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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Big Data: Size and Velocity

One of the changes envisioned in the big data space is that there is the need to receive data that isn’t so much big in volume, as big in relevance. Perhaps this is a crucial distinction to make. Here, we examine business manifestations of relevant data, as opposed to just large volumes of data. What Managers Want From […]

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Insights about Data Products

Data products are one inevitable result and culmination of the information age. With enough information to process, and with enough data to build massively validated mathematical models like never before, the natural urge is to take a shot at solving some of the world’s problems that depend on data. Data Product Maturity There are some […]

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Data Science: Beyond the Hype

While there is justifiable excitement in the technology industry (and other industries) these days on the widespread availability of data, and the availability of algorithms to process and make sense of this data, I sincerely think (like many others) that the hype behind Big Data is somewhat unfounded. For many decades, “small data” have been […]

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