Data Science links

05/07/2012 -

03/08/2012 - - building data science teams

02/29/2012 - 

“For the last 20 years everything in IT has revolved around structured data in a traditional, transactional database,” he said. That model was changing. He said Big Data was fundamentally different in four ways:

1.It’s a rich data model, it contains both structured and unstructured data, so it could be things like video, or Twitter feeds, as well as structured data.
2.The size is big—no longer terabytes but petabytes of data. And it is multisource data.
3.It is real time. “If my Google search took me to Monday morning to do, I wouldn’t do very many of them.”
4.It is collaborative. Many people will be working on it at the same time.

01/12/2012 -

01/11/2012 - - overview of Big Data

01/06/2012 - Python, Perl, BASH and AWK

01/03/2012 - - books

01/02/2012 -

01/01/2012 - Data analytics requires knowledge in multiple fields. For instance, a math major might need some familiarity with social sciences such as sociology, psychology, or biology. And candidates with degrees in the social sciences often lack sufficient math training. 

10/26/2011 - Skills - Python, SQL, Hadoop, Hive, and Map/Reduce paradigms
10/13/2011 - - Teach yourself data science (book list)

10/13/2011 - The topics covered will include (a subset of):

10/13/2011 - - big data blog

10/13/2011 - - data science article (Forbes)

10/13/2011 - - data scientist skills

10/13/2011 - - data jujitsu, data vomit

10/13/2011 - - Eye on Analytics (blog)

10/06/2011 - http;//

10/06/2011 - - Data Without Borders 

09/26/2011- - GigaScience Journal

09/21/2011 - -  Google Refine & Data Science Toolkit

09/21/2011 -

09/21/2011 - - BuzzData

09/21/2011 - - SailThru

09/21/2011 - - dream job of the future?

09/21/2011 - - Strata 2011

09/21/2011 -

09/21/2011 - - Platfora

09/21/2011 - - Platfora/Hadoop

09/21/2011 - - R

09/21/2011 - - R blogs

09/21/2011 - m6d & Claudia Perlich

09/21/2011 - - good overview of Data Science

08/11/2011 -
Call For Papers - CFP
We solicit original unpublished research and technical papers that demonstrate contemporary research in all areas of Data Science and Engineering. All registered accepted papers will be contemporary in IEEE Xplore . For submission of papers, IEEE guidelines are to be followed. Suggested content areas include but are not limited to:
Algorithms for large data sets

Business Intelligence

Cluster, Cloud, and Grid Computing

Crowd Sourcing & Social Intelligence

Computational Biology & Bioinformatics

Data-Centric Programming

Data Modelling & Semantic Engineering

Data, text and web mining & visualization

Interoperability and Data Integration using open standards

High performance Scientific/ Engineering/Commercial Applications

Infoscience and Computational Informatics

Information Discovery and Query Processing

Information Network Analysis

Domain-Specific Data Management

Knowledge based Software Engineering

Knowledge Engineering

Machine Learning for Natural Language Computing

Management of Very Large Data System

Peer-to-peer Algorithms and Networks

Statistical Computing

Web Engineering

Paper Submissions will be reviewed and evaluated based on originality, technical quality and relevance to conference

The rapid development of computer science and information technology in the last couple of decades has generated massive amount of data and fundamentally changed every field in science and engineering. Many disciplines are now rich in data and tend to adopt data science or data-intensive engineering methodologies to do research and development. Scientific approach to process data involving the engineering aspects as well, would lead to major strides in the domains of data, information and knowledge which contribute to the evolving knowledge society. This conference is intended to take stock of the trends and developments in the globally competititve environment as well as to provide indicators for future directions to researchers and practitioners


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05/17/2011 -



The art of data science is gathering the data, finding the trends and information that can be beneficial to your company, performing statistical research and wrapping everything into creative ideas or platforms for your business to use. As technology and Internet platforms advance, the amount of data out there is only going to continue to grow.