Data-Driven Management

2020-09-17T07:24:02+00:00Categories: AI Engineering Curriculum Electives, Data Engineering Curriculum Electives, Government, Data Science Curriculum, Data Governance Curriculum, Data Science Level 1, Executive Curriculum, Data Engineering Level 2, Data Governance Level 1, Dr Eugene Dubossarsky, Innovation & Tech (CTO) Curriculum, AI Engineering Level 2, Executive Level 1, All Academy Courses, Innovation & Tech (CTO) Level 1|Tags: , , , |

The Data-Driven Management course is for executives and managers who want to leverage analytics to support their most vital decisions and enable better decision-making at the highest levels. It empowers senior executives with skills to make more effective use of data analytics. It covers contexts including strategic decision-making and shows attendees ways to use data to make better decisions. Attendees will learn how to receive, understand and make decisions from a range of analytics methods, including visualisation and dashboards. They will also be taught to work with analysts as effective customers.

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 [...]

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.

Cost-Based Optimisation: Obtaining the Best Execution Plan for Complex Queries

2019-10-24T04:52:00+00:00Categories: Data Governance Level 2, Predictive Analytics & AI, Innovation & Tech (CTO) Curriculum Electives, Data Science Level 2, Data Science Curriculum Electives, Data Governance Curriculum, Innovation & Tech (CTO) Level 2, Stephen Brobst, Data Engineering Level 2, Data Engineering Curriculum, AI Engineering Curriculum, Big Data, AI Engineering Level 2, All Academy Courses|Tags: , , , |

Optimiser choices in determining the execution plan for complex queries is a dominant factor in the performance delivery for a data foundation environment. The goal of this workshop is to de-mystify the inner workings of cost-based optimisation for complex query workloads. We will discuss the differences between rule-based optimisation and cost-based optimisation with a focus on how a cost-based optimization enumerates and selects among possible execution plans for a complex query. The influences of parallelism and hardware configuration on plan selection will be discussed along with the importance of data demographics. Advanced statistics collection is discussed as the foundational input for decision-making within the cost-based optimiser. Performance characteristics and optimiser selection among different join and indexing opportunities will also be discussed with examples. The inner workings of the query re-write engine will be described along with the performance implications of various re-write strategies.

Social Network Analysis: Practical Use Cases and Implementation

2019-10-24T04:51:00+00:00Categories: Data Governance Level 2, Predictive Analytics & AI, Data Culture Electives, Innovation & Tech (CTO) Curriculum Electives, Data Science Curriculum, Data Governance Curriculum Electives, Executive Curriculum Electives, Marketing, Data Science Level 1, Data Culture Level 2, Innovation & Tech (CTO) Level 2, Fraud and Security, Stephen Brobst, Data Management, Executive Level 2, AI Engineering Curriculum, Big Data, AI Engineering Level 2, All Academy Courses|Tags: , , , |

Social networking via Web 2.0 applications such as LinkedIn and Facebook has created huge interest in understanding the connections between individuals to predict patterns of churn, influencers related to early adoption of new products and services, successful pricing strategies for certain kinds of services, and customer segmentation. We will explain how to use these advanced analytic techniques with mini case studies across a wide range of industries including telecommunications, financial services, health care, retailing, and government agencies. 

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