Python is a high-level, general-purpose language used by a thriving community of millions. Data-science teams often use it in their production environments and analysis pipelines, and it’s the tool of choice for elite data-mining competition winners and deep-learning innovations. This course provides a foundation for using Python in exploratory data analysis and visualisation, and as a stepping stone to machine learning.
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.
Blockchain is one of the most disruptive and least understood technologies to emerge over the previous decade. This course gives participants an intuitive understanding of blockchain in both public and private contexts, allowing them to distinguish genuine use cases from hype. We explore public crypto-currencies, smart contracts and consortium chains, interspersing theory with case studies from areas such as financial markets, health care, trade finance, and supply chain. The course does not require a technical background.
This full-day workshop examines the trends in analytic technologies, methodologies, and use cases. The implications of these developments for deployment of analytic capabilities will be discussed with examples in future architecture and implementation. This workshop also presents best practices for deployment of next generation analytics.
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.
This full-day workshop examines the emergence of new trends in data warehouse implementation and the deployment of analytic ecosystems. We will discuss new platform technologies such as columnar databases, in-memory computing, and cloud-based infrastructure deployment. We will also examine the concept of a “logical” data warehouse – including and ecosystem of both commercial and open source technologies. Real-time analytics and in-database analytics will also be covered. The implications of these developments for deployment of analytic capabilities will be discussed with examples in future architecture and implementation. This workshop also presents best practices for deployment of next generation analytics using AI and machine learning.
Big Data exploitation has the potential to revolutionise the analytic value proposition for organisations that are able to successfully harness these capabilities. However, the architectural components necessary for success in Big Data analytics are different than those used in traditional data warehousing. This workshop will provide a framework for Big Data exploitation along with recommendations for architectural deployment of Big Data solutions.
Social networking via Web 2.0 applications such as LinkedIn and Facebook has created huge interest in understanding the connections between individuals to predict patterns of churn, influencers related to early adoption of new products and services, successful pricing strategies for certain kinds of services, and customer segmentation. We will explain how to use these advanced analytic techniques with mini case studies across a wide range of industries including telecommunications, financial services, health care, retailing, and government agencies.
This workshop describes a framework for capacity planning in an enterprise data environment. We will propose a model for defining service level agreements (SLAs) and then using these SLAs to drive the capacity planning and configuration for enterprise data solutions. Guidelines will be provided for capacity planning in a mixed workload environment involving both strategic and tactical decision support. Performance implications related to technology trends in multi-core CPU deployment, large memory deployment, and high density disk drives will be described. In addition, the capacity planning implications for different approaches for data acquisition will be considered.