TRAINING AUTHORS & TRAINERS Archives | Page 3 of 8

Advanced Analytics Using Apache Spark

2021-03-12T03:57:56+00:00February 28th, 2019|Tags: Expert, Tech|

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.

Fraud and Anomaly Detection

2021-03-12T03:57:53+00:00February 11th, 2019|Tags: Expert, Tech|

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.

Stars, Flakes, Vaults and the Sins of Denormalisation

2021-07-23T01:03:47+00:00May 13th, 2019|Tags: Expert, Tech, Workshop, Stephen Brobst|

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.

Data Transformation and Analysis Using Apache Spark

2022-02-16T04:17:23+00:00February 21st, 2019|Tags: Expert, Tech|

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.

Best Practices in Enterprise Information Management

2021-07-23T00:58:54+00:00May 17th, 2019|Tags: Management, Business, Non-tech, Expert, Stephen Brobst, Workshop|

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.

Stream and Event Processing using Apache Spark

2021-04-13T03:39:51+00:00February 21st, 2019|Tags: Expert, Tech|

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.

DevOps Build Management

2021-03-12T03:59:39+00:00April 24th, 2020|Tags: Tech|

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 [...]

DevOps Configuration Management

2021-03-12T03:59:40+00:00April 27th, 2020|Tags: Tech|

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 [...]

Deep Learning and AI

2021-07-19T01:53:31+00:00February 28th, 2019|Tags: Tech, Expert|

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.

Transforming the Organization into a Digital Company

2021-04-13T03:52:13+00:00March 30th, 2021|Tags: Management, C-suite, Business|

This course provides an overview of how to evaluate an enterprise’s digital investment portfolio and then how to enable digital transformation via organisational change. In a context where global disruption is the norm, enterprise agility is more important than ever. To build enterprise agility you need a solid foundation of appropriate organisational structure and governance, as well as financial and human capital and a strong technical platform. This course covers how to evaluate and prepare an enterprise for digital transformation.

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