What does a data science career look like in the era of AI?

Looking to launch your data science career? If thinking about a career analysing big data, you’re no doubt aware that artificial intelligence (AI) is becoming more and more ubiquitous.

Everything is getting smarter, as new AI and machine learning technology enables data-driven solutions for individuals, organisations, governments, security frameworks, energy and natural resource management.

If you’re someone who likes digging deep into data in search of valuable insights, you’ll welcome the good news of growing investment being made in AI innovation. Advances in software and hardware are ushering in an era of pervasive intelligence, where how we work with data at the algorithmic level is increasingly crucial.

Dr Wei Luo, Deputy Course Director for Deakin’s Master of Data Science, says it’s a prime time to study data science. ‘Job advertisements for data scientists have been steadily growing in recent years,’ he explains. ‘AI has taken the industry by storm and 97.2% of Fortune 1000 companies are investing in building or launching big data or AI initiatives.’

Through a Master of Data Science at Deakin, you’ll learn how to become a data scientist. You’ll learn the deep skills required to wrangle big data and help companies achieve new levels of efficiency and effectiveness through the automation of processes and expansion of existing markets.

The difference between data science and business analytics

The easiest way to define the difference between data science and business analytics is as follows: data scientists work on an algorithmic level, whereas business analysts apply existing knowledge. This is particularly important in the context of AI adoption by global businesses.

Both data science and business analytics careers involve data collection, modelling and insights. Data scientists, however, have a unique set of problem-solving skills and more specialised knowledge.

They might develop algorithms to find connections between different data sets, or write code for algorithms used in predictive analytics.

‘Becoming a data scientist involves building highly technical skills,’ Dr Luo says. ‘The demand for data scientists has never been greater, whereas the demand for business analysts is on the decline. It’s not enough now to simply offer a statistical study of business data – businesses want to know about data logistics, algorithm building and AI-powered technology.’

Studying data science at Deakin teaches you to combine traditional analytics practices with sound programming knowledge, giving you a competitive advantage in today’s business and IT worlds.

More importantly, it trains you in the underlying principles of various algorithms through real-world application, and ensures you’re sufficiently agile for whatever the data-driven future might hold.

The intersection of machine learning and data science

Machine learning (ML) is a branch of AI in which data-driven algorithms give software applications the ability to automatically learn and improve from experience without being specifically programmed to do so.

For example, platforms like Netflix use the viewing history of other users with similar tastes to yours to recommend what you might like to watch next.

Many organisations are still early in their journey to embrace machine learning and are turning to emerging data scientists to show them the way.

When you graduate from a Master of Data Science at Deakin, you’ll have a portfolio of work that demonstrates your ability to ensure data delivers competitive value.

You’ll know how to manipulate data into the correct format to use in machine learning and understand how to embed predictive insights into new and existing business processes.

You’ll also have a strong foundation for future upskilling. ‘It’s a fast-track course,’ explains Fayaz Beigh, a current student of Deakin’s Master of Data Science. ‘Data science in general is a huge knowledge base, and in this course you touch on each and every aspect of it. It has deep roots in statistics – it goes from the foundation statistics to the advanced level – and there’s a strong emphasis on machine learning.’

Getting your hands dirty with data

Organisations are increasingly amassing hordes of data, but it’s useless without people who can extract meaning out of it. Deakin’s Master of Data Science prepares you to work in all sectors. It goes deeper into the theoretical aspects than bachelor-level data science and helps you understand data from a business point of view.

From finance stock data to environment habitat monitoring data, students work with real data sets from major corporates including:

  • Telstra
  • AFL
  • The Alfred Hospital
  • Ford Motor Company.

By replicating everyday problem- and project-based work for data science professionals, you’ll get hands-on experience with both data strategy and the newest AI tools and algorithms.

‘We designed the course in four parts to ensure your skill progression is clear,’ Dr Luo says. ‘You can choose to do a thesis or you can choose to do a professional practice. We’ll also help you source an internship, which can be a short-term unpaid internship or a longer-term paid appointment.’

Depending on your situation, you can study at either Deakin’s Melbourne Burwood Campus or at Cloud Campus. All options include ongoing support and retain a focus on soft skills in terms of communication, visualisation and data presentation.

After all, data may be the lifeblood of AI, but the robots are not taking over completely – we still need data specialists to drive efficiency, continuous learning and success.

Open the door to a career that's becoming indispensable in today's AI-powered world. Explore Deakin's Master of Data Science today.