This is the curriculum for IT professionals, data engineers, data analysts, and those supporting data science. The levels build up on another, each block represents a class of two days.

Best Practices in Enterprise Information Management

2019-10-24T04:45:22+00:00Categories: Data Culture Level 1, Data Culture Curriculum, Innovation & Tech (CTO) Curriculum Electives, Data Governance Curriculum, Fraud and Security, Stephen Brobst, Executive Curriculum, Data Engineering Curriculum, Data Governance Level 1, Data Management, Executive Level 2, Big Data, Data Engineering Level 1, All Academy Courses, Innovation & Tech (CTO) Level 3|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.

Deep Learning and AI

2020-09-18T04:37:12+00:00Categories: Keras, Tensorflow, Level 2, Data Science Curriculum, Python, Data Engineering Curriculum, Dr Eugene Dubossarsky, All Academy Courses|Tags: , |

This course is an introduction to the highly celebrated area of Neural Networks, popularised as “deep learning” and “AI”. The course will cover the key concepts underlying neural network technology, as well as the unique capabilities of a number of advanced deep learning technologies, including Convolutional Neural Nets for image recognition, recurrent neural nets for time series and text modelling, and new artificial intelligence techniques including Generative Adversarial Networks and Reinforcement Learning. Practical exercises will present these methods in some of the most popular Deep Learning packages available in Python, including Keras and Tensorflow. Trainees are expected to be familiar with the basics of machine learning from the Fundamentals course, as well as the python language.

Advanced Machine Learning Masterclass I

2020-09-18T05:35:18+00:00Categories: Level 2, Data Science Curriculum, tidyverse, R, Data Engineering Curriculum, Dr Eugene Dubossarsky, AI Engineering Curriculum, All Academy Courses|Tags: , |

This course is for experienced machine-learning practitioners who want to take their skills to the next level by using R to hone their abilities as predictive modellers. Trainees will learn essential techniques for real machine-learning model development, helping them to build more accurate models. In the masterclass, participants will work to deploy, test, and improve their models.

Advanced Machine Learning Masterclass II: Random Forest

2020-09-18T05:59:23+00:00Categories: Data Engineering Curriculum Electives, Data Science Curriculum, Data Science Level 2, tidyverse, R, Data Engineering Level 2, Dr Eugene Dubossarsky, AI Engineering Curriculum, AI Engineering Level 2, All Academy Courses|Tags: , |

This advanced machine learning masterclass will explore the many unique applications and extensions of the randomForest package, many of which are implemented in R. Access to the methods in random forest allows the user [...]

Blockchain, Smart Contracts and Cryptocurrency

2020-09-18T06:08:38+00:00Categories: AI Engineering Curriculum Electives, Data Culture Electives, Data Science Curriculum Electives, Data Governance Curriculum Electives, Executive Curriculum Electives, Tristan Blakers, Introductory, Data Engineering Curriculum, Innovation & Tech (CTO) Curriculum, All Academy Courses|Tags: , , , |

Blockchain is one of the most disruptive and least understood technologies to emerge over the previous decade. This course gives participants an intuitive understanding of blockchain in both public and private contexts, allowing them to distinguish genuine use cases from hype. We explore public crypto-currencies, smart contracts and consortium chains, interspersing theory with case studies from areas such as financial markets, health care, trade finance, and supply chain. The course does not require a technical background.

Advanced Deep Learning

2019-10-17T02:41:05+00:00Categories: Keras, Innovation & Tech (CTO) Curriculum Electives, Data Engineering Curriculum Electives, Tensorflow, Data Science Curriculum, Python, Dr Eugene Dubossarsky, AI Engineering Curriculum, Data Science Level 3, Data Engineering Level 3, AI Engineering Level 3, All Academy Courses, Innovation & Tech (CTO) Level 3|Tags: , |

This course provides a more rigorous, mathematically based view of modern neural networks, their training, applications, strengths and weaknesses, focusing on key architectures such as convolutional nets for image processing and recurrent nets for text and time series. This course will also include use of dedicated hardware such as GPUs and multiple computing nodes on the cloud. There will also be an overview of the most common available platforms for neural computation. Some topics touched in the introduction will be revisited in more thorough detail. Optional advanced topics may include Generative Adversarial Networks, Reinforcement Learning, Transfer Learning and probabilistic neural networks.

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.

Agile Data Management Architecture

2019-10-24T04:46:19+00:00Categories: Predictive Analytics & AI, Data Culture Curriculum, Data Science Level 2, Data Science Curriculum Electives, Executive Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Fraud and Security, Stephen Brobst, Data Engineering Curriculum, Data Governance Level 1, 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|Tags: , , , |

This full-day workshop examines the trends in analytic technologies, methodologies, and use cases. 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.

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. 

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