Launching a new digital investment requires the know-how to select technology, justify the investment and obtain stakeholders’ approval. This course gives you the knowledge and tools to justify your digital investment, compare real costs, understand the required resources and finally launch your project, program or product.
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 .
With big data expert and author Jeffrey Aven. The third module in the “Big Data Development Using Apache Spark” series, this course provides the practical knowledge needed to perform statistical, machine learning and graph analysis operations at scale using Apache Spark. It enables data scientists and statisticians with experience in other frameworks to extend their knowledge to the Spark runtime environment with its specific APIs and libraries designed to implement machine learning and statistical analysis in a distributed and scalable processing environment.
This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the inter-operation of human, rule-based, predictive and outlier-detection methods. Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which can be used for all supervised and unsupervised applications, as well as cluster analysis, visualisation and fraud detection based on Benford’s law. The course will also cover the analysis and visualisation of social-network data. A basic knowledge of R and predictive analytics is advantageous.
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.
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.
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.
The second module in the “Big Data Development Using Apache Spark” series, this course provides the Spark streaming knowledge needed to develop real-time, event-driven or event-oriented processing applications using Apache Spark. It covers using Spark with NoSQL systems and popular messaging platforms like Apache Kafka and Amazon Kinesis. It covers the Spark streaming architecture in depth, and uses practical hands-on exercises to reinforce the use of transformations and output operations, as well as more advanced stream-processing patterns. With big data expert and author Jeffrey Aven.
This course sets the foundation with coding stage best practices and areas of focus. The key factor in coding is deployment of lean procedures and waste avoidance. A corollary of lean is component management [...]
Configuration management operates project artefact relations. “Operates” means identification or creation, storage, retrieval, audit, control, identification and change. Configuration management combines configurations with artefacts and source code. This course looks at DevOps configuration management [...]
Copyright 2024 Alpha Zetta Pte Ltd | All Rights Reserved | Privacy Policy
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
