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Category: Data Science

Data Science With Microsoft

Published 2015-11-24 by Kevin Feasel

Jan Mulkens has started an interesting series on data science using the Microsoft stack.  His first post is an overview of the products currently available:

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!

From there, Mulkens shares a number of training materials:

Make Microsoft’s Virtual Academy your first or last stop when learning, but you should always pay it a visit!
It’s filled with incredible information broken down in some great free courses.

It seems that (at least some of) the closed edX.org courses are being placed on here, so you can follow up on them at your own pace.
Do be aware that you can’t receive certificates on Microsoft Virtual Academy.

This is an exciting time to jump into analytics.  Most of the material is free, and it’s easy to get VMs to practice, so the barrier to entry is low.

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Buck Woody On R & Python

Published 2015-11-10 by Kevin Feasel

Buck Woody’s back to blogging, and his focus is data science.  Over the past month, he’s looked at R and Python.

First, on installing R:

In future notebook entries we’ll explore working with R, but for now, we need to install it. That really isn’t that difficult, but it does bring up something we need to deal with first. While the R environment is truly amazing, it has some limitations. It’s most glaring issue is that the data you want to work with is loaded into memory as a frame, which of course limits the amount of data you can process for a given task. It’s also not terribly suited for parallelism – many things are handled as in-line tasks. And if you use a package in your script, you have to ensure others load that script, and at the right version.

Enter Revolution Analytics – a company that changed R to include more features and capabilities to correct these issues, along with a few others. They have a great name in the industry, bright people, and great products – so Microsoft bought them. That means the “RRE” engine they created is going to start popping up in all sorts of places, like SQL Server 2016, Azure Machine Learning, and many others. But the “stand-alone” RRE products are still available, and at the current version. So that’s what we’ll install.

Also on installing and getting started with Python:

Python has some distinct differences that make it attractive for working in data analytics. It scales well, is fairly easy to learn and use, has an extensible framework, has support for almost every platform around, and you can use it to write extensive programs that work with almost any other system and platform.

R and Python are the two biggest languages in this slice of the field, and you’ll gain a lot from learning at least one of these languages.

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