LEARNING PATHWAYS

Training Courses, Workshops and Seminars

The Future of Analytics

2021-07-23T01:04:45+00:00May 18th, 2019|Tags: , , , , , , |

This full day workshop examines the trends in analytics deployment and developments in advanced technology. The implications of these technology developments for data foundation implementations will be discussed with examples in future architecture and deployment. This workshop presents best practices for deployment of a next generation data management implementation as the realization of analytic capability for mobile devices and consumer intelligence. We will also explore emerging trends related to big data analytics using content from Web 3.0 applications and other non-traditional data sources such as sensors and rich media.

Harnessing Mobile Intelligence in an Omni-Channel World

2021-07-16T02:02:26+00:00June 24th, 2019|Tags: |

The proliferation of mobile devices and a new breed of consumers is driving a revolution in requirements for pervasive access to data for marketing analytics. A new breed of consumers has emerged with a DIY (Do It Yourself) mindset related to technology and unprecedented sophistication in using data for personal decisions. This change in consumer behavior is creating demand for consumer intelligence capability in which direct access to data is required for personal decision making. We will discuss the implications of this phenomena for deployment of omni-channel marketing and illustrate leading edge case studies in the delivery of marketing intelligence integrated across all channels.

How to Innovate in the Age of Big Data

2021-07-16T02:09:58+00:00June 24th, 2019|Tags: |

The era of Big Data presents exciting opportunities for leveraging analytics to create competitive advantage and new sources of revenue. To maximize business value, however, an enterprise must innovate in the context of good governance models to avoid technology projects for the sake of technology. In this workshop we describe a framework developed at the Massachusetts Institute of Technology for maximizing business value with the right combination of innovation and governance. We will examine new scenarios for monetising from Big Data sources. We will also explore opportunities for exploiting non-traditional data types from both internal and open data sources.

Advanced Implementation of Big Data Analytics with Graph Processing

2021-07-16T02:17:01+00:00June 24th, 2019|Tags: |

There are a significant number of big data analytics opportunities where graph processing is an effective model of computation for problem solving. In this workshop we present a programming model for implementation of graph algorithms and explain how the execution model works. We also provide example applications in the area of social network analysis, product cross-selling, and fraud detection.

Advanced Deep Learning

2021-07-26T01:19:34+00:00December 5th, 2018|Tags: , |

This course provides a more rigorous, mathematically based view of modern neural networks, their training, applications, strengths and weaknesses, focusing on key architectures such as convolutional nets for image processing and recurrent nets for text and time series. This course will also include use of dedicated hardware such as GPUs and multiple computing nodes on the cloud. There will also be an overview of the most common available platforms for neural computation. Some topics touched in the introduction will be revisited in more thorough detail. Optional advanced topics may include Generative Adversarial Networks, Reinforcement Learning, Transfer Learning and probabilistic neural networks.

Big Mistakes to Avoid When Performing Big Data Analytics

2021-07-16T05:44:20+00:00June 24th, 2019|Tags: |

Mark Twain, a famous American author, once stated that there are “Lies, Damned Lies, and Statistics.” This phrase is used to describe the persuasive power of numbers, particularly the use of statistics, to lead people to draw incorrect conclusions. This workshop describes the subtle mistakes that can easily be made when interpreting the results from an analytic study or report. We describe logically sound processes for deciphering data using methods designed to illuminate actionable information for data scientists without distracting or misleading the knowledge worker from the relevant facts needed for effective decision-making.

Advanced Fraud and Anomaly Detection

2021-04-13T04:01:44+00:00February 12th, 2019|Tags: , |

The detection of anomalies is one of the most eclectic and difficult activities in data analysis. This course builds on the basics introduced in the earlier course, and provides more advanced methods including supervised and unsupervised learning, advanced use of Benford’s Law, and more on statistical anomaly detection. Optional topics may include anomalies in time series, deception in text and the use of social network analysis to detect fraud and other undesirable behaviours.

Smart Cities Using Big Data

2021-07-19T01:46:01+00:00June 24th, 2019|Tags: |

The use of big data is a lynch pin in the deployment of smart cities of the future. The potential of big data goes far beyond the efficiencies that can be created in areas such as energy consumption, health care, education, and law enforcement. The true potential of big data is directly engaging society in governance through increased transparency and delivery of services using access to data as a foundation. This workshop will provide mini-case studies in a range of settings from developed countries such as the USA and developing countries such as Pakistan. An emphasis of the discussion will be on how city governments can use data to improve quality of life for their citizens.

Innovating with Best Practices to Modernise Delivery Architecture and Governance

2021-07-23T01:01:04+00:00May 20th, 2019|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.

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