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Big data &

cross-platform analytics MOOC lectures Pr. Clement Levallois

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What is a data scientist? [or, a guide for business to spot good ones and recruit them!]

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Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

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+ Math and stats knowledge • Maths and stats are excellent foundations

• But a data scientist has a different mindset

– Focuses on accuracy of prediction, not causality

– Even if this is not “elegant” in terms of formal models

– Ready to use any bit of information available in the data (text, networks, …)

– See the slide deck on “Machine Learning” for details

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+ Hacking skills • Ability to think “out of the box”

– As an econometrician, and as a computer scientist, as a computational linguist and a network analyst!

– Concerned with scale and speed

– Not dependent on packaged software • Aware of, and contributing to developments in open source • Following current developments in different academic fields

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+ Substantive expertise

• Substantive expertise = grasp of the business logic – Many jumps of optimization come from a good knowledge

of the specificities of the domain

– These domains can be quite complex!

– Data scientists must be able to understand and translate these business specificities into their data models

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A data scientist should be able to… – Discover interesting angles in the dataset

• You see worthless metadata? I see gold!

– Choose from a wide choice of techniques across social and natural sciences

• Statistics, machine learning, network analysis, natural language processing, etc. • From economics, physics, psychology, linguistics, computational science, genomics,

neuroscience, etc.

– Implement these techniques, possibly on large datasets

• Can you implement them in your programming language of choice? • Can you deal with large datasets (what if it doesn’t fit in memory?) • Can you be quick (and not ask for a couple of nights to run a script) • Can you be cheap (buying more hardware is not always a solution you can afford)

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How to hire and keep a data scientist in your business?

1. Find them where they hang out: stackoverflow, github, specialized communities on Twitter. Good profiles are PhD students near graduation, and / or leading developers of open source projects.

2. Allow plenty of time for their personal development

– Contributing to open source projects, attending conferences, working on personal projects on their working hours

3. Treat them not as executioners, but as business co-developers

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This slide presentation is part of a course offered by EMLYON Business School (www.em-lyon.com)

Contact Clement Levallois (levallois [at] em-lyon.com) for more information.