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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.
DBA and Data scientists should work together! Analyzing data gathered with XE and Query Store data using R or Python for better database insight and discovering hidden patterns.
Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
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.
In this session we focus on how Spark implements Machine Learning at Scale with Spark ML.
This session is for all developers who want to learn about the new Dev features and enhancements of SQL Server 2017 and 2019
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.
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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