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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Quora Data Science Answers Roundup

I’m given to spurts of activity on Quora. Over the past year, I’ve had the opportunity to answer several questions there on the topics of data science, big data and data engineering. Some answers here are career-specific, while others are of a technical nature. Then there are interesting and nuanced questions that are always a […]

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