|Session Date/Time (dd-MM-YYYY 24h)||Speaker||Category||Track||Title|
|07-06-2017 17:00||Michael Johnson||Breakout Session (60 minutes)||Analyze & Interpret||Analyzing Real-Time Data Using Azure Streaming Analytics|
|07-06-2017 18:00||Julie Hyman||Breakout Session (60 minutes)||Analyze & Interpret||Replacing Brio: Practical Advice for Analysts|
|07-06-2017 19:00||Todd Mostak||Breakout Session (60 minutes)||Analyze & Interpret||From Hours to Milliseconds: Using GPUs to Accelerate Data Discovery and Visual Analytics|
|07-06-2017 20:00||Mark Wilcock||Breakout Session (60 minutes)||Analyze & Interpret||A Lazy Data Scientist's Toolkit|
Event Date: 07-06-2017 17:00 - Category: Breakout Session (60 minutes) - Track: Analyze & Interpret
Event Date: 07-06-2017 18:00 - Category: Breakout Session (60 minutes) - Track: Analyze & Interpret
During this session Quest product manager, Julie Hyman will examine the crossroads at which many organizations find themselves as they replace Brio. She will explain the environment, common use cases and must-haves for a self-service data preparation tool for reporting. Julie will go on to share how to safely move from Brio to a more modern self-service data preparation solution to:
• Overcome challenges such as data-source proliferation, spreadsheet sprawl, manual processes and more. • Understand the top three workflows any reporting solution must handle efficiently. • Compare criteria for a replacement tool, such as integrating/preparing data, building queries and workflows, managing workloads and more.
Julie will give you expert advice so you can move forward confidently while reducing risks.
Event Date: 07-06-2017 19:00 - Category: Breakout Session (60 minutes) - Track: Analyze & Interpret
Event Date: 07-06-2017 20:00 - Category: Breakout Session (60 minutes) - Track: Analyze & Interpret
• Tableau, a data visualisation tool, for exploring and presenting data beautifully, • R and packages for data manipulation, plotting, text mining and predictive analytics, • Excel - for pixel perfect reports, • Power BI, an analytics and visualisation tool, for doing just that!, • A SQL database in the cloud for improving data quality, • A cloud-based machine learning toolkit (Azure ML), • And cognitive services APIs for text analysis (and how to call them easily from R).
We'll use a wide variety of public datasets in our examples. These include:
• The recent results of the strength of EU banks from the European Banking Authority, • Health, wealth and populations stats from the Gapminder foundation, • Lifespan data from the Lancet and the corporate responsibility reports from a large bank.
The examples will also use some fictional data, but based on a real project, about a trading firm's revenue reporting and other activities.