Data ethics is rapidly becoming the most critical aspect of engaging in a data driven, digital world. Significant backlash against industry giants like Facebook and Google for their data practices has pushed data ethics into mainstream society. With the ACCC signaling its intentions to focus on data practices and a host of new legislation, led by GDPR in Europe, the open data movement and the Consumer Data Right in Australia, it has become a key concern for digital consumers and the companies that serve them. The course covers the practical issues involved in implementing data ethics and uses real world illustrations and cases. We start with high profile data ethics cases and cover the essentials of the new legislation. We then walk through a data ethics policy. Day 2 focuses on a toolkit for implementing data trust and privacy by design, then covers consent and transparency requirements. It closes with a real-world framework for the governance required and an overview of the practical implementation steps.
This two day course provides an informed, realistic and comprehensive foundation for establishing best practice Data Governance in your organisation. Suitable for every level from CDO to executive to data steward, this highly practical course will equip you with the tools and strategies needed to successfully create and implement a Data Governance strategy and roadmap.
This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the interoperation of human, rule-based, predictive and outlier-detection methods. Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data. A basic knowledge of R and predictive analytics is advantageous.
The detection of anomalies is one of the most eclectic and difficult activities in data analysis. This course builds on the basics introduced in the earlier course, and provides more advanced methods including supervised and unsupervised learning, advanced use of Benford’s Law, and more on statistical anomaly detection. Optional topics may include anomalies in time series, deception in text and the use of social network analysis to detect fraud and other undesirable behaviours.