In this era of Big Data, analytics and data science are burgeoning fields, demanding highly-skilled professionals who are also effective communicators.
These two separate but interconnected areas are where science meets creativity.
Specialists who can manage vast amounts of data and skilfully use data visualisation to communicate insights and make accurate predictions are highly sought after.
But what’s involved and what’s the difference? RMIT spoke to Dr James Baglin, program manager for the Master of Analytics, to find out more.
What is the difference between Data Science and Analytics at RMIT?
Analytics emphasises the statistical, mathematical and technological skills required to make informed decisions through the retrieval, preparation, analysis and modelling of complex real world data. It cuts across all industries and sectors, big and small.
Data Science incorporates statistical methods, but puts a greater emphasis on the specialised computational skills required to manage and analyse big data accumulating from sources such as social media, sensors, mobile and transaction data.
Big data presents many challenges to organisations and a data scientist's role is to develop the capability to derive insight and opportunity from the vast repositories of data that many organisations collect.
What are the soft and hard skills required?
Analytics and data science professionals work in multidisciplinary teams and often report directly to managers and stakeholders who do not possess the same technical background. Therefore, the most successful will possess excellent communication and interpersonal skills coupled with strong technological knowledge and experience.
Analytics and data science professionals must also be good at reframing questions in terms of data, methodological in all that they do, willing to follow the evidence wherever it leads them, critical of their solutions and highly creative problem-solvers.
Analytics and data science require exceptional statistical and mathematical knowledge. Both also require strong technological skills to collect, retrieve, prepare and analyse data.
In addition, data scientists need specialised computer science skills to collect and crunch data on scales that cannot be processed on regular computing systems.
What are the real-world applications for Data Science and Analytics?
Analytics and Data Science are highly versatile professions.
Analytics professionals assist organisations in deriving insight from their data – whether it’s for routine annual reporting, optimising delivery schedules, understanding and forecasting sale trends, predicting who is going to buy a product, or modelling complex transport systems.
Wherever there is data, analytics can be applied.
Data scientists are able to help organisations, in all sectors of the economy, handle very large volumes of data and make of sense of it.
They enable businesses to gain a competitive edge, governments to deliver more targeted services, and research teams make new discoveries.
What is the current and prospective job market like for future graduates?
There has been a real surge in job opportunities for Analytics and Data Science professionals in recent years and the trend doesn’t appear to be slowing down.
You only have to do a quick search of a job website using terms such “Analytics”, “Data Analyst”, “Statistics”, “Statistician” or “Data Science” to check for yourself.
Indeed, Australia is one of the top ten countries that employ data scientists according to the 2015 Stich Data report on The State of Data Science.
Are there any industry or professional learning opportunities offered with RMIT’s Master programs?
The Master of Analytics includes an applied research project where students can work in teams on industry projects, be placed in organisations, or complete internships related to analytics.
RMIT has a large network of partner organisations that provide industry-based project, placements and internships.
Analytics students have completed placements, projects and internships for organisations including City West Water, Sustainability Victoria, Medibank Private, Yarra Valley Water, iSelect, ANZ, and Austin Health to name a few.
This program aims to build on your experience; working on real industry problems and developing professional communication and interpersonal skills.
Most other courses in the program emphasise project-based work where students are given the opportunity to apply what they learning by working on a real problem.
The overall aim of the degree is to build a portfolio of analytical capabilities that demonstrate capabilities to employers.
The first year of the Master of Data Science develops a solid foundation in computer science and statistics – core skills necessary for every data scientist in their professional work.
The second year also includes a major project, which can be working on an industry or research project while based on campus, or off campus as an internship working as a data scientist in industry.
Many of our graduates have completed internships and gained employment at some of Australia’s most innovative and well known organisations.
RMIT is a supporter and sponsor of Data Science Melbourne, which in 2016 placed RMIT statistics and analytics interns in ANZ, iSelect, Zendesk and Silverpond.
Analytics or Data Science - what’s right for me?
At RMIT, it boils down to where you see yourself.
If you are business-focused and your dream job is to use data to drive business decision making and problem solving, choose the Master of Analytics.
If you love data, computers, programming and using computational methods to analyse data and find insight, the Master of Data Science is a better fit.
RMIT also offers a Master of Statistics and Operations Research and opportunities to move between these programs offer students incredible flexibility.
In addition, a broad range of industry partners, emphasis on real-world application and an external partnership with Data Science Melbourne provide students with extensive opportunities to kick-start their careers.
Applications now open for Midyear, start studying the Master of Analytics or the Master of Data Science in July.
Story: Rebecca McGillivray