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.

Data Science and Big Data Analytics: Leveraging Best Practices and Avoiding Pitfalls

2020-01-09T04:11:53+00:00Categories: Data Governance Level 2, Data Engineering Curriculum Electives, Data Science Curriculum, Data Science Level 2, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Engineering Level 2, Data Management, Executive Level 2, Big Data, All Academy Courses|Tags: , , , , , , |

Data science is the key to business success in the information economy. This workshop will teach you about best practices in deploying a data science capability for your organisation. Technology is the easy part; the hard part is creating the right organisational and delivery framework in which data science can be successful in your organisation. We will discuss the necessary skill sets for a successful data scientist and the environment that will allow them to thrive. We will draw a strong distinction between “Data R&D” and “Data Product” capabilities within an enterprise and speak to the different skill sets, governance, and technologies needed across these areas. We will also explore the use of open data sets and open source software tools to enable best results from data science in large organisations. Advanced data visualisation will be described as a critical component of a big data analytics deployment strategy. We will also talk about the many pitfalls and how to avoid them.

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 [...]

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.

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