Build skills for a data-connected career, in both professional and management capacities, with a grad cert in data science online. The latest courses from Australia prepare you for vital roles and serve as pathways to masters degrees.
The ability to extract insights and knowledge from large and complex datasets is incredibly valuable today. By studying data science, you'll gain skills to inform business decisions, improve operations, and drive innovation. With a relatively short online course, you have the opportunity to establish the foundations for a high-value career exploiting the power of big data.
To quickly get started on data science projects, a Graduate Certificate in Applied Data Science may be ideal. The 4-subject course uses case studies to develop job-ready skills. Students learn by doing, completing tasks such as cleaning up messy datasets, python programming, and presenting data visually. You still have the option to continue on for a masters.
University of Adelaide
The University of Adelaide’s Graduate Certificate in Data Science (Applied) allows you to build practical skills quickly. Students work with real datasets to complete the kinds of tasks you may encounter as a data science professional. The online course can be completed over 8 months of part-time, flexible study. As a graduate, you will have gained job-ready competencies, with the ability to do things such as clean data and program proficiently in Python.
The Graduate Certificate in Data Science from RMIT Online is designed for people working in business roles where data has value. You'll learn the essentials of analytics, data wrangling, programming and data visualisation. Not only will you improve your fluency in the language of data, but you'll gain insight into how to better manage data responsibilities and solve business problems. The accelerated online course can be completed in 8 months of part-time study. Graduates have the option to continue studying for a masters focused on strategy and leadership.
An online Graduate Certificate in Data Science consists of four subjects from a master's program with 12+ subjects. The grad cert is generally a foundations or introductory course, covering core topics that are essential for mastery in the field. Graduates gain a solid platform for further study.
James Cook University
James Cook University's Graduate Certificate of Data Science is a convenient, affordable way to get started in data science. Each of the 4 subjects takes less than 2 months to complete and you have full control over when you study. An intuitive online platform allows you to easily connect with classmates and academic staff. Topics covered include an introduction to the data science discipline and fundamental concepts, statistical methods, visualising big data, and database systems.
Delivered 100% online, the Graduate Certificate in Data Science at UNSW is a 4-subject pathway program for a masters degree. Core topics are programming principles, data science foundations, and statistical inference for data scientists. You can also choose to study database systems or strategic decision making. The course is open to graduates with a relevant degree and/or 2+ years' experience as a data analyst or scientist. Each subject takes just 7 weeks and can be done while you work full-time.
As a foundations course, a grad cert in data science typically eases students into the discipline. Your instructors may outline data science methods, introduce programming, explore statistical and data visualisation techniques, and give students the opportunity to do applied projects.
Here are examples structures for the four-subject course.
Applied data science
Data taming, modelling, and visualisation
Foundations of computer sciences – Python A
Human and ethical factors in computer science
In this subject, students are introduced to core statistical concepts and contemporary techniques used in data analysis. It covers topics such as statistical data exploration, summary statistics, data visualisation, and probability as a measure of uncertainty. Building on these concepts, you'll learn about sampling, sampling distributions, and confidence intervals, which serve as the foundation for statistical inference and decision-making. The course concludes with a series of modules focused on common hypothesis-testing methods for various types of data. There is a strong emphasis on conceptual understanding, interpretation of statistical output, and the use of R for statistical computation in an analytical or data science setting.
Data Visualisation and Communication
Learn how to create effective data visualisations that communicate the insights from data and help stakeholders solve real-world problems. The adage "seeing is believing" highlights the significance of data visualisation. Whether you're analysing large datasets, communicating your data analysis in an understandable manner, or presenting the story behind your data to persuade your audience, visualisation is the most powerful tool at your disposal. As an interdisciplinary field, we continue to be heavily influenced by research in fields such as visual perception and psychology, statistics, computer science, art, and more. The subject starts with a focus on defining the problem to be solved between you and your stakeholder. Then you will design visualisations appropriate to the data and problem at hand. You'll also learn to use cutting-edge, open-source, cloud-based applications to create clear depictions of complex, real-world data.
Acquire the skills needed to clean and prepare various forms of real-world data for analysis. Real-world data is often incomplete, noisy, and inconsistent, and the course will cover core concepts in data pre-processing such as tidy data, and data integration, cleaning, transformation, standardisation, discretisation and reduction. You'll have the opportunity to develop and apply your data-wrangling skills to complex and inconsistent real-world data using popular open-source software.
Practical Data Science with Python
Gain a set of practical skills for dealing with data in various formats and sizes, including text, spatial, and time-series data. These skills encompass the entire data analysis process, including data acquisition, modelling, transformation, integration, querying, application of statistical learning and data mining techniques, and presentation of results. The course also focuses on data wrangling, which involves converting raw data into a more usable format for analysis. The course is hands-on and uses the Python programming language within the iPython interactive computing framework.
Foundations for Data Science
Statistical Methods for Data Science
Foundations of Data Science
Principles of Programming
Statistical Inference for Data Scientists
Electives (choose one)
Strategic Decision Making
The job outlook for those interested in data analysis and science is extremely positive. Data scientists and analysts is often considered to be the top emerging occupation worldwide (WEF). The growth trend is driven in large part by the technology sector, where companies like the world's five largest technology firms alone employ a significant number of data scientists and engineers. And employment is not limited to the technology industry as there is high in demand from other sectors.
The kinds of jobs available include these.
- Analytics manager
- Business data analyst
- Business intelligence analyst
- Business intelligence manager
- Business manager
- Data analyst
- Data engineer
- Data governance analyst
- Data governance manager
- Data manager
- Data scientist
- Financial data analyst
- Healthcare data analyst
- IT manager
- Machine learning engineer
- Marketing data analyst
- Operations manager
- Supply chain data analyst
The entry requirements vary by course. A bachelor degree with solid grades in a cognate (maths-heavy) discipline is generally a sufficient qualification. You may also be admitted based on relevant professional experience and/or evidence of academic ability in mathematics.
To be eligible for the data science graduate certificate, you need a bachelor’s degree or equivalent in any discipline with a minimum GPA of 4.5 (7.0 GPA scale).
If English is not your first language of instruction, you must demonstrate that you meet Minimum English Language Requirements.
To enroll in the program at RMIT Online, students must meet certain academic requirements, and international students must have a proficient level of English. Additionally, it is recommended that students have a basic understanding of statistics and prior knowledge of coding to be successful in the program.
The academic requirements include: holding an Australian Bachelor degree (AQF 7) or equivalent from a recognised institution, or having a minimum of five years full-time equivalent work experience in any industry setting in lieu of a formal qualification.
Key dates: Intakes are available in Jan, Mar, May, July, Aug, Oct
Tuition fees are $3,840 per subject in 2023. FEE-HELP is available to cover course costs.
The course is a pathway to the online Master of Data Science Strategy and Leadership.
The course is open to students who have a bachelor degree plus high-school intermediate-level mathematics (basic algebra and differential calculus) OR five years of relevant work experience (with high numeracy skills).
Applicants from a non-English speaking background must meet the English language proficiency requirements of Band 2 – Schedule II of JCU's Admissions Policy.
To be eligible, you must have a bachelor degree in Data Science or cognate discipline (e.g. Computer Science, Economics, Mathematics, Statistics) OR a bachelor degree in a non-cognate discipline and sufficient Data Science background as indicated by an average of 65 or above across three Level III courses in Mathematics and/or Statistics and/or Computer Science and/or Econometrics OR a degree in a non-cognate discipline and sufficient Data Science background as indicated by 2+ years of experience in a data science or data analytics role.
At the time of publishing, the indicative fee for this course in 2023 is $18,120 for domestic students and $24,720 for international students.
A data analytics specialisation may be more suitable if you want to do less coding and be more involved in the decision-influencing side of data work.
Data analysts spend limited time on data mining and other coding-intensive tasks, instead producing statistics for data-driven decision making. At the extremity are business analysts and business intelligence analysts. They pragmatically use well-structured data for tasks such as solving business problems and reporting market trends.
A grad cert in this field may be sufficient for you to start working as a data analyst in Australia... READ MORE
A Graduate Diploma in Data Science is a popular extension of the grad cert course that provides advanced training in key areas. Data science is a demanding profession in terms of the skillset required and a grad dip may offer you the right balance. You gain a strong skills foundation for a career as a data scientist while still leaving room for doing professional development in your own way after graduating.
UNSW Online's Graduate Diploma in Data Science is an 8-subject course consisting of 5 core subjects and your choice of 3 electives. Core topics are programming principles, data science foundations, statistical inference for data scientists, data mining and machine learning, and regression analysis for data scientists. The 3 electives can be chosen from a suite of 9 contained in the data science masters program at UNSW Online. Using UNSW Online's fast-tracked model, you can graduate with your customised graduate diploma in as little as 16 months of part-time study.
If you have an interest in this field, it's hard to see significant downsides from studying the data science course. It should be a valuable addition to your education and career. Graduate certificates are often worth it because of their compactness and high potential benefits.
You'll gain a postgraduate university qualification in a booming employment area. Although much shorter than a master's degree, graduate certificates are similar to degrees in prestige and impact.
The course provides an opportunity to build a foundational understanding of useful concepts and techniques, and to develop the skills needed to analyse and interpret data. These skills are in high demand across industries, making you more attractive to potential employers.
Additionally, the program of study is a good way to gain a taste of the field before committing to the longer graduate diploma qualification or a full master's degree. It's also a means for someone with a degree in a different discipline to gain knowledge and transition to a new career path.
This type of course can be suitable for "beginners". But it depends on the specific program and your background, strengths and weaknesses. Graduate certificates can be either foundational or advanced.
Postgraduate courses in data science are often designed for those with a background in a related field such as statistics, computer science, or mathematics, and may assume some level of prior knowledge. However, other courses are more suitable for beginners, with less stringent prerequisites.
Some grad cert programs have a more theoretical focus, while others are more hands-on and provide opportunities to gain practical experience working with data. Some are more focused on the business side of the field, while others are more technical.
If you're a complete beginner and want to start learning, it's recommended to start with a beginner-level course that covers the basics of data science and programming. Once you have a solid understanding of the fundamentals, you can then consider a graduate certificate to gain a deeper understanding of the subject and learn more advanced techniques.
For all of the courses listed, you'll be part of a virtual class. You'll have interactions with fellow program participants and instructors online, working to a collective weekly schedule.
Interaction with the learning community is crucial for a successful online study experience. Connecting with classmates, instructors, and university support staff will not only enhance your understanding of the material, but also help you develop skills that go beyond the course content.
By design, you will, however, be able to study flexibly at your own pace and on your own schedule. Students can access course materials and complete assignments at any time, rather than having to be online at a specific time for live virtual classes. You'll also be able to access lectures, readings, and other course materials at any time, rather than having to wait for a live class to view them.
The courses are also accelerated, meaning you study across the year in 6-8 week study blocks. Part-time online students complete a subject from start to finish every couple of months. Assessment relies of quizzes and assignments and generally there are no final exams.