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.
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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.
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DataOps helps us add quality, speed, and automation into our work so that the traditional 80% time spent data wrangling is lower and the Business As Usual will be lower too.
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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.
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Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
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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
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.
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.
A walk-through on what is possible analyzing your data with the "R" language.
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.
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.
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
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„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.
A little bit of knowledge about how SQL Server works can go a long way towards making large data engineering queries run faster.
Before doing any analysis, you have to prepare the data properly.
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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