Understanding Big Data

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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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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Big DataTechnology & ITUnderstanding Big Data

The latest version of MySQL is one of the world’s most popular databases. It is open source, reliable, compatible with all major hosting providers, cost-effective, and easy to manage. Many organizations are leveraging the data security and strong transactional support offered by MySQL to secure online transactions and enhance customer

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Big DataCybersecurityUnderstanding Big Data

Big data is big news at the moment. The latest Yahoo! data breach, which affected data from over 500 million customers, continues to be discussed by press and public alike, while the role of big data in predicting and even influencing last year’s US election brought the term firmly into

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Big DataData ScienceData Science 101FeaturedUnderstanding Big Data

Data Science, Data Analytics, Data Everywhere Jargon can be downright intimidating and seemingly impenetrable to the uninformed. While complicated vernacular is an unfortunate side effect of the similarly complicated world of machines, those involved in computers, data and whole host of other tech-intensive sectors don’t do themselves any favors with

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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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Big DataData ScienceData Science 101FeaturedUnderstanding 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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BI & AnalyticsData ScienceResourcesUnderstanding Big Data

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

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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 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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Big DataData ScienceUnderstanding Big Data

Social networks, in one form or another, have existed since people first began to interact. Indeed, put two or more people together and you have the foundation of a social network. It is therefore no surprise that, in today’s Internet-everywhere world, online social networks have become entirely ubiquitous. Within this

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Big DataTech TrendsUnderstanding Big Data

This is Part 2 of Data Strategy series discussing “How To” following Part 1 of Data Strategy that dealt with “5 ‘W’s of Data Strategy”.  Strategy is about doing the right things and tactics is about doing things right. Data Strategy is about doing the right things to distill Data

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Understanding Big Data