While the field of data science is not tied directly to Big Data, advances in one tends to produce advances in the other. Big Data increases our ability to harvest and process data, while data science allows us to dig into it for insights.
In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I’ve observed that some actually lack the necessary mathematical intuition and
Data Science Platforms: Myth v. Reality The phrase “data science platform” has been bandied about a lot recently — at conferences, in market research, and in tech publications like this one. Forrester named data science platforms a top emerging technology last year, and companies using data science at an enterprise
2017 is set to be a success for the IoT industry, as the number of connected things grows at soaring speeds. The time has come for businesses, consultancies, and entrepreneurs to tap into this opportunity, if they want to stay in the vanguard. Of the projected 8.4 billion IoT-enabled devices
Convince Your Boss! 5 Reasons to Attend the IoT Weekend You really want to come to our IoT workshop but you are not sure how to convince your boss to pay your ticket? Say no more. We’ve prepared some pretty good reasons for you (not that you do not know
There’s a part of data science that you rarely hear about: the deployment and production of data flows. Everybody talks about how to build models, but little time is spent discussing the difficulties of actually using those models. Yet these production issues are the reason many companies fail to see
Data-driven businesses are five times more likely to make faster decisions than their market peers, and twice as likely to land in the top quartile of financial performance within their industries. Business Intelligence, previously known as data mining combined with analytical processing and reporting, is changing how organizations move forward.
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
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
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
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