With all of the discussion about Big Data these days, there is frequest reference to the 3 V’s that represent the top big data challenges: Volume, Velocity, and Variety. These 3 V’s generally refer to the size of the dataset (Volume), the rate at which data is flowing into (or out

In this day and age, you’d be hard-pushed to find an industry-leading business whose marketing strategy doesn’t heavily feature social media. Indeed, for businesses of all sizes, Twitter and its kin offer huge opportunities for promotion and increasing brand awareness. Getting people to retweet your site/product opens you up to

By now, it’s almost impossible to not have heard the term Big Data- a cursory glance at Google Trends will show how the term has exploded over the past few years, and become unavoidably ubiquitous in public consciousness. But what you may have managed to avoid is gaining a thorough

Chris Pearson is a partner at Big Cloud. Big Cloud is a talent search firm focusing on all things Big Data. We talked to him about Big Cloud’s unique position in Big Data recruitment, and what he looks for in a data scientist. Could you tell me a little about yourself

Hamed Pirsiavash, a postdoctoral scholar from MIT, is developing a new activity-recognition algorithm to identify what’s happening in video files. Pirsiavash and his former thesis advisor, Deva Ramanan of the University of California at Irvine, will present the video recognition programme at the Conference on Computer Vision and Pattern Recognition

Over the past two months, SAS have been staking their claim for in-memory analytics for Hadoop. They have been providing data scientists and analysts with fast, powerful new tools for advanced analytics. First, there was SAS In-Memory Statistics for Hadoop, allowing users to build, explore and model data, all within

As data science becomes more mainstream, it appears that Python is muscling out R as the programming language of choice. The following factors are at work: 1. R not really a language R is considered by some to be an “interactive environment for doing statistics”. This makes it harder for

Remi Mollicone is the Chairman of CFAR-m. CFAR-m is an original method of aggregation based on neural networks which can summarize with objectivity, the information contained in a large number of complex variables. CFAR-m solves the major problem of fixing the subjective importance of each variable in the aggregation (It avoids

Simpson’s paradox happens when behaviour at the group level is different from the behaviour of its subgroups. For example, consider a mobile app with 10,000 Android users and 5,000 on iOS. If we make the assumption that all users spend equally, then it may make more sense to prioritize development