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Five key trends all data scientists should know about

Monash University’s 100 per cent Online Data Science Single Units ​are designed to provide the foundation for professionals to capitalise on all of these key trends in data science.

Data science jobs will leap by 28 per cent by 2020 globally, ensuring data scientists are in significant demand. In Australia, the shortage of data scientists already being experienced means those with data skill sets are well compensated, with a salary range of $63,000 to $139,000. Furthermore, Australian executives who like to travel can take advantage of global skills shortages and find well-compensated work wherever they go.

These trends are unsurprising when you consider data scientists are at the forefront of so many emerging trends in business and technology. As businesses transform their technology infrastructure, and move to deliver products and services digitally (digital transformation), it’s the data scientists who facilitate the process by optimising and innovating experiences and delivery mechanisms.

Five emergent hot trends in which data scientists play a critical role include:

Regulation and data security: As organisations become more technology-aware and digitally-driven, they also need to grapple with an increasingly strict regulatory environment, and elevated risks when data security fails. Following the introduction of Europe’s GDPR in 2018, there are potentially business-ending penalties for non-compliance, and all governments are keen to crack down on the irresponsible use of data. As modern businesses are risk-adverse, discussions about risk are top of mind at an executive and board level, those with data science skills may find themselves at the C-Suite table as they’re asked to establish risk mitigation practices around the collection and use of data.

As Apple CEO, Tim Cook, said in a speech in 2015: “If you put a key under the mat for the cops, a burglar can find it, too. Criminals are using every technology tool at their disposal to hack into people’s accounts. If they know there’s a key hidden somewhere, they won’t stop until they find it.” Security risks are rife throughout every organisation. Protecting the organisation, as well as customers, can no longer be the sole province of security experts. There’s just too much to do. Data scientists will be called on to work with the security specialists to ensure that the proverbial key isn’t left out.

AI: For many years, artificial intelligence (AI) has been held as the holy grail of data practice. Feeding the masses of data gathered within an organisation into a properly structured, machine learning AI will allow organisations to deliver true automation which, in addition to delivering greater efficiency with customer interactions, also frees up employees’ time to instead dedicate to advanced tasks. As Amazon CEO, Jeff Bezos, explains, it is becoming core to the efficient business: “Much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations.”

For the data scientist, the challenge will be in mapping out the strategic use of AI, testing and delivering the applications, and then analysing the performance of the AI to ensure that it is delivering on its promise.

The application of AR and VR to business: Along with AI, both augmented reality (AR) and virtual reality (VR) have the potential to completely transform how consumers interact with brands, and deliver seamless experiences across both the physical and virtual. The data scientist’s role will be integral when it comes to ensuring any AR or VR experience is built on actionable, data-driven insights to meet the increasing demand consumers will have of digital interactions.

Apple’s Cook has placed big bets on the ubiquity of AR into the future: “I do think that a significant portion of the population of developed countries, and eventually all countries, will have AR experiences every day, almost like eating three meals a day. It will become that much a part of you.” It will be the data scientists that deliver and drive these AR experiences, and for that the stock that organisations place in data science as a competitive opportunity will only accelerate into the future.

Edge computing: Thanks to the emergence of the Internet of Things (IoT), organisations can do more work, analysis, and data processing at the “edge” – i.e. close to the source of origin of data. This provides an opportunity to the organisation (and data scientist) thanks to the immediacy of data – for example, as venture capitalist, Scott Weiss outlines, it will improve the stability and efficiency of the organisation’s technology. “With the IoT, we’re headed to a world where things aren’t liable to break catastrophically – or at least we’ll have a hell of a heads’ up,” Weiss said. “We’re headed to a world where our doors unlock when they sense us nearby.”

Data scientists will have a significant role to play here in ensuring that the network environment runs efficiently enough to leverage the breadth of opportunity that IoT offers.

Blockchain: Finally, data scientists will need to understand and implement blockchain solutions. As Nobel Prize nominee and executive director and cofounder of the Free Market Foundation, Leon Louw, said “Every informed person needs to know about Bitcoin because it might be one of the world’s most important developments.”

Organisations are falling over themselves to learn about it because the unbending security of blockchain makes it the ideal solution for data records where integrity is paramount, and the efficiency with which blockchain can handle large numbers of detailed transactions are appealing to the modern business.

Skilling up

Monash University’s 100 per cent Online Data Science Single Units are designed to provide the foundation for professionals to capitalise on all of these key trends in data science. Subjects are broken into four categories – Introductory, Programming, Databases, and Maths & Stats. Across those categories are units as far ranging as data wrangling, data processing for big data, and mathematical foundations for data science, through to programming foundations in Python, data exploration and visualisation, and applied data analysis. Through these, students will understand how to get ‘hands on’ with the data, and draw creative and meaningful insights from it.

Collectively, these courses provide attendees the foundational capabilities that all advanced applications, sit on. For further study, the Single Units also provide a pathway to advanced data science education, with Monash offering a 100% online Graduate Diploma of Data Science. Students that complete that degree will be well placed to undertake leading roles in the data science space within Australia.

For more information on the courses available through Monash University’s 100% Online Data Science Single Units, or to enrol, please click here.

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