Engagement
Information Resilience Training
CIRES is responding to an urgent need to build workforce capacity in Australia to create, protect, and sustain agile data pipelines, capable of detecting and responding to failures and risks across the information value chain.
CIRES has developed a tailored professional training program focused on the upskilling needs of its stakeholders as well as the broader Australian workforce.
Register your Interest
CIRES is responding to an urgent need to build workforce capacity in Australia to create, protect, and sustain agile data pipelines, capable of detecting and responding to failures and risks across the information value chain.
The CIRES training program aims to:
- Build capacity in Australian public and private sector organisations to develop resilient data pipelines capable of delivering game-changing productivity gains.
- Position Australian organisations at the forefront of technology leadership and value creation from data assets.
The program has been designed in a modular way to cater for a variety of training needs and backgrounds, while providing end-to-end coverage of topics relating to information resilience – the capacity to create, protect, and sustain agile data through value processes in enterprises.
Who Should Participate?
The training program will benefit three types of professionals:
- those working directly with data including storage, curation, infrastructure, analytics, model development, machine learning, visualization, and reporting; for example,
(a) enterprise end users such as business intelligence specialists, data reporting analysts; and
(b) more technical roles, such as data analysts, data engineers, data analytics specialists, data architects, data scientists, data governance analysts, data administrators, research and data strategists, data operations specialists, and machine learning and AI engineers. - those that manage data workers, analysts, and data scientists, or oversee data-driven functions at an executive level; for example, data analytics managers, business intelligence leads, data science team leads, and data governance heads.
- those working in other functions of the organisation and wishing to gain knowledge of Information Resilience more generally; for example solution architects, and business function heads.
How it Works
The curriculum combines theoretical instruction with hands-on demonstration, and the opportunity to bring your own data to work with, and participation in discussion, case studies, and reflection. The program is designed to upskill the workforce with information resilience knowledge and related skills through the following courses:
- Managing Enterprise Data
- Responsible AI for Enterprise
- Value Creation from Data
Each course consists of multiple modules of three hours each, with an additional hour of discussion. The modules can be stacked within and between courses in a variety of ways. Delivery will be face to face, with light pre-module self-training and post session work. Courses are designed to be taken independently of each other by cohorts to which they are relevant. The initial offering of each course is expected to be 10 hours of contact and an additional three hours of independent work.
Courses, Modules and Learning Outcomes
Managing Enterprise Data: To perform and develop data management techniques to model and manage organisational data to generate insights and inform decision making via storytelling with data.
Essential Prerequisite: Basic proficiency with a programming language (SQL, Python, or R)
Leveraging your Data Assets: Understand how to use the data science process to best utilise your data assets.
Resilient data pipelines: Investigate the quality and fitness of your data and learn how to wrangle your data to make it fit for purpose.
Storytelling with Data: Become familiar with how to present your findings via storytelling with data through effective visualisations and narratives.
Responsible AI for Enterprise: To apply machine learning models to leverage AI for business growth while prioritising transparency, equity, and accountability to align with societal well-being and corporate responsibility.
Essential Prerequisite: Basic proficiency with a programming language (ideally Python)
Preferred Prerequisite: Familiarity with the content of the Managing Enterprise Data course.
Machine Learning Overview: Learn about machine learning paradigms, applicability and potential pitfalls of opaque algorithms.
Clustering and Classification Methods: Become familiar with using several clustering and classification methods.
Deep Learning and Other Techniques and Applications: Get to grips with some of the latest trends in machine learning.
Value Creation from Data: To unlock the potential of data, transforming it into actionable insights and strategic assets that drive innovation, optimize operations, and create economic value while upholding the principles of data integrity and privacy.
Essential Prerequisite: None.
Data Governance: Understand data governance and its relationship with legislative compliance and ethics.
Responsible Use: Learn about frameworks for best practice for responsible use of data.
Data Monetization Strategy: Learn effective strategies of how to monetize data.
ASSOCIATE PROFESSOR GIANLUCA DEMARTINI
The University of Queensland
Expert In: Information Retrieval, Semantic Web, and Human-in-the-loop Artificial Intelligence
PROFESSOR MARTA INDULSKA
The University of Queensland Business School
Expert In: IT business value, data quality, business process management, and open innovation
ASSOCIATE PROFESSOR HASSAN KHOSRAVI
The University of Queensland
Expert In: Artificial intelligence in the future of education and learner-centred, data driven learning at scale
PROFESSOR SHAZIA SADIQ
The University of Queensland
Expert In: Data quality management and responsible use of advanced technologies
DR IDA ASADI SOMEH
The University of Queensland Business School; Centre for Information Systems Research (CISR), MIT Sloan School of Management, USA
Expert In: Organizational and societal impact of data, analytics, and artificial intelligence
DR THOMAS TAIMRE
The University of Queensland
Expert In: Probability theory, computer simulation, and mathematical optimization
DR SEN WANG
The University of Queensland
Expert In: Feature Selection, Semi-supervised Learning, Deep Learning, Pattern Recognition, Data Mining, and Health Informatics
Register your Interest
We plan to launch the Training Program in the first half of 2025. Please register your interest via cires@uq.edu.au to receive further information when available.