This article is a video tutorial on introduction to the very bare basics of R. Its a bit dry, but it is the underlying components of everything covered in the interesting stuff. Can’t do cool stuff without understanding the basics first.
Ever wonder the difference between R and Microsoft R? Considering learning R as a programming language? You should probably watch this video. It is the first in a 4 part series to give you the jump start you need to becoming a professional data scientist with R.
Due to my time in the games industry, I still occasionally receive questions around it. Today I received an email from a college student with some questions I feel are more broadly applicable and am therefor writing an article which can be shared to that broader audience.
For this portion of the HoL, we will be bypassing the Raspberry Pi portion all together and go straight to provisioning Resources and using a local app to simulate the telemetry data. You can alternatively still use a Raspberry Pi as this session was intended, but for time purposes, that can be skipped and the app below can be used for simulating data for a live dashboard. Continue reading →
This article is for a hands on lab in IoT I am running. You will find full documentation at the provided link. Also, you may run into a scenario in which the MCP 3008 code does not work. If this is the case, you are likely using an older version of the nugget package which contains it or the new version has not been pushed yet. You can find the code for the MCP 3008 below.
Here is the link: https://onedrive.live.com/redir?resid=BA8DC4B28555902A!3406&authkey=!AGawE2hfolHvC8s&ithint=file%2cpdf
These days I need to make videos instead of written articles, so I am going to post a few of those here.
In this video we will do an initial exploratory analysis on a water flow data set that came from a prototype that I built. The prototype consists of a water pump, a valve and a flow meter. The data set exists in SQL Azure. We will use R and R Studio to perform the analysis from an Azure virtual machine.
This article is outside of my normal articles, mostly because I didn’t write it. I’m sure you can tell the stylistic difference. However it is worth posting here as any hackathon is worth posting about, especially one putting up $32,500 in prizes. Hopefully this is a good read.
Today is a freaking cool day. Why do you ask? Because today I am writing an article on how to use two of the coolest freaking big data/data science tools out there together to do epic shit! Lets start with HBase. HBase is a way to have a big data solution with query performance at an interactive level. So many folks are starting to just dump data into HBase. In the project teddy solution, we are dumping tweets, dialogue and dialogue annotations to power our open domain conversational api. There really is no other way that is easy to use for us to do this.
The second part of project teddy is to predict based on an incoming conversational component, what sort of response the speaker is attempting to illicit from the teddy bear. If we power our teddy bear with predictive analytics and big data, this would be perfect. What better platform to do this quickly and easily than AzureML?