10 wks, avg 5 hrs/wk]
Go in-depth into key machine learning concepts through theory, maths and programming. [10 wks, avg 5 hrs/wk]
Build your foundational data background to develop a skillset to run data mining and analysis projects. [10 wks, avg 5 hrs/wk]
Explore tools to analyse and present data on NSW communities during the early to mid-stages of COVID-19.
Understand the global response to COVID-19 through a data-driven approach, to explain how nations have responded.
Explore a case study investigating the impacts of COVID-19 and Australian bushfires, on our health and wellbeing.
Explore how data analytics enables improved NBN workforce planning.
