Building a Data Science Capability: Inspirational Keynote at Data Leaders Summit Europe

I’ve just delivered the inspirational keynote at Data Leaders Summit Europe, 2018. Lots of great engagement and feedback. In particular, it seems people liked a clear definition of what data science actually is and the practical steps (miss-steps) I took in building a capability at Sainsbury’s.

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Distinguishing Data Analytics from Data Science. Implications for your organisation

People often struggle distinguishing Data Analytics from Data Science. These are two related but distinct disciplines which are both important to a business. This post distinguishes Data Analytics from Data Science and lists the implications of that distinction for your organisation.

People often struggle distinguishing Data Analytics from Data Science. These are two related but distinct disciplines which are both important to a business. This post distinguishes Data Analytics from Data Science and lists the implications of that distinction for your organisation.

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13 Steps to Better Data Science: A Joel Test of Data Science Maturity

Data Science teams have different levels of maturity in terms of their ways of working. In the worst case, every team member works as an individual. Results are poorly explained and impossible to reproduce. In the best case, teams reach full scientific reproducibility with simple conventions and little overhead. This leads to efficiency and confidence in results and minimal friction in productionising models. It is important to be able to measure a team’s maturity so that you can improve your ways of working and so you can attract and retain great talent. This series of questions is a Joel Test of Data Science Maturity. As with Joel’s original test for software development, all questions are a simple Yes/No and a score below 10 is cause for concern. Depressingly, many teams seem to struggle around a 3.

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Data Science jargon buster – for Data Scientists

Data Scientists need to communicate without jargon so customers understand, believe and care about their recommendations. Here is a Data Science jargon buster to help with communicating data science project results.

Bamboozled. That’s your customers’ reaction to the Data Scientists in your organisation. Data Scientists need to communicate without jargon so customers understand, believe and care about their recommendations. Here is a Data Science jargon buster to help with communicating data science project results.

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Reproducible Data Science: faster iterations, reviews and production

Data Science involves applying the scientific method to the discovery of opportunities and efficiencies in business data. An essential part of the scientific method is reproducibility. Reproducible Data Science is essential for scientific credibility but also improves your Data Science efficiency in 3 keys ways – faster iterations, reviews and pushes to production.

Data Science involves applying the scientific method to the discovery of opportunities and efficiencies in business data. An essential part of the scientific method is reproducibility. Reproducible Data Science is essential for scientific credibility but also improves your Data Science efficiency in 3 keys ways – faster iterations, reviews and pushes to production.
If you start to apply the 7 Principles of Guerrilla Analytics your teams will quickly achieve reproducibility and benefit from these efficiencies.

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To Become A Data Scientist, Focus On Competencies before Skills

Too often, the path to becoming a Data Scientist focuses on technology skills in vogue rather than more permanent competencies. Competencies are a more general combination of skills, behaviours and knowledge. You can have great Powerpoint skills creating beautiful slides but still be a terrible communicator. It is competencies that are most important when you build a Data Science career that is robust to changing trends in skills like languages and technology platforms. This post describes the most important competencies for being successful in data science.

Too often, the path to becoming a Data Scientist focuses on technology skills in vogue rather than more permanent competencies. Competencies are a more general combination of skills, behaviours and knowledge. You can have great Powerpoint skills creating beautiful slides but still be a terrible communicator. It is competencies that are most important when you build a Data Science career that is robust to changing trends in skills like languages and technology platforms. This post describes the most important competencies for being successful in data science.

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The Rigour of Science is Essential for Successful Data Science in Business

The rigour of Science is essential for successful Data Science in business. The scientific method helps drive successful data science projects in business. This post will show you how.

The rigour of Science is essential for successful Data Science in business. The scientific method helps drive successful data science projects in business. This post will show you how.

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