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September 8th, 2016

The best vision is insight. - Malcolm Forbes And yet in the jargon laden world of big data analytics actionable insights are as difficult as driving through thick fog. Business users shouldn’t be bothered about understanding the magnanimity of data or the various technologies that come into play. Users for most part want solutions that do what they are supposed to without getting people to...

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September 7th, 2016

Big Data, the big daddy of buzzwords that has been done to death by industry pundits and leading publishers with their understanding of what it means and its impact on the way we do business. If technology thought leader Gartner is to be believed, Big Data is that fat lady whose size is 3 Vs big: Volume: The most self-explanatory aspect of the term which...

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June 7th, 2016

Our perspective on Analysis The accepted first steps in any data analysis project begin with a business-worthy questions/purpose. Start with a question Form an hypothesis Get hold of data Run some numbers to prove or disprove the hypothesis. Right? Maybe not. In our experience, the highest value questions originate from An understanding of the business fundamentals combined with A free-form exploration of data. Moreover the...

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May 23rd, 2016

Link to: 3 data lessons from Netflix on Data Driven Journalism Netflix knows what we like to watch, when, for how long, and a whole lot more. Whenever we select a program, the system recalibrates its data to personalize our experience. And again with each session. Within this, Netflix applies a myriad of cool data techniques, and many of the challenges and decisions behind their...

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May 18th, 2016

Where and how we look affects what we see. Big data offers us a window on the world. But large and easily available datasets may not show us the world we live in. For instance, epidemiological models of the recent Ebola epidemic in West Africa using big data consistently overestimated the risk of the disease’s spread and underestimated the local initiatives that played a critical...

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April 9th, 2016

Analytics. Their purpose is to surface insights that help you manage and grow your business. The casual observer might think that all analytics are created equally; they aren’t. There’s a spectrum, with some more powerful than others. Below are four important categories of analytics – each more powerful than the next. 1) Descriptive Analytics When you track data like email opens, clicks, and replies, you’re...

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February 2nd, 2016

Link to Caveat: This post is meant address people who are completely new to deep learning and are planning an entry into this field. The intention is to help them think critically about the complexity of the field, and to help them tell apart things that are trivial from things that are really hard. As I wrote and published this article, I realised it...

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October 30th, 2015

Link to Data Science Central: 3 Effortless Tactics to Be a Data Science Success in Business "Move out of the way – I am ready to model.” That is the typical sentiment of a Data Science team when given a business problem. However, in the context of a dynamic business, things are not that simple; instead, business needs require that the Data Science team be...

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