Wednesday, March 30, 2016

Data Modeling...

In today's world of ever growing data and information, one of the areas that has been kind of battling the existence has been data modeling, there are wide ranging opinions about the validity of data modeling both positive and negative. One of the viewpoints favoring data modeling has been to provide a context around the data that needs to be accessed and used, how it can be stored and resented to users. There are number of situations where how the data being organized has to be presented to the business in a concise fashion. There are different types of models within data modeling like conceptual models, logical models and physical models. What do these type of mean and how they differ from each other is very concisely presented in the article below. Quoting from the article: "My uses of conceptual, logical, and physical come from the Information Engineering (IE) methods of data modeling". The article has been written Karen Lopez: Senior Project Manager and Principal Consultant, InfoAdvisors, Inc.

I hope the above article provides good explanation especially for folks who are getting into the area of data modeling.

Tuesday, March 22, 2016

Data Science Resource/Blog...

One of the rapidly advancing areas today is Data Analytics/Data Science in the Business Intelligence space. There are lot of resources/tools that are coming explaining the capabilities of Data Science. Data Science has seen tremendous growth alongside the adoption of Big Data technologies. It is a very challenging space to keep up with, one of the blogs that i regularly visit to understand the concepts in Data Science and keep up with the trends in Data Science/Analytics, is The blog/website is very nicely laid out with content rich articles and tutorials. They cover a variety of tools and also encourage folks interested in data science to participate in challenges/contests. Please do visit the site, it provides amazing content and coverage.

Saturday, March 19, 2016

Experiments - Azure ML Studio

Azure ML studio is very intuitive and a powerful tool that can be used for performing different type of data analysis experiments. The Studio can be used to set up very basic experiments like descriptive statistics to performing complicated machine learning algorithms. The whole layout of the ML studio is very user friendly and in some ways remind me of the layouts in SSIS packages. I decided to setup a basic experiment in ML studio to perform certain descriptive statistics calculations on a sample data set. The dataset I used was adult census data from UCI repository and ran the data set through a R script component to data, once the data was massaged it was run through a descriptive statistics to perform descriptive statistics on each column. Once all the components are in place the experiment can be run in the ML studio to get the results. The data can be verified and if everything looks good, the experiment can be published as a web service and can be accessed by a say .NET program.
I have illustrated the experiment which i have set up in the ML Studio.

Hope this provides a basic overview of ML Studio. Azure ML studio opens up a lot of possibilities to perform different types of data science experiments and bring them more into the main stream.