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InfoQ Homepage Articles Article Series: Getting a Handle on Data Science as a Software Developer

Article Series: Getting a Handle on Data Science as a Software Developer

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Data science is fast becoming a critical skill for developers and managers across industries, and it looks like a lot of fun as well. But it’s pretty complicated - there are a lot of engineering and analytical options to navigate, and it’s hard to know if you’re doing it right or where the bear traps lie. In this series we explore ways of making sense of data science - understanding where it’s needed and where it’s not, and how to make it an asset for you, from people who’ve been there and done it.


Solving Business Problems with Data Science

Enterprises are increasingly realising that many of their most pressing business problems could be tackled with the application of a little data science. In this article, the first in a series, Francine Bennett looks at the foundations of a successful business-orientated data science project.

Case Study: Selecting Big Data and Data Science Technologies at a large Financial Organisation

Adopting Big Data and Data Science technologies into an organisation is a transformative project similar to an agile transformation and with many similar challenges. In this article, Christian Prokopp describes such a project for a FTSE100 financial services company.

Getting Started with Machine Learning

Rafael Fernandes makes a quick introduction to the machine learning field, exploring both supervised and unsupervised approaches.


From Raw Data to Data Science: Adding Structure to Unstructured Data to Support Product Development

With unstructured database technologies like Cassandra, MongoDB and even JSON storage in Postgres, unstructured data has become remarkably easy to store and to process. Software and data engineers alike can succeed in a world (mostly) free from data modelling, which is no longer a prerequisite to collecting data or extracting value from it. In this article, Rishi Nalin Kumar talks about adding structure to unstructured data and more.

Data Science Up and Down the Ladder of Abstraction

Although Clojure lacks the extensive toolbox and analytic community of the most popular data science languages, R and Python, it provides a powerful environment for developing statistical thinking and for practicing effective data science. As a data scientist working primarily in the Clojure programming language, Henry Garner is often asked why he doesn't use one of the more popular alternatives. In this article he is setting out his reasons for adopting Clojure.

Book Review: Cathy O’Neil’s Weapons of Math Destruction

"Big Data has plenty of evangelists, but I'm not one of them," writes Cathy O’Neil, a blogger ( and former quantitative analyst at the hedge fund DE Shaw who became sufficiently disillusioned with her hedge fund modelling that she joined the Occupy movement.


Series Manager

Francine Bennett is the CEO and cofounder of Mastodon C. Mastodon C are agile big data specialists, who offer the open source Hadoop and Cassandra-powered technology and the technical and analytical skills which help large organisations to realise the potential of their data. 

She is a recognised expert in the application of analytics and ‘data science’ techniques. She has been an invited speaker to the Royal Society and is an adviser to the Cabinet Office on the better use of data. She has a first class degree in Maths & Philosophy, and was previously an analytics lead at Google. She is also a trustee of DataKind UK.




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