Key Data and AI trends in 2017

This year, 2017, has been quite a busy year for artificial intelligence and data science professionals. In some ways, this is the year when AI truly began to be debated and discussed, from frameworks and technologies to ethics and morality. This is the year when opportunities for AI-driven improvement in businesses began to be examined […]

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Lessons from Agile in Data Science

Over the past year and a few months, I’ve had a chance to lead a few different data science teams working on different kinds of hypotheses. The engineering process view that the so-called agile methodologies bring to data science teams is something that has been written about. However, one’s own experiences tend to be different, […]

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The Expert System Anachronism in the Data Science and AI Divergence

Although the data science and big data buzzwords have been bandied about for years now, and although artificial intelligence has been talked about for decades, the two fields are irrevocably inter-related and interdependent. For one thing, the wide interest in data science started just as we were beginning to leverage distribute data storage and computation […]

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Hypothesis Generation: A Key Data Science Challenge

Data scientists are new age explorers. Their field of exploration is rife with data from various sources. Their methods are mathematics, linear algebra, computational sciences, statistics and data visualisation. Their tools are programming languages, frameworks, libraries and statistical analysis tools. And their rewards are stepping stones, better understanding and insights. The data science process for […]

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Azure ML Studio and R

A decade ago, Microsoft looked very different from the Microsoft we see today – it has been a remarkable transformation. One of the areas where MS have made a big push is machine learning and data analytics. Although the CRAN repository is going strong with >10,000 packages as of today, the MRAN repository (Microsoft’s Managed […]

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