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Data ScienceData Science 101Machine Learning

This article is part of a media partnership with PyData Berlin, a group helping support open-source data science libraries and tools. To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. Miroslav Batchkarov and other experts will be giving talks

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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 101Understanding Big Data

The late data visionary Hans Rosling mesmerised the world with his work, contributing to a more informed society. Rosling used global health data to paint a stunning picture of how our world is a better place now than it was in the past, bringing hope through data. Now more than

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Data ScienceData Science 101

The rise of the data scientists continues and social media is filled with success stories – but what about those who fail? There are no cover articles praising the failures of the many data scientists that don’t live up to the hype and don’t meet the needs of their stakeholders.

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Big DataData Science 101Understanding Big Data

Oftentimes while analyzing big data we have a need to make checks on pieces of data like number of items in the dataset, number of unique items, and their occurrence frequency. Hash tables or Hash sets are usually employed for this purpose. But when the dataset becomes so enormous that

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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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