Data Governance Curriculum

Our Data Governance Curriculum takes data culture to the next level of awareness. Those taking control or responsibility for data structures, management and ethics will gain a deeper understanding of the data governance process.

Fundamentals of AI, Machine Learning, Data Science and Predictive Analytics

2020-11-02T23:25:54+00:00Categories: Predictive Analytics & AI, Level 1, Data Science Curriculum, Data Governance Curriculum, Executive Curriculum, Data Engineering Curriculum, Dr Eugene Dubossarsky, Innovation and Technology Curriculum, AI Engineering Curriculum, All Academy Courses|Tags: , , , , |

This course is an intuitive, hands-on introduction to ai, data science and machine learning, it's your artificial intelligence 101. The training focuses on fundamentals and key skills, leaving you with a deep understanding of the core concepts of ai and data science and even some of the more advanced tools used in the field. The course does not involve coding, or require any coding knowledge or experience. As our leading course, it has transformed the artificial intelligence (AI), machine learning (ML) and data science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data analytics and data science practitioners who have attended it.

AI and Data Science for Managers and Executives

2020-09-22T00:26:33+00:00Categories: Predictive Analytics & AI, Data Culture Electives, Data Governance Curriculum, Executive Curriculum, Dr Eugene Dubossarsky, Innovation and Technology Curriculum, All Academy Courses|Tags: , , , |

Improve your project’s chance of success by avoiding common failures in AI and data science projects. This one-day workshop is aimed at current or aspiring leaders and managers of AI / machine learning teams and functions. The focus of the course is on the key concepts that are required to avoid the most common and far too frequent failures in AI projects and initiatives.

Data Literacy for Everyone

2020-09-18T01:26:38+00:00Categories: Data Culture Curriculum, Data Governance Curriculum, Introductory, Executive Curriculum, Dr Eugene Dubossarsky, Innovation and Technology Curriculum, All Academy Courses|Tags: , , |

This course is for managers and workers without a strong quantitative background. It introduces a range of skills and applications related to data literacy for digital transformations and critical thinking in such areas as forecasting, population measurement, set theory and logic, causal impact and attribution, scientific reasoning and the danger of cognitive biases. There are no prerequisites beyond high-school mathematics; this course has been designed to be approachable for everyone.

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, Executive Curriculum, Dr Eugene Dubossarsky, Innovation and Technology Curriculum, All Academy Courses|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 Governance I

2020-09-18T02:53:29+00:00Categories: Data Culture Electives, Government, Data Science Curriculum, Data Governance Curriculum, Executive Curriculum, Mark Burnard, Data Engineering Curriculum, Innovation and Technology Curriculum, AI Engineering Curriculum, Financial Risk, All Academy Courses|Tags: , , , |

This two day course provides an informed, realistic and comprehensive foundation for establishing best practice data governance in your organisation. Suitable for every level from CDO to executive to data steward, this highly practical course will equip you with the tools and strategies needed to successfully create and implement a data governance strategy and roadmap.

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

2020-10-26T10:07:42+00:00Categories: Data Engineering Curriculum Electives, Data Science Curriculum, Data Governance Curriculum Electives, Stephen Brobst, Executive Curriculum, Data Visualisation, Data Management, 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.

Data Governance II

2020-12-02T02:02:44+00:00Categories: Executive Curriculum Adv Electives, Data Culture Electives, Government, Data Science Curriculum, Data Governance Curriculum, Mark Burnard, Data Engineering Curriculum, Innovation and Technology Curriculum, AI Engineering Curriculum, Financial Risk, All Academy Courses|Tags: , , , |

This one day course builds on the foundation of Data Governance I, and dives deeper into selected areas that are designed to provide the most practical and real-world applications of data governance. It includes the change management journey to the “data-driven” organisation, and implications of the necessity of model governance in the context of data science, AI/ML initiatives and RPA/IPA .

Data Transformation and Analysis Using Apache Spark

2020-10-19T06:52:15+00:00Categories: Jeffrey Aven, Level 1, Apache Spark Training with Jeffrey Aven, Experienced Analytics Instructor + Big Data Author, Data Science Curriculum Electives, Data Governance Curriculum Electives, Apache Spark, Data Engineering Curriculum, All Academy Courses|Tags: , |

With big data expert and author Jeffrey Aven. Learn how to develop applications using Apache Spark. The first module in the “Big Data Development Using Apache Spark” series, this course provides a detailed overview of the spark runtime and application architecture, processing patterns, functional programming using Python, fundamental API concepts, basic programming skills and deep dives into additional constructs including broadcast variables, accumulators, and storage and lineage options. Attendees will learn to understand the Apache Spark framework and runtime architecture, fundamentals of programming for Spark, gain mastery of basic transformations, actions, and operations, and be prepared for advanced topics in Spark including streaming and machine learning.

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.

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