In an earlier article, we spoke about why businesses should be using Data Science, machine learning and AI. In the second of the series, we look at what Data Scientists do and what benefit they can bring to an organisation. 

In the modern business parlance:

  • Data Engineers work with storing, housing, forming and managing the collection of data
  • Data Analysts work on data history; determining what has been, and
  • Data Scientists work in the data’s future; determining what will be.

The science of data is a predictive discipline which uses the mathematics and methodology of physics to infer causal and law-like patterns. But the world of human data is not law-like. It is often messy and unpredictable, especially at the individual level - and so the science of data is not physics. It is perhaps, equally an artful endeavour.

To make sense of sometimes curious, odd and counter-intuitive predictions, a Data Scientist needs an understanding of business to determine sense and to discard meaningless outcomes. They need computer programming expertise to manage the scale of human data, and to build the tools that automate predication and use its results and they need design and communication skills to translate the technical results into meaningful business action. 

The co-incidence of skills needed to predict the future is an expanding, practical toolset, not found solely in the ‘narrow’ professions it builds upon. Few Programmers are Data Scientists; few Mathematicians are Business Analysts, few Business Analysts are Programmers. Few, of any of these have enough broad business experience and can communicate and understand business strategy. This is the domain of a Data Scientist and it would be unthinkable to begin any programme of Digital Transformation without someone bringing all of this together as insight.

The training of a Data Scientist is an intensive programme of applied and theoretical learning in each of these areas. A demanding speciality that rewards both its practitioners - and their employers. The passionate Data Scientist will expand their skill set further than ever before, and certainly further than many of their peers. And then, more so...

The employer, lucky enough to have on-board, one or more, trained Data Scientists, is rewarded with a whole new way of seeing their business, its future and its past. Plus an ability to make business decisions informed by real trends as measured, observed and qualified: enhancing business insight of its leaders; inspiring new strategies and revealing failures quickly and with a known confidence. What’s not to like?

Do you have a budding Data Scientist in your organsition, or can see the need to build a capability from scratch? Then first consider QA's Understanding Data Science and Big Dataand Understanding Machine Learning programmes to introduce these concepts . For practitioners that need more depth of understanding, please see QA’s complete range of training for Data Scientists and Analytics and those interested in Machine Learning and Artificial Intelligence (AI).


About QA Group

QA helps individuals and organisations achieve their potential through world-class Learning Strategy and Solutions. This includes: training and certification, innovative Talent Solutionsthat solve both business critical skills and capability gaps, Business Transformation solutions, enabling change and transformation through engagement and education of workforces, and Managed Learning Services. In addition, QA provide consultancy, apprenticeships and post graduate degrees on a range of technical, business and leadership subjects. With over 22 UK training centres – including Apprenticeships, Consulting and Cyber Academies – and a range of online learning options, QA offer an unparalleled set of learning solutions to both private and public sector organisations.

About the author

Michael began programming as a young child, and after freelancing as a teenager, he joined and ran a web start-up during university. Around studying physics and after graduating, he worked as an IT contractor: first in telecoms in 2011 on a cloud digital transformation project; then variously as an interim CTO, Technical Project Manager, Technical Architect and Developer for agile start-ups and multinationals. His academic work on Machine Learning and Quantum Computation furthered an interest he now pursues as QA's Principal Technologist for Machine Learning. Joining QA in 2015, he authors and teaches programmes on computer science, mathematics and artificial intelligence. He co-owns QA's Data Science and Machine Learning curriculum with his colleague Lianheng Tong. His areas of expertise include: Data Science and Machine Learning; Programming; Data and Big Data Tools; Agile; Project Lifecycles.

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