Overview

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 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.

This program is launching in March 2025 with exclusive access for CIRES partners. Please see the individual course tabs for further details and register your interest for future sessions by emailing k.aldridge@uq.edu.au.

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

Who Should Participate?

The training program will benefit three types of professionals:

1. 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.
2. 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.
3. 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.

Register your Interest

This program will be launched in March 2025 with exclusive access for CIRES partners. Please see the individual course tabs for further details and registration.

If you are interested in future offerings, please register your interest via k.aldridge@uq.edu.au to receive further information when available.

• 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

• ASSOCIATE PROFESSOR GIANLUCA DEMARTINI

  The University of Queensland

  Expert In: Information Retrieval, Semantic Web, and Human-in-the-loop Artificial Intelligence

• 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

• PROFESSOR MARTA INDULSKA

  The University of Queensland Business School

  Expert In: IT business value, data quality, business process management, and open innovation

• PROFESSOR SHAZIA SADIQ

  The University of Queensland

  Expert In: Data quality management and responsible use of advanced technologies

• 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

Managing Enterprise Data

Registrations for this course have now closed.

Please register your interest for future sessions by contacting k.aldridge@uq.edu.au

Aim: Apply data management, data science, inferential analysis techniques, and generative AI to model and manage organisational data, extract insights, and guide decision-making through impactful storytelling.

Prerequisite: Basic proficiency with Python

Evidence and Insights from Data: Utilise exploratory data analysis, inferential statistics, and generative AI to uncover patterns, gain insights, and draw reliable conclusions from datasets.

Storytelling with Data: Develop skills to transform insights into compelling narratives using visualisation and communication techniques, empowering stakeholders to make informed decisions.

Data Modelling and Management: Develop expertise in analysing data management systems, designing effective organisational data models, querying data for management efficiency, and leveraging generative AI to automate and enhance data processes.

Contact

If you have any questions about this course or the Information Resilience Training Program, please contact CIRES Centre and Operations Manager Kate Aldridge via k.aldridge@uq.edu.au.

Responsible AI for Enterprise

Registrations for this course have now closed.

Please register your interest for future sessions by contacting k.aldridge@uq.edu.au

Aim: 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.

Topics covered during the course

Session 1: Machine Learning & Clustering

• Overview of AI & Machine Learning (Key concepts and generative AI)
• Machine Learning Approaches (Supervised, unsupervised, semi-supervised, reinforcement, zero-shot, one/few-shot learning)
• Bias in ML (Real-world implications & mitigation strategies)
• Clustering Techniques (K-means & hierarchical clustering, evaluating & choosing the optimal number of clusters)
• Dimensionality Reduction (Feature selection, principal component analysis (PCA))

Session 2: Classification Methods & Model Evaluation

• Classification Overview (Types, process)
• Classification Algorithms (Decision trees, random forest, naïve Bayes, k-nearest neighbours (KNN), support vector machines (SVM), artificial neural networks (ANNs))
• Model Challenges (Bias-variance trade-off, data imbalance & handling techniques)
• Model Evaluation (Train-test split, cross-validation, confusion matrix, accuracy, precision, recall, F-score)
• Comparing Models (Choosing the right evaluation metrics & performance comparison)

Session 3: Deep Learning, Generative AI & Model Resilience

• Deep Learning Overview (Fundamentals, challenges with ANNs)
• Neural Network Architectures (Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs))
• Advanced Architectures (Encoder-decoder models, attention mechanisms, transformer models)
• Generative AI (Pre-trained models (BERT, GPT), large language models (LLMs), generative adversarial networks (GANs))
• Model Resilience Techniques (Enhancing model performance, hyperparameter tuning, sensitivity to data perturbations and model resilience techniques)

Contact

If you have any questions about this course or the Information Resilience Training Program, please contact CIRES Centre and Operations Manager Kate Aldridge via k.aldridge@uq.edu.au.

Value Creation from Data

Registrations for this course have now closed.

Please register your interest for future sessions by contacting k.aldridge@uq.edu.au

Aim: Explore how data can be converted into value, including identifying value creation strategies, evaluating key capabilities, and fostering a data-driven organisational culture.

Essential Prerequisite: None.

Data Value Creation Foundations: Define and apply the “Improving, Wrapping, and Selling” framework to identify and analyse opportunities for data value creation.

Capabilities for Data Value Creation: Understand and evaluate the key capabilities required for successful data value creation, including acceptable data use.

Leading Organizational Change for Data Value: Analyse and recommend organizational structures and changes needed to foster a data-driven culture and support data value creation.

Contact

If you have any questions about this course or the Information Resilience Training Program, please contact CIRES Centre and Operations Manager Kate Aldridge via k.aldridge@uq.edu.au.