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Senior Technical Talent Advisor and Community Development - A.I, Machine Learning and Data Science.

Director & Co-Founder @ www.Abso-Fashion-Lutely.co.uk
Director & Founder of Data Science & Big Data Analytics IT Job Board http://datasciencebigdataanalytics.jobboard.io/

Roles:

Data Scientist, Statistician, Insight Analyst, Chief Data Scientists, Data Architect, Data Engineer, Data Analyst, Statistics Consultant, Research Engineer, Quantitative Analyst, Developer, Engineer, Pre-Sales / Post Sales engineer, Sales Engineer, Software Engineer, Systems Engineer, Technical Evangelist, Client Services Engineer, Architects (Cloud, Solutions & Enterprise), Cloud & Big data Analytics, Microsoft Azure and consulting roles.

The above is not an exclusive list and I work on a diverse range of roles and technologies. Contact me:  0044 788 135 1363

I am currently studying with Udacity to further my career in Data Science, Big Data Analytics, Programming, Algorithms, Artificial Intelligence, Computer Science, Statistics, Physics, Psychology & Visualizing Algebra.

I specialise in the following:

• Talent Manager and also responsible for expanding our internal team; vetting, meeting & technically testing candidates
• Providing advice around legal, accounting and general recruitment to the community
• Utilising the latest social media strategies for recruitment Twitter,Github Etc ..
• Setting up interviews, negotiating extensions, offers & contracts
• Developing new relationships and bringing in new business
• Client & account management
• Attending and arranging technical conferences to better my understanding of the technologies and markets I specialise in

I own & run the following groups on LinkedIn:
• ASP.Net MVC 3, MVC 4 & MVC5 Ninjas
• Cloud & Big data
• Microsoft Azure Ninjas
• Data Scientist & Analytics UK
• HTML5 Ninjas
• Java Blackbelt
• Hadoop Experts UK & EMEA
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Wednesday, 3 July 2013

Is Big Data Analytics for Real?



Very Interesting


The term ‘big data‘ has generated a lot of attention in the past eighteen months or so, to the point that it has been overused and oversold at times. Clearly a candidate for the peak of the ‘hype cycle’. Many people ask me whether their data is ‘big enough’ to qualify as ‘big data’. Are petabytes a must or will terabytes or even gigabytes qualify?

I tell them that this is the wrong question to ask. Two different examples serve to illustrate: First, consider basic census data about all 7 billion people; is this ‘big’? Well, with minimal effort it will fit in memory on most high-end servers. So is it big? No? Well, try loading it into a traditional database – I bet it takes at more than a day to merely get in. Oh, so it is big after all …. Well, not so fast. A C program can process all this data and calculate say, the median age for each gender that runs in minutes. So its not big ..?

Second example: Think of a few hundred individuals along with a small sample of their genetic information, which might be a few hundred thousand features per person. Big? Not in size – a few dozen megabytes at best. But try to slice and dice this data using a traditional OLAP tool. Many lifetimes are not enough to view all slices.
Lessons? First, traditional technology makes small amounts of data appear big for no reason. So new technology is needed. Second, even small data sets that are ‘wide’ appear big when it comes to analysis. So statistics, machine learning and data mining must be used rather than traditional slice and dice.

Big data is about counting, not queries. Also having ‘wide’ data, rather than lots of data, make for a ‘big data’ problem.

Info From :
Dr. Gautam Shroff
VP & Chief Scientist @ TCS Innovation Labs

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