Microsoft Professional Program – Data Science

Last year, from oktober 2016 up to january 2017, I participated in Microsoft’s Professional Program, specifically the Data Science track.
It was only the 1st public iteration of the program but back then it already felt like a mature course.
This probably had to do with the fact that there had already been a private run of the course in the months before.
David Eldersveld (twitter | blog) was one of the participants in that original run and he gives you a high level overview on his blog.

In this post I’ll be going into a bit more detail and explain how I experienced the program.
In short, there was joy and there were tears.

Read on for the full story!

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Power BI and R – Part 3 (Getting started)

This entry is part 3 of 3 in the series Power BI and R

In this post we’ll look at some simple examples of how to use R in Power BI.
While going through these examples, we’ll have a look at what is already possible in this preview version and what is not possible.
We’ll do this by slowly examples from scratch so you can follow or rebuild the examples yourself.
I’ll keep the technical explanations to a minimum as there are already a ton of books and courses filled with that.

So if you’re seeking deep knowledge of the inner workings of R, this post is going to disappoint you.
However, if you’re looking to keep it simple and just get started building cool or useful visualizations in R to explore your data, just keep reading!

In case you missed the previous posts:

In part 1 of this series, I gave you a couple of reasons to start using R for visualizations in Power BI.
In part 2 of this series, I gave you remarks on the R integration. You might’ve caugtht a glimpse of what is possible if you stepped outside of the lines that Microsoft drew. And I showed you an ugly error you can receive because of  decimal symbol setting in your OS.

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Power BI and R – Part 2 (Remarks and errors)

This entry is part 2 of 3 in the series Power BI and R

In part 1 of this series, I gave you a couple of reasons to start using R for visualizations in Power BI.

In this part I’ll tell you about the things I discovered and the problems that I ran into in this preview version.

Please note that the remarks below count for the december 2015 version of Power BI Desktop.
R script visuals is still a preview, things might change in later versions.

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Data science with Microsoft – An introduction

Let’s be honest, Microsoft isn’t a name that would traditionally be associated with data science.
But just as we’ve witnessed in other areas, they have quickly caught up!

in the last year we’ve seen the following appear on the Microsoft Data Science radar:

  • Azure Machine Learning
  • Power BI
  • Cortana Analytics Suite
  • Acquisition of Datazen & Revolution Analytics
  • Integration of R in SQL Server

Looking at it like this, it’s just a list like any other. Not even a big list.
The magic happens when we look at what this means for the developers, consultants and ultimately the business.


Azure Machine Learning

We now have the ability to create AND deploy predictive models in minutes using Azure Machine Learning.

AzureMachineLearning2 AzureMachineLearning

This opens up interesting possibilities where we can send data from SQL Server, a SQL Azure Database or just live from a mobile application or excel to gain insights


Power BI

If you still need an introduction to Power BI then you’re doing something wrong.
Contact me on twitter, LinkedIn or via the comments.
I’ll gladly talk you through Power BI and why you should be using it for almost everything you do with data in your company.
I mean that, get in touch with me. Now! 🙂

But on a more serious note, I’m going to be crude to Microsoft here.
A long time ago, Power BI started as an over-hyped and underwhelming experience. Everyone saw the potential this Excel stuff had but I’m guessing the experience most people had was similar to mine. That is, Power BI back then was a disappointment because of what we were expecting.
The one good thing it did have at one point was PowerPivot.

Skip forward to august 2015.
The Power BI dream had suddenly come true!
Most of the things we were expecting in the past suddenly were there, in a web service AND a desktop application.
AMAZING!

Skip forward 3 more months and Power BI has exceeded our wildest dreams.
I could literally fill books with all the great stuff the Power BI team has done and enabled for the community.
The Power BI API, a plugin for PowerPoint, custom visualizations, support on all devices, enterprise ready and a lot more all combined with a CRAZY pace of new releases!

If you haven’t used Power BI yet, skip all the praise and commercial talk, go download the desktop application and start working with it. Soon you’ll be an Power BI evangelist as well 😉

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Cortana Analytics Suite

If I had to summarize Cortana Analytics for anyone, I’d say it is basically Azure Machine Learning for predictive analytics combined with Power BI for a beautiful presentation of your data. And sprinkled on top are some of the most incredible and integrated services you can dream of.

Cortana Analytics is not really a product, it’s more a combination of several services that work really great together and form a solution to your questions.
It enables different scenario’s for any case you can think of.

Whether you have a scenario with real-time data analytics, (real-time) predictive analytics or you’re just in need of a data lake to fill with your data for analysis, Cortana Analytics is where you need to be.

This picture from Microsoft summarizes the Cortana Analytics Suite the best.
It shows you how different tools fit different purposes in the chain from data to insight to action.

Cortana-Analytics-Suite[1]


Acquisition of Datazen & Revolutions Analytics

Not much to say except: WOW!
I bet I’m not the only one who did not see both of these coming.

Datazen was already known for it’s mobile dashboarding solutions. It’s acquisition could only mean something big was coming for on-premises BI.
And it did, Microsoft announced at PASS Summit 2015 that Datazen would basically be integrated with SSRS to provide an outstanding mobile BI solution for those who must stay on-premises.

Revolution Analytics was widely known in the world of the R programming language.
Where R standard is limited to a single machine and the memory that machine has, Revolution Analytics provided a scalable solution.
How cool is that? So cool that Microsoft wanted it integrated in SQL Server 2016!
I’m sure that the R services in SQL Server 2016 are just a starting point. But imagine the possibilities from a data science perspective when you combine this with the columnstore and in-memory technologies.

splash[1] architecture[1]

Integration of R in SQL Server

This isn’t just R in SQL Server, it’s an implementation of Revolution R Enterprise in SQL Server!

No longer do you have to pull data to your developer machine, data can just stay in the database where it should be.
Combine this with columnstore indexes and the in-memory technology and you know that the data scientists are now drooling.
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Summary

Microsoft has improved so much in the last year, it’s as if it’s a whole new company.
Data professionals are getting a lot of shiny new toys and can expect a lot more solutions to be build end to end on a Microsoft platform.
Whether that platform is on-premises or in the cloud is up to the business to decide.

Data science is no longer unknown territory for people who work with SQL Server, it’s already on our doorstep.
On top of that, Microsoft’s Cortana Analytics solution offers incredible value and an ease of use I’ve never seen before with something like this.
It took me only an hour to set up a solution that parsed real-time sensor data, combined it with reference data in a database and then show it on a Power BI dashboard.

One thing is sure, you can expect some interesting blog posts in the feature.
Not only from myself but the entire SQL Server community!

When you’re ready, move on to this list of training materials I compiled for you. Let’s get started!

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