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  • (7)
A journey through the Tidyverse Thomas Hütter
An introduction to the philosophy of tidy data and the collection of R packages called Tidyverse that help to treat your data appropriately. Including lots of demos from ingesting to cleaning to visualizing your data.
Data Exploration & Experimentation with Notebooks in Azure Ian Griffiths
Learn how Azure supports interactive, exploratory notebooks (e.g. Jupyter) for data processing and experimentation across a range of scales from simple single-computer work up to massively parallel Databricks clusters.
Machine Learning in Azure Databricks Terry McCann
In this session we focus on how Spark implements Machine Learning at Scale with Spark ML.
New SQL Server Features for Developers Hasan Savran
This session is for all developers who want to learn about the new Dev features and enhancements of SQL Server 2017 and 2019
Rapid Requirements: Introducing the Machine Learning Canvas Terry McCann
In this session, we will introduce the Advancing Analytics Machine Learning Canvas and how it can be used to capture requirements for Machine Learning Projects.
The Azure Spark Showdown - Databricks VS Synapse Analytics Simon Whiteley
Azure now has two slick, platform-as-a-service spark offerings, but which one should you choose? A separate specialist tools or a one-size-fits-all solution? Join Simon as he compares and contrasts the spark offerings.
The Data Lifecycle: Decision making with data in Power BI Jennifer Stirrup
Data can be viewed as having a product lifecycle, where it can be collected, analysed, visualized, utilized and then monetized. This session is focused more on the destination of the data, rather than the journey.
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