What is Data Science and why is it important?

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WADSIH

Here at the Hub our focus is all about data science, but we often get asked “what exactly is data science?”. Data science involves the use of data in a multitude of ways to generate information and subsequently knowledge – to solve complex problems, discover insights and generate business value. Using data to solve problems is not new, it is as old as science itself! Scientists have always collected data and then used that data to solve problems. Data science is no different, it allows us to make informed decisions through analysing existing data to generate insights. But what is different is the amount of data that we now collect, in this digital age we are able to collect data on almost anything. This “big data” combined with new techniques, including advanced machine learning and artificial intelligence, can now help us predict future events – and this is really exciting!

Developed from the evolution of computer science, mathematics and statistics, data science takes historical data to try to identify and understand complex behaviours and trends which may predict future states. Have you ever used predictive text? Your phone and gmail can now complete sentences for you. This is all down to an AI tool known as natural language processing which uses vast amounts of language data and, through very clever algorithms, can predict with pretty good accuracy what you mean to type. Data science, including AI, can open the door to innovation through enhancing predictive accuracy. Ultimately, this facilitates improvements to how we do things – all sorts of things! The uses and applications of data science are endless, and that’s what makes it such an exciting field to be involved with.

The data science life cycle encapsulates the various steps involved, with initial identification of a business problem or potential opportunity from within an organisation (or outside of it!), then collection, collation and preparation of the data to get it into a form where information can be extracted from it. The data scientist then determines what the best analytical tool is to use on the data to solve the problem or validate an opportunity. The data scientist’s tools and techniques are many, ranging from rather straight forward statistical analysis, to developing more complex machine and deep learning models. The key to any developed models or analytics is that they need to be deployable and generate real value to the organisation. The results of any data science process must be evaluated in line with business objectives and communicated effectively, with any beneficial outcomes put into action.

So with data science enabling us to extract meaningful insights from complex and large data sets, the data science process incorporates significant advances in the areas of predictive analytics (extracting information from existing data sets in order to determine patterns and predict future outcomes and trends), prescriptive analytics (gathering data from a variety of both descriptive and predictive sources for modelling and applying this to the process of decision-making), machine learning (the development of computer programs that can access data and use it to learn for themselves), and artificial intelligence or AI (the development of computer systems able to perform tasks normally requiring human intelligence). The end result of any data science effort should be the production of knowledge which leads to insights and valuable business information.

Here in Western Australia, we have loads of great examples of where data science is being used by business and industry to facilitate innovative projects to achieve goals that only years ago would have seemed unimaginable. We have world leading resource companies using data science to run autonomous mines and oil and gas assets, we have incredible radio-astronomy experts leading the way in space exploration through the Murchison Field Array and Square Kilometre Array Telescope. One incredible example where AI learning is helping to save peoples eyesight, involves development of a computer program that is trained to recognise symptoms of a degenerative eye condition – diabetic retinopathy. You can read about other examples of how data science is being utilised for the benefit of human kind on our Data Science Projects & Case Studies page.

Working in the area of data science highlights the importance of STEM education, as it requires skills in science, technology, engineering and mathematics combined with strategic business management. This learning and education can be facilitated through several courses, study options and internships available in Western Australia. Keep an eye out for WADSIH’s exciting upskilling programs to be launched in 2020.

As there is such a wide variety of applications for data science, this means there are lots of potential career opportunities – and we’ll cover this in further detail in a future blog post.

Dr Liz Dallimore and Dr Phil Tucak

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