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Big DataData ScienceData Science 101FeaturedUnderstanding Big Data

Data Science, Data Analytics, Data Everywhere Jargon can be downright intimidating and seemingly impenetrable to the uninformed. While complicated vernacular is an unfortunate side effect of the similarly complicated world of machines, those involved in computers, data and whole host of other tech-intensive sectors don’t do themselves any favors with

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StickyData ScienceData Science 101FeaturedMachine Learning

We hear the term “machine learning” a lot these days (usually in the context of predictive analysis and artificial intelligence), but machine learning has actually been a field of its own for several decades. Only recently have we been able to really take advantage of machine learning on a broad

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Big DataData ScienceData Science 101FeaturedUnderstanding Big Data

If you are new to the field, Big Data can be intimidating! With the basic concepts under your belt, let’s focus on some key terms to impress your date, your boss, your family, or whoever. Let’s get started: Algorithm: A mathematical formula or statistical process used to perform an analysis of

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Data ScienceFeaturedinfographics

How many hours do you think the world spends watching TV per day? Well, it’s a lot, we are sure of that, especially given the popular growth of Netflix! According to eMarketer, in 2015, American adults spent a daily average of 5.5 hours with video content. In addition, a recent

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Data Science 101Featured

Data Scientist are in high demand, ranked the #1 profession in America on Glassdoor. And according to Forbes, an additional 1,700 job openings paying an average salary of $116,000 US dollars are available, contributing to an exponential expansion of the field. So are you interested in Data Science but not sure

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BI & AnalyticsData Science 101FeaturedMachine Learning

One of most popular posts this year came from Ferris Jumah, a data scientist at LinkedIn, who mapped the most popular skills of data scientists by scraping LinkedIn profile data. One of the common comments amongst data scientists who came across this post- as with most of our posts focused

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FeaturedMachine Learning

Machine learning- to put it mildly- is an incredibly broad and varied field, with multitudes of applications. Thus, writing a list entitled “10 Machine Learning Experts You Need to Know” proves challenging for a number of reasons. Firstly, I’ve restricted my ten picks to those currently working in the field-

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ConversationsFeaturedMachine Learning

In the first part of our interview with Sean Owen, Cloudera‘s Director of Data Science, we discussed the relationship between machine learning and Hadoop, the future of Apache Mahout and why machine learning has become such hot property. In this part of our discussion, we delved into the future of

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Data Science 101FeaturedUnderstanding Big Data

‘SQL is outdated’. ‘RDBMS can no longer meet businesses’ data management needs’. ‘New database technologies like NoSQL are the solution for today’s enterprises’. We hear statements like these alot, both inside and outside the database technologies industry. But are they accurate? Is SQL a thing of the past, and are

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