What does data science mean in the built environment sector?
Quy Vu, a data scientist at Mott MacDonald, explains exactly what a career in data science and the built environment looks like.
The demand for data scientists has skyrocketed in the past decade and the potential for data science to make a powerful and meaningful difference to the world isn’t lost on Mott MacDonald and the built environment sector.
Quy Vu, a data scientist at Mott MacDonald, offers some exclusive insights into how working with data can lead to real-world changes and the types of skills that graduates need to build – in and out of university – to get ahead.
Data science and the built environment
Mott MacDonald defines data science as using statistical, programming and machine learning knowledge to follow a scientific and data-driven approach to solving a particular problem. In the built environment, your projects will be linked to infrastructure. For example, you could be considering how to get a train to run more efficiently or predicting pipe failures or flooding. ‘The water sector is quite a big focus for us at the moment,’ says Quy.
Sound like small work? Not exactly. Data scientists work across any and all of Mott MacDonald’s sectors, including education, environment, health and international development. They are creating solutions to some of the world’s biggest and most pressing challenges. ‘The projects we work on are all very high impact; they’re going to affect millions of lives,’ says Quy. This is just one of his favourite things about his job.
‘I wouldn’t have the opportunity to solve the problems I’m working on if I didn’t work at Mott MacDonald. These problems don’t have answers you can find on the internet!’ he adds. ‘That’s why data science is so crucial at Mott MacDonald. There’s no shortage of new and unconventional problems that are not well researched, alongside problems that have been studied for centuries. Traditional approaches to solving them take a lot of time and are heavily constrained by assumptions. A data science approach is helping to create quicker, more adaptable, more accurate and more intelligent solutions that have previously been out of reach.’
The demand for data scientists has skyrocketed in the past decade and the potential for data science to make a powerful and meaningful difference to the world isn’t lost on Mott MacDonald and the built environment sector.
Quy Vu, a data scientist at Mott MacDonald, offers some exclusive insights into how working with data can lead to real-world changes and the types of skills that graduates need to build – in and out of university – to get ahead.
Data science and the built environment
Mott MacDonald defines data science as using statistical, programming and machine learning knowledge to follow a scientific and data-driven approach to solving a particular problem. In the built environment, your projects will be linked to infrastructure. For example, you could be considering how to get a train to run more efficiently or predicting pipe failures or flooding. ‘The water sector is quite a big focus for us at the moment,’ says Quy.
Sound like small work? Not exactly. Data scientists work across any and all of Mott MacDonald’s sectors, including education, environment, health and international development. They are creating solutions to some of the world’s biggest and most pressing challenges. ‘The projects we work on are all very high impact; they’re going to affect millions of lives,’ says Quy. This is just one of his favourite things about his job.
‘I wouldn’t have the opportunity to solve the problems I’m working on if I didn’t work at Mott MacDonald. These problems don’t have answers you can find on the internet!’ he adds. ‘That’s why data science is so crucial at Mott MacDonald. There’s no shortage of new and unconventional problems that are not well researched, alongside problems that have been studied for centuries. Traditional approaches to solving them take a lot of time and are heavily constrained by assumptions. A data science approach is helping to create quicker, more adaptable, more accurate and more intelligent solutions that have previously been out of reach.’
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