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 problems in their organizations. A good first step

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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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Financial data finally starting to pay off If you are in Finance, you would have read at least one of the many predictions articles that poured from all directions on the internet in the past month. This is not trying to be yet another one but focus on the CX

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Journey Science, being derived from connected data from different customer activities, has become pivotal for the telecommunications industry, providing the means to drastically improve the customer experience and retention. It has the ability to link together scattered pieces of data, and enhance a telco business’ objectives. Siloed approaches are becoming

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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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Let’s face it: Women in tech are few and far between. According to a recent ‘Women in Tech’ report, women held 57% of all professional occupations in 2015, yet they held only 25% of all computing occupations. There are, however, those women in tech “unicorns” that magically appear every so

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