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Data Science Courses overview
Data science is the scientific method applied to the extraction and interpretation of data. It is a multidisciplinary field dedicated to the management of highly complex statistics and computation. Postgraduate studies are for students with bachelor level qualifications in statistics, mathematics, computer science, engineering and more. They feature a broad array of topics, ranging from subject-oriented units in data management, visualisation, and analytics to broadly applicable topics on decision making, statistical methods, and biostatistics.
The term ‘data science’ was interchangeable with ‘computer science’ for much of the 1960s. This was due to the lack of hardware and data collection tools available to modern-day people. It wasn’t until the late 90s when Jacob Zahavi foresaw the need for new technology and practices to handle enormous quantities of data that the field started garnering serious consideration. By the early 2000s, it rapidly began picking up interest, leading to the field we know of today.
Graduates in data science are entering a young and highly important field. They will be needed across all industries for decades to come.
Is data science for me?
Data science is for anyone fascinated by interpreting quantitative data. It takes highly numerate people who can spot patterns, interpret puzzling findings and manage a multitude of details. If you’re the sort of person who enjoys solving these sorts of issues, data science could be for you.
Study pathway
Postgraduate data science can be taken up to master level, with each program offering its own unique opportunities for specialised learning.
Graduate certificates in data science are six-month programs that provide graduates from relevant fields the opportunity to learn the fundamentals of data management, statistical methods, data mining, and other essentials. Courses like those offered by Charles Darwin University give students the chance to pick electives suited to their interests, such as general decision making, impact analysis or biostatistics.
Graduate diplomas are for students with prior knowledge in database programming. They provide units in foundational mathematics, coding in Python, modelling data and a broad selection of useful electives. Offerings from the likes of Monash University provide data exploration units, applied data analysis, data processing for big data and more. All these courses take roughly one and a half years to complete, up to a maximum of four years if part-time.
The Master of Data Science is for students with a desire to gain a more comprehensive knowledge of the topics covered in prior qualifications. Courses like those offered by the RMIT also provide students with industry connections, helping them grow their career during and after their degree. These tend to take two years of full-time study to complete, or four years part-time.
Employment options
Data architect
Data architects determine the requirements of any given database by evaluating client objectives and comparing them with current systems they may have in place. These professionals are responsible for ensuring a database has the integrity to sustain the type and amount of information clients require. Companies like Teradata specialise in data architecture, making them excellent employment options.
Business intelligence analyst
Graduates who choose this career path are expected to interpret data drawn from external sources and internal systems relevant to business. Through a combination of market research and statistical acumen they report on data so decision makers can act upon it. Companies like Tableau are dedicated to this line of work.
Data mining engineer
This field is for those with excellent pattern recognition, requiring these professionals to utilise machine learning, statistics and database system knowledge to the fullest extent. They are required to extract data from large swathes of otherwise-indeterminable information. Companies like Alteryx, Blia Solutions and Semcasting all use data mining in various important applications.
Specialisations
The wide range of industries requiring data science lead to a wide array of specialisations. The following are just some of these: