Featured Blog Posts – November 2012 Archive (24)

Data Science Central Weekly Digest - November 30

This newsletter contains three sections: announcements from sponsors (at the top), featured contributions from the Data Science Central community, and valuable resources for big data, data science and analytic practitioners (at the bottom).

Sponsored Listings…


Added by Vincent Granville on November 30, 2012 at 8:30am — No Comments

Data scientists making $300,000 a year | Wall Street Journal.

Without having anyone working (reporting to) them, according to the Wall Street Journal.

My opinion:

  1. Highly paid data scientists having nobody reporting to them? Indeed they have robots (computer programs working…

Added by Vincent Granville on November 29, 2012 at 4:30pm — 3 Comments

Do You Really Need a Data Scientist?

Live Simulcast Panel Discussion on Dec. 12.

Everybody has an opinion on Santa Claus. There are tons of books, recipes, decorations and family traditions dedicated to his arrival for a successful Holiday season. But how about Data Scientists? Is this new position…

Added by Vincent Granville on November 29, 2012 at 10:30am — No Comments

Develop analytical skills to unleash the power of Big Data


Added by Vincent Granville on November 29, 2012 at 8:00am — No Comments

Data Veracity


Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V's of Big Data: Volume, Velocity and…


Added by Michael Walker on November 28, 2012 at 3:00pm — No Comments

Unstructured content processing with MarkLogic and Tableau

Join Fernando, Chief Technologist, Enterprise, for MarkLogic in a discussion on how to combine the power of Tableau™ against a NoSQL Database that is able to perform analytics against disparate data sets composed of structured, unstructured and graph data. The result is an unprecedented level of flexibility and power from being able to correlate and analyze “Big Ugly Data” in its natural form. The use case will…

Added by Vincent Granville on November 28, 2012 at 10:30am — No Comments

Big data Analytics – A disruptive technology !!

Big data is the most talked term these days in the analytics world. It will have big transformative impact on all the aspects of the business.

Most of the companies now have realized that there is a huge competitive advantage in analyzing the humongous data quickly & effectively for future insights.
Big data analytics is the disruptive technology…

Added by Sandeep Raut on November 24, 2012 at 6:12pm — No Comments

Data Science Tools

Top 50 data science / big data tools, described in less than 40 words, for decision makers. Please help us: any definition that you fill will have your name attached to it: send your definition or new term and definition to [email protected]


Added by Vincent Granville on November 24, 2012 at 1:30pm — 6 Comments

An Open Letter to CEOs of US Software Companies

October 22, 2012StatSoft CEO Dr Paul Lewicki

Dear Colleagues,

As some of you may know, StatSoft has launched a program to offer free Enterprise Business Analytics software to aid struggling companies in Greece, Portugal, and Spain with the intent to…


Added by Vincent Granville on November 21, 2012 at 9:00am — 1 Comment

Data Science Central Weekly Digest

Featured Discussions

  1. R + Hadoop = Data Analytics Heaven…

Added by Vincent Granville on November 18, 2012 at 5:00pm — No Comments

Data as Strategic Asset

Transforming into a data-driven organization - turning information into actionable insights is a three (3 ) part strategy:

• Technology – build a modern BI/DW architecture & analytics ecosystem with…


Added by Michael Walker on November 14, 2012 at 9:22am — No Comments

Digesting Big Data

Big Data holds a big promise. But has that promise paid out already? Or are you heading for Big Dollar Disaster? Many take inventory of their data and find out they have terabytes of data lying around. Surely something should be done with that, so here’s how we see a lot of companies going about implementing ‘something’ for their Big Data.

  • Buy hardware, set up a cluster and install some flavor of Hadoop.
  • Break their brains to figure out how to run map-reduce…

Added by Jos Verwoerd on November 13, 2012 at 2:08am — No Comments

S3 as Input or Output for Hadoop MR jobs

How to use s3 (s3 native) as input / output for hadoop MapReduce job. In this tutorial we will first try to understand what is s3, difference between s3 and s3n and how to set s3n as Input and output for hadoop map reduce job. Configuring s3n as I/O may be useful for local map reduce jobs (ie MR run on local cluster), But It has significant importance when we run elastic map reduce job (ie when we run job on cloud). When we run job on cloud we need to specify storage location for input as…


Added by Rahul Patodi on November 11, 2012 at 8:00am — No Comments

Article: Data Mining Misconceptions - The 50/50 Problem

When creating a predictive model, data miners need to “tune” it to make the right kind of mistakes.  Setting the cut-off point between ‘promising’ and ‘unpromising’ depends a lot on our client’s biggest concern -- missed opportunities or false alarms.

Data Mining Misconceptions #1: The 50/50 Problem…


Added by Daniel R. Graettinger on November 8, 2012 at 5:00pm — 2 Comments

Big Data Debunks View That Job-Hoppers Make Bad Hires | Forbes

Jim Meyerle is co-founder and EVP of Strategy and Finance forEvolv On-Demand.…


Added by Vincent Granville on November 8, 2012 at 1:49pm — No Comments

Pundit Election Forecasts All Wrong, Nate Silver Perfectly Right. | TechCrunch

Predicting election results in the 50 states is actually much more easy than most people think. West Coast and East Coast are democrat, Midwest, Texas etc. are mostly republican (the Midwest becoming more republican because the population is aging due to brain drain by young, smart people - mostly democrats). So indeed the task is not about correctly predicting results for the 50 states, but simply predicting…


Added by Vincent Granville on November 8, 2012 at 11:00am — No Comments

Big Data pioneers show the way

There is little time, about 3 or 4 years, if you wanted to process a large amount of textual data or web logs, you need to mobilize large servers and implement consistent SQL programs, long to be developed long and long to give results. Fortunately requests were few and generally volumes were measured at most in terabytes. Now e-commerce and social media have been largely developed, and many companies see their customer relationships, and therefore their survival, entirely dependent on the…


Added by Michel Bruley on November 8, 2012 at 3:58am — No Comments

R + Hadoop = Data Analytics Heaven


Hadoop (MapReduce where code is turned into map and reduce jobs, and Hadoop runs the jobs) is the most well known technology used for "Big Data" because it allows an organization to store huge quantities of data at very low…


Added by Michael Walker on November 7, 2012 at 3:57pm — No Comments

Feature selection for efficient modeling

Feature selection, also known as variable selection, feature reduction, attribute selection or variable subset selection is the technique of selecting a subset of relevant features for building robust learning models (Source: Wikipedia). Data mining problems may involve hundreds, or even thousands, of variables that can potentially be used as inputs. As a result, a great deal of time and effort may be spent examining which variables to include in the model. Feature selection allows us to…

Added by Venky Rao on November 3, 2012 at 10:03pm — 2 Comments

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