Boris Paillard, CEO, Le Wagon

female data scientist, woman leading teamDigitisation has affected virtually every company, in every sector and across every department. Processes are being automated, new insights into operations are being generated and new services are being created.

The improvements and efficiencies that this digitisation is generating are not simply a result of ‘better software’, but based on organisations’ ability to collate, analyse and manipulate massive volumes of data. The sheer scale of the data presents challenges as well as opportunities however.

Not only is it the case that the bigger the company, the bigger and more complex the data – it is also the case that the discipline of data science has not yet reached the well-documented, well-known and well-established processes and best-practices that we see in the software space. This is new ground for every organisation and the simple fact is that there is a lag on the skills education and training front when it comes to data science.

This is where we are seeing a shift in the ecosystem around data science. There is far greater understanding of the need to train people in how to apply data science skills to different departments, and how we can retrain people to meet the exploding demand for these skills.

One of the key issues now being addressed directly is the need for diversity in data science teams. Although there is explosive demand for data science skills, women occupy only a minority of these positions – in the UK, women represent less than 17% of the tech workforce.

Redressing this imbalance is crucial to building the value and validity of a field that is seeking to analyse and influence the lives of everyone. As such, it is a crucial moment for women to consider changing their career or to gain new skills that will help them make a bigger impact in their current role. There are huge opportunities, but it can also be somewhat boggling. For people that are interested in data science it’s important to understand that data science is a broad church.

My advice to people is that, there are lots of resources out there, but the best thing you can do is to start playing around with data, not only to experiment and get your head around the principles, but also to gain a better understanding of your own personal skills and objectives.

After that, there are a number of organisations you can work with to gain more formal education – including Udemy or Le Wagon and Imperial College London’s joint Imperial Data Science Intensive Course. But it is only by getting your hands dirty that you will find the right course and pathway into data science for you.

Boris PaillardAbout the author

After studying engineering and applied mathematics at Ecole Centrale Paris, Boris Paillard worked 3 years in investment banking. Passionate about tech & education, he quit his job to work on various tech products before founding Le Wagon to teach tech skills to creative people. For the past 7 years, he has been leading the development of Le Wagon’s training programmes and platforms. To date, his teams have trained 10,000+ alumni in Web Development and Data Science across 41 cities, making Le Wagon the world’s leading coding bootcamp worldwide.

 


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