Thursday, August 13, 2015

Azure ML Studio - Part 2

In continuation of my earlier blog post related to Azure ML studio, I would like to describe some additional components that can be used while setting up an experiment. One of the main components available in the Experiment designer is called Statistical functions. This section has a set of multiple functions to choose from, they range from Elementary Statistics to Hypothesis testing. These components would typically used once the dataset has been cleansed to an extent so that one can accurate readings of the data from the experiment. Please see the image below. In this example after executing an R-Script the output is fed to an Descriptive Statistics module.

The Descriptive statistics module typically can include Counts, Range, Statistical Summaries and Percentiles. Once the descriptive statistics is completed, the output can be stored on a variety of formats and this is provided by the writer component. Please see the image below. The Data destination can be

Azure (SQL Database, blob storage, table), Hive Query. Each format has its own advantages and for more info, one can refer to the link here:

Friday, August 7, 2015

Azure ML Studio - Part 1

Data Science has been experiencing tremendous growth in the business world today, there is a tremendous scope/job opportunities for people with Data Science Experience. One of the challenges has been to learn the different components of Data Science since most of them involve lot of Statistical, Math, Data Mining Algorithms knowledge. Microsoft on its part has been working steadily expose data science for the programming public. Initially Azure was slow to take off, but now with growing cloud implementations, Azure has been experiencing a lot of growth. So Microsoft decided to use the Azure platform and provide Data Science tools for the programmers. One of the very effective tools that is offered is called the Azure ML Studio, this is a development environment for Machine Learning Model Development. The interface of this tool is similar to some of the Visual Studio tools provided earlier by Microsoft. In order to start using a the Azure ML Studio one needs to have a Azure account. The whole concept of Azure ML works on the concept of Software as a service. One can use the following link to learn more about the Azure ML capabilities: Once you login to the azure studio, the first that will happen is that the workspace will be set up. There will be a + symbol at the bottom of the workspace, click on that to create your first experiment. You have a couple of choices here 1) You can create a blank experiment 2) You Can create a experiment based on the templates provided. The option 2 would help one to set up an experiment quickly and understand the various components of the experiment. When you choose from the samples, you can either open it in ML Studio and view it in the gallery. I feel tools like Azure ML studio provide a great first step in exploring the power of Machine Learning/Data Science.

 One of the components in the above image is the Enter Data component. This component is primarily used for defining column headings, these column headings can be used to assign to the data sets that are read through the Reader component. In this case in the Reader component, we are downloading a file from a website. Since in this example the headers of the file downloaded by Reader component were not user friendly, we use the Enter Data component to provide meaningful column_names. In this example we have used the column_names to be in the csv format. For example please see the image below for Enter Data component:
In the image above the column_name is the header in the csv file and the other below it are actual column names which would be used to assign it to the data set ready by reader component.

Monday, August 3, 2015

Tableau and R Integration...

Among the Data visualization tools, tableau is one of the leading tools which is used by lot of organizations in various capacities. The types of reporting range from operational to really sophisticated data visualizations combining various data sources. With R growing to be a language of choice for Data Science related activities such as Machine Learning and Data Mining, it is being integrated with a variety of Tools. Given such a scenario it was natural to expect the integration between tableau and R to happen. Please see the link below for a video on R and tableau integration.
Quoting tableau" Tableau Server can also be configured to connect to an instance of Rserve through the tabadmin utility, allowing anyone to view a dashboard containing R functionality".
Also the link contains a whitepaper on integration between the two tools, please check out the same.
Lot of interesting things happening in the Data Science these days...