Quantum Computing

2020-09-18T06:21:35+00:00Categories: AI Engineering Curriculum Electives, Data Science Curriculum Electives, Introductory, Dr Eugene Dubossarsky, Innovation & Tech (CTO) Curriculum, All Academy Courses|Tags: , , , |

This course is an introduction to the exciting new field of quantum computing, including programming actual quantum computers in the cloud. Quantum computing promises to revolutionise cryptography, machine learning, cyber security, weather forecasting and a host of other mathematical and high-performance computing fields. A practical component will include writing quantum programs and executing them on simulators as well as on actual quantum computers in the cloud.

The Future of Analytics

2020-07-21T02:14:18+00:00Categories: Predictive Analytics & AI, Data Science Level 2, Data Science Curriculum Electives, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Engineering Curriculum, Data Management, Innovation & Tech (CTO) Curriculum, Executive Level 2, AI Engineering Curriculum, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses, Data Governance Level 3, Innovation & Tech (CTO) Level 1|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.

Innovating with Best Practices to Modernise Delivery Architecture and Governance

2019-10-24T04:48:00+00:00Categories: AI Engineering Curriculum Electives, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Data Governance Level 1, Data Management, Innovation & Tech (CTO) Curriculum, Executive Level 2, Infrastructure & Technologies, Big Data, Data Engineering Level 1, AI Engineering Level 2, All Academy Courses|Tags: , , , , , |

Organisations often struggle with the conflicting goals of both delivering production reporting with high reliability while at the same time creating new value propositions from their data assets. Gartner has observed that organizations that focus only on mode one (predictable) deployment of analytics in the construction of reliable, stable, and high-performance capabilities will very often lag the marketplace in delivering competitive insights because the domain is moving too fast for traditional SDLC methodologies. Explorative analytics requires a very different model for identifying analytic opportunities, managing teams, and deploying into production. Rapid progress in the areas of machine learning and artificial intelligence exacerbates the need for bi-modal deployment of analytics. In this workshop we will describe best practices in both architecture and governance necessary to modernise an enterprise to enable participation in the digital economy.

Modernising Your Data Warehouse and Analytic Ecosystem

2019-10-24T04:49:10+00:00Categories: Data Governance Level 2, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Data Management, Innovation & Tech (CTO) Curriculum, Infrastructure & Technologies, AI Engineering Curriculum, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , , |

This full-day workshop examines the emergence of new trends in data warehouse implementation and the deployment of analytic ecosystems.  We will discuss new platform technologies such as columnar databases, in-memory computing, and cloud-based infrastructure deployment.  We will also examine the concept of a “logical” data warehouse – including and ecosystem of both commercial and open source technologies.  Real-time analytics and in-database analytics will also be covered.  The implications of these developments for deployment of analytic capabilities will be discussed with examples in future architecture and implementation. This workshop also presents best practices for deployment of next generation analytics using AI and machine learning. 

Optimising Your Big Data Ecosystem

2019-10-24T04:48:58+00:00Categories: Predictive Analytics & AI, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Curriculum, Data Management, Innovation & Tech (CTO) Curriculum, Executive Level 2, Infrastructure & Technologies, AI Engineering Curriculum, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses|Tags: , , , , |

Big Data exploitation has the potential to revolutionise the analytic value proposition for organisations that are able to successfully harness these capabilities. However, the architectural components necessary for success in Big Data analytics are different than those used in traditional data warehousing. This workshop will provide a framework for Big Data exploitation along with recommendations for architectural deployment of Big Data solutions.

Capacity Planning for Enterprise Data Deployment

2019-10-24T04:51:48+00:00Categories: Innovation & Tech (CTO) Curriculum Electives, Data Governance Curriculum Electives, Executive Curriculum Electives, Stephen Brobst, Data Engineering Curriculum, Data Governance Level 1, Data Management, Infrastructure & Technologies, AI Engineering Curriculum, Data Engineering Level 1, AI Engineering Level 1, Executive Level 1, All Academy Courses|Tags: , , , |

This workshop describes a framework for capacity planning in an enterprise data environment. We will propose a model for defining service level agreements (SLAs) and then using these SLAs to drive the capacity planning and configuration for enterprise data solutions. Guidelines will be provided for capacity planning in a mixed workload environment involving both strategic and tactical decision support. Performance implications related to technology trends in multi-core CPU deployment, large memory deployment, and high density disk drives will be described. In addition, the capacity planning implications for different approaches for data acquisition will be considered.

Real-Time Analytics Development and Deployment

2019-10-24T04:50:30+00:00Categories: Executive Curriculum Adv Electives, Data Governance Adv Electives, Data Science Curriculum Advanced Electives, Innovation & Tech (CTO) Adv Electives, Stephen Brobst, Data Engineering Curriculum, Data Management, AI Engineering Curriculum, Data Science Level 3, Big Data, Data Engineering Level 1, AI Engineering Level 1, All Academy Courses, Data Governance Level 3, Executive Level 3, Innovation & Tech (CTO) Level 3|Tags: , , , , , |

Real-time analytics is rapidly changing the landscape for deployment of decision support capability. The challenges of supporting extreme service levels in the areas of performance, availability, and data freshness demand new methods for data warehouse construction. Particular attention is paid to architectural topologies for successful implementation and the role of frameworks for Microservices deployment. In this workshop we will discuss evolution of data warehousing technology and new methods for meeting the associated service levels with each stage of evolution.

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