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  • (43)
Data Science and Analytics: Real-World Experience from Diverse voices in the field Jennifer Stirrup (She/Hers)
In this session, we will cut through the marketing buzzwords to share experiences, tips, and tricks on how to be successful with Data Science and Analytics in the real world. Tune in to hear the team share real-world experience and get takeaways from industry insiders on real projects with impact. We will also discuss the ethics and fairness of Data Science and Analytics projects and how we can be more inclusive from a technology, people, and process standpoint.
The video is not available to view online.
Designing Data Visualisations that can be understood "in the blink of an eye" Adam Vero
"Don't force me to *think*!" - how to help people find insights in information more easily by making well-informed design decisions.
The video is not available to view online.
Hunting for fraud with Benford's law in R Thomas Hütter
In this lightning talk I'll demonstrate how to apply the R implementation of Benford's law (which actually is not about crime or fraud) to identify possibly fraudulent invoice or other data.
The video is not available to view online.
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.
Analyzing database with XE and Query Store data using R / Py Tomaž Kaštrun
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.
The video is not available to view online.
Common Data Science Mistakes Tomaž Kaštrun
Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
The video is not available to view online.
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.
An R primer for SQL folks Thomas Hütter
A walk-through on what is possible analyzing your data with the "R" language.
Some transformations that can save your ETL in Power BI Ana Maria Bisbe York
To build analytical models, we need to start by extracting, transforming, cleaning, preparing and loading the data. This session analyzes a set of scenarios that may happen during the ETL step using the Power Query in Power BI.
The Five Cs of Artificial Intelligence for Businesses Jennifer Stirrup
Organisations need to know how to get started with Artificial Intelligence. This practical session offers organizations, small and large, with a helping hand in practical advice and demos using Microsoft Azure with Open Source technologies.
Using SQL Server and Azure SQL Database for ML workloads Robin Lester
Ever wondered how you can add the power of ML to your existing SQL estate without the need to invest in new services? Come to this session to learn about running Python and R workloads in SQL, the PREDICT function, and how to operationalise models in Azure SQL Database
The video is not available to view online.
50 ways to show your data Thomas Hütter
„A picture is worth a thousand words“ - compelling visualizations beyond the usual bar, line or scatter plots, produced with the help of the ggplot2 package and friends.
An introduction to SQL Server for data scientists Gavin Payne
A little bit of knowledge about how SQL Server works can go a long way towards making large data engineering queries run faster.
Data Overview and Manipulation - T-SQL, R, Python Dejan Sarka
Before doing any analysis, you have to prepare the data properly.
Data Science For The DBA - Get Started Now Manohar Punna
If you are a DBA and want to get started with Data Science, then this session is for you. This demo-packed session will show you an end-to-end Data Science project covering the core technologies in Microsoft Data + AI stack.
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