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March 2014 Blog Posts (13)

Register now to attend the Useful Business Analytics Summit in Boston

Do you know how to extract the most useful insights from your business data in order to make better, smarter, faster business decisions?
 …
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Added by Kirk Borne on March 29, 2014 at 11:30am — No Comments

Comparing apples and oranges

Interesting cartoon:

Click here to see a few more cartoons about analytics /…

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Added by Vincent Granville on March 23, 2014 at 1:36pm — 1 Comment

Building Web applications using Shiny R

Ever since I’ve started working on R , I always wondered how I can present the results of my statistical models as web applications. After doing some research over the internet I’ve come across ShinyR – a new package

from RStudio which can be used to develop interactive web applications with R.

Before going into how to build web apps using R, let me give you some overview about ShinyR.

Features:

  • No JavaScript/HTML knowledge…
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Added by suresh kumar Gorakala on March 23, 2014 at 1:30am — No Comments

Big Data A to Z – The Annotated Glossary of my Favorite Data Science Things

The extended annotated version of the "Big Data A to Z Glossary of my Favorite Data Science Things" is now live at: http://bit.ly/1g5NcBt

However, the original…

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Added by Kirk Borne on March 20, 2014 at 3:00pm — No Comments

The 10 Data and Analytics Innovators at Georgia Technology Summit

Georgia is a vibrant technology hub with strong entrepreneurial influences. As home to 13 Fortune 500 companies, the world’s busiest airport, and major research universities, our state is uniquely positioned to be on the cutting edge of technology, and specifically Big Data because its impact is truly cross sector.

Last Friday, the Technology Association of Georgia (TAG) announced the Top 40 Innovative Technology Companies in Georgia. We were…

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Added by Radhika Subramanian on March 18, 2014 at 10:30am — No Comments

Binary Classification with no training set

Hi guys,

I have this question. I have a dataset with unique IDs (people).

Each one has some attributes. I want to classify them to good and bad customers.

Since I donot have a training set (i.e. having for some IDs their score 0 or 1), how can I classify them to 2 groups?

I understand that regression (logistic for example) cannot take place since I donot have a dependent variable.

One solution could be clustering for example and have only 2…

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Added by Konstantinos Chlouverakis on March 16, 2014 at 5:43pm — No Comments

7 Key Skills of Effective Data Scientists

 

 

 

Are you looking for an exciting career opportunity that is just as paying as it is desirable? Harvard Business Review calls Data Scientists are the sexiest jobs of the 21st century. Data Scientist term coined when two people, DJ Patil and Jeff Hammerbacher, were trying to name their data…

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Added by Mousumi Ghosh on March 14, 2014 at 10:11pm — No Comments

Big Data Predictive Analytic (Advanced) Training program

This course is intended for those who have elementary knowledge of Hadoop ETL layer and want to get deeper into Machine learning techniques implementation in Hadoop.

The course incorporates an introduction to Big Data, the Data Analytics lifecycle, machine learning, R & R Studio, as well as Hadoop (MapReduce, HDFS, Storm, Kafka, Cassandra).

This course will be taught by industry experts, Dominique A. Heger Ph.D. and Dr. Alain Biem. Together they have 30+ years of experience…

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Added by Avi Ghosh on March 11, 2014 at 9:06pm — No Comments

Importance of detailed data in maintenance operation

Maintenance, Repair and Overhaul (MRO) operations are of extreme importance for some industries. They are particularly crucial for airplanes, helicopters, trains and heavy production machinery (such as power plant equipment). In fact, some equipment has a lifespan of 20 to 25 years, and over the course of its usage period, for every euro spent to purchase the equipment, MRO costs will be 3 to 3.5 euros.

 

In this market, the distribution of MRO activities varies widely between…

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Added by Michel Bruley on March 10, 2014 at 2:35am — No Comments

The Ideal Data Science Team

While everyone talks about Data Scientists and there is extensive discussion on who a Data Scientist is, I've experienced one factor being overlooked while creating an analytics project. Essentially, that the Data Scientist isn't a person... it is rather a team.

Now, I have been busy with Sensor Data analytics for the last few months. I might have missed reading up what some people have been talking about on this aspect. However, what I am writing here is more from specific experience…

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Added by Amogh Borkar on March 10, 2014 at 2:34am — 2 Comments

Usability & Usefulness - the twin virtues of any powerful analytical application

It is now a given that it is extremely difficult, if not impossible, for organizations to survive without 'Business intelligence' (BI) i.e., Analytic applications that help in decision making by providing the right actionable information to the right people in the management at the right time.

For any analytical application to be powerful enough to provide decision support, it has to be both useful and usable. Usability requires that the application be easy to…

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Added by Kesavan Hariharasubramanian on March 7, 2014 at 1:30am — No Comments

Few Exploratory Analysis techniques explained

In my previous blog post I have explained the steps needed to solve a data analysis problem. Going further, I will be discussing in-detail each and every step of Data Analysis. In this post, we shall discuss about exploratory Analysis.

What is Exploratory Analysis?

“Understanding data…

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Added by suresh kumar Gorakala on March 6, 2014 at 11:09pm — 2 Comments

The Gap in the State of Modern Analytics

The state of data analysis today is one of marginal increases in speed and ease of use. Combined with a shortage of skilled analysts and data scientists, our progress towards better storage and greater processing speed has led to a gap in the state of modern analytics. While companies continue to address data problems with personnel and storage, that gap continues to widen. The most common method for addressing this gap today is Machine Learning, but machine learning efforts have proven…

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Added by Radhika Subramanian on March 5, 2014 at 7:18am — No Comments

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