How to Become a Data Scientist in Australia
Postgraduate university qualifications, along with professional experience, establish your credentials and expertise as a data scientist.
The smoothest roadmap to working in data science and artificial intelligence.
Step 1. Earn a Relevant Bachelor Degree
The first step to become a data scientist in Australia is to do a 3-year bachelor degree that offers foundational training in data systems, statistics, as well as subjects like data analysis and machine learning. A Bachelor of Data Science is ideal. Other good majors are information technology, computer science, and mathematics and statistics.
Being a data scientist requires deep knowledge across multiple disciplines. A good foundation degree will provide a career platform.
Choose a program that has strong relevance to the field. You don't have to get this perfectly right, as any relevant learning could be valuable, a source of specialist expertise, and give you better access to the job market in the short term. Inevitably, you will need to keep learning and stay abreast of technology changes throughout your career.
Programming skill has been a major part of the field, but this is quickly diminishing in significance with the ability of AI to perform routine programming tasks. Therefore, you may want to steer clear of courses with a heavy coding curriculum, instead favouring courses that emphasise concepts and leverage AI capabilities.
Useful topics in your undergraduate degree include data management, statistical methods, machine learning fundamentals, and data visualisation techniques. Specialisations like cybersecurity, artificial intelligence, and cloud computing deepen your expertise. Cross-disciplinary options, such as bioinformatics, enterprise systems development, and blockchain technologies, broaden your skill set.
Step 2. Gain Developmental Work Experience
An important intermediary step in a data science career is working in related fields. Seek out jobs such as data analyst, machine learning engineer, data engineer, analytics manager, and AI engineer. Work experience will help you determine (a) if you want to be a data scientist, (b) what type of data scientist to become, and (c) key skills gaps to fill.
The need to gain real-world experience as a technology professional can sometimes be overlooked. Going straight from a bachelor degree into postgraduate studies could be considered a naive way to get into the profession. You don't know what you're getting into and can easily misdirect your study efforts.
Several kinds of jobs are good career preparation for a future data scientist.
- Working as a data analyst, for instance, exposes you to the nuts and bolts of data. You get hands-on with data cleansing and basic analysis.
- Machine learning engineers and data engineers dive deep into algorithms and data frameworks. This builds technical prowess.
- Roles like analytics manager and AI engineer showcase the strategic value of data science. They illuminate the power and ethical stakes of AI.
Such experiences refine your skills and widen your view. They reveal which data science facets spark your passion. You learn where you excel. This guides your further education and career path with precision.
Step 3. Do a Postgraduate Data Science Course
Postgraduate university qualifications, along with professional experience, firmly establish your credentials and expertise as a data scientist. Introductory or specialised learning are available through a Graduate Certificate or Graduate Diploma course. A Master of Data Science is the premier qualification for data scientists in Australia.
Postgraduate qualifications in data science offer a high return because Australian employers value them. For a complex discipline such as data science, it's a good idea to invest in a university program that covers unfamiliar learning areas.
You can study data science online while maintaining a full-time job, which lowers the opportunity cost of postgraduate courses. It's also common to start with a four-subject Graduate Certificate in Data Science, with the option to use completed subjects as credit towards a graduate diploma or master's degree.
University programs cover areas such as artificial intelligence, database management, analytics, data visualisation, and machine learning. There is usually significant flexibility in what you study, allowing you to target your areas of interest.
We recommend you identify the kinds of jobs you're interested in before finalising your program of study. You should check out job adverts to see the skill requirements.
UNSW Online - Graduate Certificate in Data Science
The online Graduate Certificate in Data Science at UNSW serves as a four-subject gateway to a master's degree. It covers programming principles, foundational data science, and statistical inference for data scientists. Electives include database systems and strategic decision making. Applicants need a relevant degree or 2+ years' experience working as a data analyst or scientist. Each subject is completed part-time in a 7-week study block.
Step 4. Build a Data Science Portfolio
If securing a data science job proves challenging, building a professional portfolio is key. This not only displays your capabilities to recruiters and network connections but also builds personal satisfaction. You shape its direction, enabling the achievement of the desired skill set for a data scientist. You can also target your preferred positions through project choice.
In building up your portfolio and CV, incorporate a mix of projects. Balance code-based tasks like data analysis or model training with content-driven efforts such as blog posts or video tutorials. Through diverse projects, you pinpoint and bridge skill gaps.
Examine Australian job advertisements to understand market demands. Identify the skills employers are looking for. Shape your portfolio to showcase these competencies.
Some ideas for portfolio development are to:
- Join Kaggle, IDAO, or Topcoder competitions to benchmark and showcase your skills.
- Collaborate on open-source GitHub projects, contributing to tools used in data science.
- Develop case studies solving real business problems, showing practical application.
- Share work at Meetups and through blogs or GitHub, building your network.
- Explore projects from r/DataIsBeautiful for inspiration and creativity.
This comprehensive approach, from project selection to showcasing your work, prepares you for a successful data scientist career.