Data Science Resource Articles

Data ScienceData Science 101Understanding Big Data

As the scale of data grows across organizations with terabytes and petabytes coming into systems every day, running ad hoc queries across the entire dataset to generate important metrics and intelligence is no longer feasible. Once the quantum of data crosses a threshold, even simple questions such as what is

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Data ScienceData Science 101

I recently stumbled across a research paper, Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US, which piqued my interest in derivative uses of data, an ongoing research interest of mine. A variety of deep learning techniques were used to draw conclusions about relationships

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StickyData ScienceData Science 101Machine 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 101Understanding 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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Big DataData ScienceData Science 101

This post appeared originally in the dataArtisans blog Six Common Streaming Misconceptions Needless to say, we here at data Artisans spend a lot of time thinking about stream processing. Even cooler: we spend a lot of time helping others think about stream processing and how to apply streaming to data

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Data ScienceData Science 101Understanding Big Data

Competent analysis is not only about understanding statistics, but about implementing the correct statistical approach or method. In this brief article I will showcase some common statistical blunders that we generally make and how to avoid them. To make this information simple and consumable I have divided these errors into

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