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Postgraduate data science: Study options explained

James Davis

Careers Commentator
Data science allows us to measure and interpret the voluminous amounts of information corporations and governments have at their fingertips. With a postgraduate qualification, you can join this thriving profession.

Data science is needed across many industries. Whether it’s a cyber-security firm, social media platform, government agency, retailer or any number of things, they need data scientists to help manage their databases. Postgraduate study allows you to learn the field’s intricacies through units in data analytics at scale, machine learning, rudimentary software engineering, database principles and more. This article will walk through the modes and methods of study available, as well as what you need to get in.

Graduate certificates or diplomas

These six month to one year full time or one year to two year part time courses act as a capstone for existing expertise in a quantitative field, which we’ll elaborate on later. This makes them a versatile, comparatively time-efficient way of getting into the field. Students can expect to learn a great deal of fundamentals in database management, data visualisation, big data processing and more.

Some of these are only available part time, with international students being unable to apply; Monash’s course is an example of this. Generally this shouldn’t be a restriction. Regarding cognate disciplines that are eligible, if you’re coming from any one of the following, you should be eligible for these programs. Bear in mind this list isn’t exhaustive and you’re free to argue your case to the faculty in question at your chosen institution:

  • Computer science
  • Econometrics
  • Economics
  • Engineering
  • Mathematics
  • Other sciences
  • Physics

As long as your undergraduate program featured a strong mathematics, computer science or databases component, you should be fine. Note that you only need one of these things to be eligible, but having more will no doubt improve your chances of entry. Another way in is via work experience. If you’ve spent six months or more working with databases, you can reduce the time it takes to complete your program provided the faculty accepts this experience as relevant. So, if you’re someone wanting to fill in the gaps of your knowledge that has been working in the industry for a short time, graduate certificates or diplomas are an even more time efficient way of bolstering your professional confidence.

Master’s degrees

These offer a broad scope of foundational units that one can expect to find in a graduate certificate or diploma, with the added benefit of customisability. Indeed, these programs come with a huge variety of electives to choose from that can help you get prepared for a particular industry of interest. Just look at some of the electives available at the University of Queensland in their Master of Data Science:

  • Advanced software engineering
  • Computation in financial mathematics
  • Consumer & buyer behaviour
  • Design thinking
  • Elements of econometrics
  • Experiemental design
  • Finance
  • Financial calculus
  • Financial econometrics
  • FInancial mathematics
  • Fundamentals of marketing
  • High-performance computing
  • Introduction to epidemiology
  • Mathematical statistics
  • Numerical methods in computational science
  • Portfolio management
  • Probability models and stochastic processes
  • Statistical analysis of genetic data

So, if you’ve got the time, these are definitely the way to go when committing to a career in data science within a particular realm. Just look at some of this inter-disciplinary variety! If you’re looking to go into public health, why not take that introduction to epidemiology? Want to go into research? Experimental design is perfect. Want to work for a bank? Financial econometrics pared with financial mathematics is right up your alley. Budding entrepreneur? Market and consumer research is perfect. Use your imagination when building your Master of Data Science.

So how do you break in? Well funnily enough, entry requirements are practically identical to the previous programs. Provided you’ve come from a quantitative discipline, obtained at least a credit GPA during this study (5/7 or 65%) and provided a CV detailing any experience working with data, you should be good to go. If you’ve not yet obtained any experience in data science and this is your first foray, don’t worry. This is mostly considered a nicety by most institutions and is by no means a breaking point in your application. Simply give them your CV of what you’ve done so far, even if it’s just previous study, and offer a cover letter where appropriate detailing why you have your interest in data science. If you’re inexperienced and sincere, it’s far better than being disingenuous or pretending to be more experienced than you are.

Data science is a fast-growing profession with a bright future, so you’re well advised to consider this highly safe and useful field. Wherever you go with your qualification, know that there are organisations all over the world who would love to have you. Good luck in your application, studies and career!