The world is changing at a rate faster than ever before. Technological advancements in telecommunications, computing and mobile technology have made way for an unprecedented level of efficiency in the workplace. Accordingly, employers and business owners are now continually searching for better ways to increase efficiency and knowledge.

What is the main thing that helps them accomplish this? Data.

The ability to draw meaningful information from data is one of the most sought-after skills in the work force today. So much so, that Australia’s most prestigious research organisation, CSIRO, formed ‘Data61’ in 2016, which is now Australia’s leading data innovation group. Data61 works with companies to solve practical problems relating to data and computing as well as publishing research in this field. But it doesn’t end here.

Today, almost all workplaces require data mining and analysis skills. Accounting firms and banks are the common places we expect to find a need for these skills, however there are so many more. Schools, law firms, media companies, small businesses, Government, marketing firms, hospitals, construction companies, the armed forces and tech companies such as Facebook and Google all require the skill of data mining and analysis.

Why? Data allows these workplaces to assess: worker efficiency, the effectiveness of advertising, customer behaviour, the results of change, areas in need of growth, competition and much more useful information. Data gives these entities a path towards saving cost, maximising revenue and minimising wasted time and effort.

There are two examples that I think are worth mentioning. The first are social media platforms such as Facebook. The reason why we are shown advertisements related to our interests or the things we buy online is because Facebook mines and analyses our user data to advertise relevant pages and products to us. This is usually referred to as ‘the algorithm’. This ‘algorithm’ is fed data and the people creating this algorithm are constantly evolving the way it analyses and learns from this data. This requires the skill of people who understand how to categorise and give meaning to data to use it effectively.

The second example is a hospital. Hospitals are fast paced and sometimes stressful environments. This gives reason for strict rules and guidelines which streamline processes and minimise wasted time. But no system is perfect. Data analysis can be used to determine information like the optimum number of nurses in a surgery room or peak times during the day and during the year for particular emergencies. Once systems are modified, data analysis is needed again to monitor the effectiveness of these changes. This constant cycle of data mining, feedback and analysis allows for meaningful and useful change.

So, what does this have to do with maths?

Everything. To start a career in data science, studying maths is fundamental. The common avenues are completing a Bachelor of Science in Statistics or a Bachelor of Data Science or similar at a university. These courses require an understanding of calculus and basic proofs which are taught in the HSC Mathematics 2 Unit, 3 Unit and 4 Unit courses at varying levels of depth and difficulty. Studying data science after completing HSC General Mathematics is also a possibility. There are summer bridging courses and more fundamental maths courses available throughout the semester at universities for students preparing for a degree in statistics or data science. However, for General Mathematics or non-mathematics students, this path requires a lot more dedication and hard work during the early stages of study.

Is a career in data the only thing maths is good for? Of course not. As always, mathematics is required for professions such as engineering, maths/science teaching, software developing and accounting.

But the beauty about data science is that you can work in so many different workplaces for so many different purposes. You’re not necessarily attached to an accountancy firm or a school or a hospital.

With a world always looking for the most efficient use of time and resources, studying mathematics has never been as useful as it is right now.