Posts Tagged

R

Data ScienceData Science 101Resources

The R language is often perceived as a language for statisticians and data scientists. Quite a long time ago, this was mostly true. However, over the years the flexibility R provides via packages has made R into a more general purpose language. R was open sourced in 1995, and since

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Data ScienceData Science 101Resources

Categorical data is a kind of data which has a predefined set of values. Taking “Child”, “Adult” or “Senior” instead of keeping the age of a person to be a number is one such example of using age as categorical. However, before using categorical data, one must know about various

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Artificial IntelligenceData ScienceEventsMachine LearningUnderstanding Big Data

Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit’s work involves applying machine learning techniques to

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Data Science 101infographics

Choosing the right language for data analysis can be almost as complicated as actually learning the language. For many reasons, R and Python are two of the most popular: R is often praised for its great features for data visualization, as it was developed with statisticians in mind; plenty of programmers love multi-purpose Python for its so-simple-a-child-could-do-it syntax. Why

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Data Science 101Machine Learning

This article originally appeared at Sharp Sight Labs. Follow Joshua Ebner, the founder of Sharp Sight Labs, on Twitter. Read more here. Over and over, when talking with people who are starting to learn data science, there’s a frustration that comes up: “I don’t know which programming language to start

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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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Big DataData Science 101

2014 has been a huge year in big data- and big data publishing. Viktor Mayer-Schoenberger and Kenneth Cukier re-published and added an extra chapter to their bestselling “Big Data”; Nate Silver graced the publishing world with his presence once more with the Best American Infographics of 2014. We’ve compiled a

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Data ScienceTransportation & Logistics

Success stories of how data-driven practices can revitalise businesses are rife today, but there are few as compelling as the story of Ford. In 2006, the legendary car manufacturers were in trouble; they closed the year with a $12.6 billion loss, the largest in the company’s history. As we reported

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Data Science

Summary: In the past, R seemed like the obvious choice for Data Science projects.  This article highlights some of the issues, such as performance and licensing, and then illustrates why Python with its eco-system of dedicated modules like Scikit-learn, Pandas and others has quickly become the rising star amongst Data

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Data Science

When business leaders read about (and tackle) Big Data, there is a lot to take in. The field is developing so dynamically that many of the industry buzzwords will not have existed until a few short years ago. Just a short list of some programming languages is enough to make

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