Electric Power System simulations using R
The field of electric power systems engineering relies heavily on computer simulations for analysis because of its nature. These computer simulations aid the planning, operation and management of the system. Computer simulations have been implemented using several scientific computing tools. However, I have not yet seen any implementations using R. This inspired my thesis at a German institution. I am now privileged to have implemented several power systems analysis simulations using R. In the process, I started by creating standalone scripts for several topics of interests in this domain. Examples are:
• Power flow simulation (Gauss-Siedel & Newton-Raphson techniques)
• Time domain simulation of the Single-Machine Infinite Bus (SMIB) system using the Modified Euler numerical integration method.
• Multi-machine transient stability simulation using the ‘deSolve’ package for solving n-ODEs that describe the dynamics of interconnected generating machines.

Predicting and Plotting Crime in Seattle
I have recently been watching “The Wire” and along with my Amazon Prime membership looking better and better, it’s actually given me some things to think about. Besides making me an expert police detective, it has steadily been making an impact on how I view a city. It sounds kind of cheesy, but I never really understood how nice Seattle is when looking at the kind of crime that a city like Baltimore can experience on a day to day basis.

SAP Helps Companies Unlock the Value of Contextual Data – See more at: http://www.news-sap.com/sap-helps-companies-unlock-value-contextual-data/#sthash.IMYdnbDA.dpuf
SAP SE today announced that it will develop a Big Data solution tailored to the needs of the telecommunications industry. The solution intends to help simplify IT and unlock business innovation for communication service providers (CSPs). Powered by the SAP HANA database and designed to run on SAP HANA Cloud Platform, the data model is expected to provide real-time, contextual data processing and analytics for telcos. It is planned that customers will have the option of running the Big Data solution on premise, in the cloud or in a hybrid deployment. The announcement was made at Mobile World Congress (MWC), being held March 2–5 in Barcelona.

Supervised Classification, Logistic and Multinomial
We will start, in our Data Science course, to discuss classification techniques (in the context of supervised models). Consider the following case, with 10 points, and two classes (red and blue)

How Data-as-a-Service (DaaS) Is Revolutionizing Marketing
Marketers analyze the data and crunch the numbers – but how many can really say they know which consumers or businesses are in market for their products and services? Or how many companies are overly dependent on modeling what they thinka prospect or customer may do versus having real-time insights into their actual behaviors? Primed to make a huge entrance in 2015, Data-as-a-Service (DaaS) empowers companies with real-time data to overcome these tough marketing challenges. In fact, DaaS is completely revolutionizing marketing – generating real-time insights and revenue from Big Data has become a reality, no longer a process that takes months.

Experiments in Time Series Clustering
I should start by saying that I really don’t know what I’m doing, so be warned. I thought it would interesting to apply TSclust to the S&P 500 price time series. I took the 1-day simple rate of change, grouped by year with dplyr, and then indexed by the day of the year all in one pipeR pipeline. Since the TSclust paper “TSclust: An R Package for Time Series Clustering” demonstrates interoperability with hclust in their OECD interest rate example ( Section 5.2 ), I thought I could visualize the results nicely with treewidget from the epiwidgets package. Just because the htmlwidget was designed for phylogeny doesn’t mean we can’t use it for finance. Here is the result:

So What Can Text Analysis Do for You?
Despite believing we can treat anything we can represent in digital form as “data”, I’m still pretty flakey on understanding what sorts of analysis we can easily do with different sorts of data. Time series analysis is one area – the pandas Python library has all manner of handy tools for working with that sort of data that I have no idea how to drive – and text analysis is another.
So prompted by Sheila MacNeill’s post about textexture, which I guessed might be something to do with topic modeling (I should have read the about, h/t @mhawksey), here’s a quick round up of handy things the text analysts seem to be able to do pretty easily…
Taking the lazy approach, I has a quick look at the CRAN natural language processing task view to get an idea of what sort of tool support for text analysis there is in R, and a peek through the NLTK documentation to see what sort of thing we might be readily able to do in Python. Note that this take is a personal one, identifying the sorts of things that I can see I might personally have a recurring use for…

Using Tables for Statistics on Large Vectors
This is the first post I’ve written in a while. I have been somewhat radio silent on social media, but I’m jumping back in. Now, I work with brain images, which can have millions of elements (referred to as voxels). Many of these elements are zero (for background). We want to calculate basic statistics on the data usually and I wanted to describe how you can speed up operations or reduce memory requirements if you want to calculate many statistics on a large vector with integer values by using summary tables.

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