sqlpasshistory

ID: 958

Back to Main list

PASS Marathon: Analytics 2018

Session Date/Time (dd-MM-YYYY 24h)	Speaker	Category	Track	Title
28-11-2018 01:00	Brad Llewellyn	Breakout Session (60 minutes)	Advanced Analytics	Azure Machine Learning Studio: Making Data Science Easy(er)
28-11-2018 02:00	Tony McGovern	Breakout Session (60 minutes)	Advanced Analytics	Spatial Data Analysis in Power BI
28-11-2018 03:00	Julie Hyman	Breakout Session (60 minutes)	Business Intelligence and Data Warehousing	Getting data-driven on a budget
28-11-2018 04:00	Joseph Yeates	Breakout Session (60 minutes)	Advanced Analytics	Predictive Analytics with R in Power BI
28-11-2018 05:00	Tomaž Kaštrun	Breakout Session (60 minutes)	Advanced Analytics	Common Data Science Mistakes
28-11-2018 06:00	Paul Andrew	Breakout Session (60 minutes)	Advanced Analytics	Beyond IoT Real-time Data Ingestion with Azure Stream Analytics

SessionID: 86855

Azure Machine Learning Studio: Making Data Science Easy(er)

Back to calendar

Event Date: 28-11-2018 01:00 - Category: Breakout Session (60 minutes) - Track: Advanced Analytics

Speaker(s): Brad Llewellyn

Title: Azure Machine Learning Studio: Making Data Science Easy(er)

Description:

There's so much buzz around Data Science right now. What is it? What can I do with it? Do I need a Ph.D. and a team of super nerds? In this introductory session, we'll see that Azure Machine Learning Studio's intuitive interface and selection of built-in modules brings the power of Data Science to the people....no advanced degrees necessary. Bring your data hats and sense of adventure, because the world of data science just got a lot more inviting!

SessionID: 87001

Spatial Data Analysis in Power BI

Back to calendar

Event Date: 28-11-2018 02:00 - Category: Breakout Session (60 minutes) - Track: Advanced Analytics

Speaker(s): Tony McGovern

Title: Spatial Data Analysis in Power BI

Description:

This presentation is aimed at people who want to create beautiful thematic maps and perform impactful spatial data analysis with Power BI. Much of the content will make use of Power BI's out-of-the-box spatial capabilities through Esri's ArcGIS Maps and the MapBox Visual.

While I use data from the United States Census Bureau and the Socrata Open Data APIs, what you'll learn can also be reproduced using similar non-U.S. spatial data.

I'll also highlight some commonly used machine learning models you can integrate with Power BI using the popular statistical programming language, R. From clustering algorithms to hotspot analysis, the combination of ArcGIS, Mapbox, R, and Power BI allows us to deliver better and more rigorous insight from our spatial data.

If you're accustomed to mapping data in Power BI and want to be able to perform advanced spatial analysis and visualize more beautiful maps, this is the session for you.

SessionID: 87254

Getting data-driven on a budget

Back to calendar

Event Date: 28-11-2018 03:00 - Category: Breakout Session (60 minutes) - Track: Business Intelligence and Data Warehousing

Speaker(s): Julie Hyman

Title: Getting data-driven on a budget

Description:

Organizations know they need to be more data-driven but many feel unprepared (and possibly unbudgeted) to implement an intensive full data science-driven analytics platform. What if you are ready to get more value from your data but aren't sure what options are available? And what about data integration? Learn more about successful models that you can implement to up your analysis game, including empowering your current data and business analysts to meet their own reporting and data prep needs.

Join us on this webcast to learn about the different approaches to analytics within organizations and what solutions you may wish to consider as you move to be more data-driven.

SessionID: 86887

Predictive Analytics with R in Power BI

Back to calendar

Event Date: 28-11-2018 04:00 - Category: Breakout Session (60 minutes) - Track: Advanced Analytics

Speaker(s): Joseph Yeates

Title: Predictive Analytics with R in Power BI

Description:

Power BI reports are typically used for descriptive analysis: a historical look of what has happened. But what if your report could visualize what is likely to happen and recommend what you can do about it now?

This session will introduce how to start the shift from descriptive to predictive analysis and how you can begin to leverage the power of machine learning in your business intelligence solutions. No previous knowledge of R or predictive modeling is required!

We will cover the basics and best practices of predictive modeling and then these concepts will be applied to create three different predictive models in R. Then we will cover three ways to integrate the predictive power of these models into a Power BI report.

SessionID: 87255

Common Data Science Mistakes

Back to calendar

Event Date: 28-11-2018 05:00 - Category: Breakout Session (60 minutes) - Track: Advanced Analytics

Speaker(s): Tomaž Kaštrun

Title: Common Data Science Mistakes

Description:

In the middle of deploying the model, team of data scientists realize that the predictions are "somewhat-off". Troubleshooting on the horizon and what to do. Session will guide you through most common mistakes data scientists and statisticians are making when preparing and engineering the data using T-SQL or any other database system. Further more, we will explore common statistical and data science mistakes when modeling data, extracting know-how from the data, finding the hidden patterns and running different test against the structural models using mainly R, Python, or Spark. What not-to-do will be replaced with what to-do explanations using sample datasets and sample codes. Some statistical knowledge or background is a plus!

SessionID: 86902

Beyond IoT Real-time Data Ingestion with Azure Stream Analytics

Back to calendar

Event Date: 28-11-2018 06:00 - Category: Breakout Session (60 minutes) - Track: Advanced Analytics

Speaker(s): Paul Andrew

Title: Beyond IoT Real-time Data Ingestion with Azure Stream Analytics

Description:

The desire and expectation to use real-time data is constantly growing, businesses need to react to market trends instantly. In this new data driven age a daily ETL load/processing window isn’t enough. We need a constant stream of information and analytics achieved in real-time. In this session we will look at how that can be achieved using Azure Stream Analytics. Building streaming jobs that can blend and aggregate data as it arrives to drive live Power BI dashboards. Plus, we’ll explore how a complete lambda architecture can be created when combining stream and batch data together.