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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
Power BI brings the concept of self-service BI, and gives a chance to incorporate Data Science models into analysis using R and Python. It also analyse data with a feature called Dataflows.
Go from beginner to expert with learning new Azure skills and the best material out there
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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