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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"Don't force me to *think*!" - how to help people find insights in information more easily by making well-informed design decisions.
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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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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.
Hierarchies and graphs are the bread and butter of most business applications. SQL Server 2017 comes now with a different approach: Graph Database. You will learn best practices, have fun and will leave with handy scripts.
Text analysis of financial documents using R & Power BI.
Learn the basic knowledge to use the R language in Power BI as source, transformation or visualization
Industry independent analysts say “Pyramid Analytics is essentially the best-in-class, on-premises offering for Microsoft BI today and tomorrow” What does this mean for you and PowerBI?
Learn how to use this fantastic package in R for high-speed, low-code table wrangling.
In this talk, we learn the two ways that SQL R Services can be invoked one from the R IDE via the ScaleR package; and as a stored procedure directly from SQL Server Management Studio.
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SQL Server vNext R Services and the new MicrosoftML package
As we have always seen a steady growth in SSRS since 2005, we now have much more advanced brand new features in 2016 SQL Server Reporting Services, beyond our imagination and in particular using Revolution R Models.
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Learn how to configure SQL Server 2016 for running R. The individual components and executables installed with R on SQL Server will be explored to understand the internal interactions and processes involved.
In this talk Laura will go through the theory and practice of time series analysis with examples in R covering concepts such as lag, serial correlation and Box-Ljeung tests.
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There's base R, modern R, the Hadleyverse, R in SQL, and more - understanding where to begin can be an exercise in frustration and cost you valuable time. This session takes you through the R you need to be productive quickly.
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With R in SQL Server 2016, data engineers (DBAs, developers) are expanding their horizons and deriving more value for the business from their data. Come see how this is brining people in business together.
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Come to this session to learn how to turn your existing applications into intelligent applications using the R integration into the SQL Server engine.
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This sessions covers the SQL Server 2016 Hero Features and you will see a real customer scenario demo showcasing the Full Lifecycle of Financial Data.
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This session looks at building time series analysis with AzureML in R
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R extends statistical and mathematical capabilities of SQL Server 2016.
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In this session, we will look at Predictive Analytics using SQL Server 2016 and R, using our boozy day at the Guinness factory as a backdrop to understanding why statistical learning is important for analytics today. Drinking Guinness is optional!
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Azure Machine Learning is a fully managed cloud service for predictive analytics. In this 1hour session, we will run through; ML Studio,using data, Creating and running Experiments, visualising results, R , publishing and using experiments.
When it comes to analyzing data, the R statistical language come readily to mind. Python is yet another one of your secret power tool for SQL Server, data analysis and business intelligence. In this session, we will look at both Python and R.
RMan and Reporting Girl join forces to fight the crimes in data visualization. By building a common ground (based on the IBCS standards) they improve their weapons (R and SSRS) that help them in their fight.
Need a snazzy dashboarding tool, and want it free or cheap? With a modicum of R and Linux knowledge, you can make interactive realtime dashboards for free that can be used in any web browser. Learn how in this session.
In this intensive one hour session, learn how to analyse and visualise your data using the Data Analysts Toolkit - Microsoft Power BI complemented with R
R was originally for doing stats, but it now does database CRUD, reporting, documentation, and even websites. It can be awkward to know where to start, so this session fast tracks you to the sections you need to know about.
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