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One of the most quoted statistics about data science is that ‘90% of all the data in the world was generated in the last couple of years.’ But it’s not the quantity of data that matters to the future of data science jobs, it’s the variety. As the variety of data increases, data science jobs appear in more industries – often with more focus and specialisation required.

Knowing that the bulk of the world’s data was generated in recent times is an exercise in hindsight – while data science is more interested in future events. With that in mind, let’s explore how the world as we know it will continue to change and increasingly rely on data.

What is data science

The first step in any data science or analytics process is to define the problem. It’s one of the things that separates data science from your common-or-garden business intelligence (BI) processes – where the business problem has already been identified. So, let’s start with a definition of data science.

In general terms, data science is about interpreting and extracting meaning from data using tools and methods from statistics and machine learning. While massive amounts of data are usually collected, cleaned and transformed using technology, to perform data science requires a human. Communication is one of the most important skills in data science jobs. Data scientists must be able to communicate effectively with team members, leadership and other stakeholders. Only through communication can the power of data science be realised across all areas of an organisation.

The need for data scientists

In recent years, the most in-demand jobs in Australia have included commercial analysts, data analysts and digital marketing analysts. Businesses of all sizes have clearly realised the potential competitive advantage that insights from data can provide. The most common use of these insights is forecasting for things like sales estimates, IT storage requirements and procurement predictions. 

In many cases, it appears that these predictions are being prepared manually. An analyst extracts data with a BI tool, pastes it into Excel, then generates a projection with Excel calculations or macros. These businesses need data scientists to introduce automated processes with artificial intelligence and machine learning. Not only will this create faster more accurate predictions, but it will free up the time of analysts to create more specific and varied forecasts.

Already there are a variety of industries that offer data science jobs that require specialist knowledge and experience. With data science’s dependence on the human qualities of communication, leadership and other soft skills, there’s increasing opportunities for data scientists to go macro.

Data science in healthcare

Most of the world is experiencing digital disruption, but in healthcare, the changes appear to be more rapid and far-reaching. As an example, the human genome project began in 1990 with the goal of determining the sequence of DNA in 15 years. With thanks to advances in computing technology, they had a rough draft within a decade. Today, wearable technology is able to produce two terabytes of data daily for each individual – including heart rates, sleep patterns, blood glucose, stress levels and brain activity.

This sort of healthcare data has recently been used by a US startup to develop a cancer treatment drug. With data science using artificial intelligence, they analysed 14 trillion data points in over 1000 patient samples. The drug in development detects cells that have been damaged by cancer and triggers their natural death – so there’s no need for extensive medication which is often harmful to the patient’s health.

Another rich source of data in healthcare is in clinical registries. In addition to moving patient records from paper files to digital records, practitioners and patients can also contribute their health data to national and international databases. The Movember Foundation supported Prostate Cancer Outcomes Registry in Australia and New Zealand is one such database. It not only provides a benchmark for doctors and nurses treating patients with prostate cancer, but it also identifies trends in patient health. On a local level, data scientists identified a region in Australia where it was more common for men to experience sexual problems after prostate cancer treatment. 

Data science in astronomy

The Australian Square Kilometre Array Pathfinder (ASKAP) has recently helped identify the origin of a fast radio burst (FRB) from space for the first time in history. The team of researchers behind this feat did so using data from ASKAP and several other telescopes around the world. After analysing the data, they were able to identify the FRB and then, using the different perspectives and formats of the various telescopes, identify its origin.

In the same week, the Australian Space Agency celebrated its first year of operation. It’s looking forward to supporting companies operating in the space economy. No doubt those companies will be relying on the skills of data scientists.

Astronomy is spoiled for data with the Sloan Digital Sky Survey delivering over 15 terabytes of data since it started collecting data in 2001. Meanwhile, the devices connected to the Pan-STARRS system drop over 10 terabytes every single night. If that’s not enough to whet your appetite, the Large Synoptic Survey Telescope in Chile will soon come online and deliver more than 20 terabytes of data every 24 hours.

Data science in sales and finance

In some ways, you could say sales gave data analysis its first big break. After years of toiling away in academia, data scientists became the darlings of marketing companies. In 1994 they began crunching numbers to predict how likely you are to buy a product. A quarter of a century later this simple proposition has been broken down into a kaleidoscope of macro observations and indicators. Still, there is plenty of work to be done to master these predictions, not least of which is refining the soft skills required to communicate insights and influence behaviours for success.

The future of the finance sector will rely heavily on modellers and data engineers for very specific forecasting. One startup in the US is using a machine learning platform to predict risk in lending. The system also identifies anomalies in payment transactions to detect fraud. Using the predictive work of modellers, bank staff can make better decisions in their loan approvals process to increase market share and profits.

Prepare for the future today

RMIT’s Master of Data Science Strategy and Leadership has been designed by industry experts to enhance your credibility amongst data scientists and with business leaders. In addition to future-proofing your data science practices, you’ll develop the soft skills to lead and influence in this age of digital disruption.

Get in touch with our Enrolment team on 1300 701 171.