Data science is a growing field

The world is inundated with data. There’s a virtual tsunami of data moving around the globe, renewing itself daily. Take just the global financial markets. They generate vast amounts of data — share prices, commodity prices, indices, and option and futures prices, to name just a few.

But data is of no use if there aren’t people able to collect, collate, analyse and apply it to the benefit of society. All that data generated by global financial markets gets used for asset and wealth management — and it must be properly analysed and understood to inform good decision making. That’s where data science comes in.

Data science’s primary aim is to extract insight from data in various forms, both structured and unstructured. It’s a multidisciplinary field, involving everything from applied mathematics to statistics and artificial intelligence to machine learning. And it’s growing. This is because of advances in computer technology and processing speed, the relatively low cost to store data, and the massive availability of data from the Internet and other sources such as global financial markets.

For data science to happen, of course, you need data scientists. Because data science is so wide in scope, being a data scientist covers a range of professions. These include statisticians, operations researchers, engineers, computer scientists, actuaries, physicists and machine learners.

This variety isn’t necessarily a bad thing. From my own practical experience, I quickly learnt that when solving data-science problems, you need a range of people. Some can work in depth on theory and others can explore the application area.

But how should these data scientists be trained so they’re prepared for the big data challenges that lie ahead?

Data scientists typically use innovative mathematical techniques from their own subfields to try and solve problems in a particular application area. The application areas — finance, health, agriculture and astronomy are just some examples — are very different. This means that each poses different problems, and so data scientists need knowledge about the particular application area.

For example, consider astrophysics and the Square Kilometre Array being built on the southern tip of Africa. It will be the world’s largest radio telescope when completed in the mid-2020s.