APPLICATIONS

Leadership and Resilience Skills for Data Professionals

2020-09-18T03:04:04+00:00Categories: Data Science Curriculum, Leadership & Management, Data Engineering Curriculum, Katrina Loukas, All Academy Courses|Tags: , |

Many people today have been developed emotionally and mentally for an era that no longer really exists. This has created a critical soft-skills gap between current workforce ability and business requirements today. In this course participants learn to ‘readapt’ their soft skills so that they are aligned with a thriving 21st century business. They are also given a simple framework from which to continue the self-development so that the training instigates sustainable change.

Fraud and Anomaly Detection

2020-10-19T06:58:03+00:00Categories: Level 2, Data Science Curriculum Electives, Fraud and Security, R, Dr Eugene Dubossarsky, Financial Risk, All Academy Courses|Tags: , |

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 inter-operation 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.

Stars, Flakes, Vaults and the Sins of Denormalisation

2020-09-18T04:23:11+00:00Categories: Innovation & Tech Curriculum Electives, Data Governance Curriculum Electives, Stephen Brobst, Data Engineering Curriculum, Data Management, AI Engineering Curriculum, All Academy Courses|Tags: , , , |

Providing both performance and flexibility are often seen as contradictory goals in designing large scale data implementations. In this talk we will discuss techniques for denormalisation and provide a framework for understanding the performance and flexibility implications of various design options. We will examine a variety of logical and physical design approaches and evaluate the trade offs between them. Specific recommendations are made for guiding the translation from a normalised logical data model to an engineered-for-performance physical data model. The role of dimensional modeling and various physical design approaches are discussed in detail. Best practices in the use of surrogate keys is also discussed. The focus is on understanding the benefit (or not) of various denormalisation approaches commonly taken in analytic database designs.

Best Practices in Enterprise Information Management

2019-10-24T04:45:22+00:00Categories: Data Culture Curriculum, Innovation & Tech Curriculum Electives, Data Governance Curriculum, Fraud and Security, Stephen Brobst, Executive Curriculum, Data Engineering Curriculum, Data Management, Big Data, All Academy Courses|Tags: , , , , , |

The effective management of enterprise information for analytics deployment requires best practices in the areas of people, processes, and technology. In this talk we will share both successful and unsuccessful practices in these areas. The scope of this workshop will involve five key areas of enterprise information management: (1) metadata management, (2) data quality management, (3) data security and privacy, (4) master data management, and (5) data integration.

Overcoming Information Overload with Advanced Practices in Data Visualisation

2019-10-24T04:46:56+00:00Categories: Data Culture Electives, Innovation & Tech Curriculum Electives, Data Science Curriculum, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Management, AI Engineering Curriculum, Big Data, All Academy Courses|Tags: , , , , , , , |

In this workshop, we explore best practices in deriving insight from vast amounts of data using visualisation techniques. Examples from traditional data as well as an in-depth look at the underlying technologies for visualisation in support of geospatial analytics will be undertaken. We will examine visualisation for both strategic and operational BI.

The Future of Analytics

2020-07-21T02:14:18+00:00Categories: Predictive Analytics & AI, Data Science Curriculum Electives, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Engineering Curriculum, Data Management, Innovation and Technology Curriculum, AI Engineering Curriculum, Big Data, All Academy Courses|Tags: , , , , , , |

This full day workshop examines the trends in analytics deployment and developments in advanced technology. The implications of these technology developments for data foundation implementations will be discussed with examples in future architecture and deployment. This workshop presents best practices for deployment of a next generation data management implementation as the realization of analytic capability for mobile devices and consumer intelligence. We will also explore emerging trends related to big data analytics using content from Web 3.0 applications and other non-traditional data sources such as sensors and rich media.

Advanced Fraud and Anomaly Detection

2020-11-03T01:32:55+00:00Categories: AI Engineering Curriculum Electives, Data Science Curriculum Advanced Electives, Fraud and Security, R, Level 3, Dr Eugene Dubossarsky, Financial Risk, All Academy Courses|Tags: , |

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.

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