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How to Apply for a Masters in Data Science in Australia?


Since the rise of "big data," the need for trained data scientists has gone through the roof. As businesses in all fields try to make choices based on data, it has become more important than ever to find people with advanced analytical skills and the ability to find useful information in huge amounts of data.

Australia has become a popular place for people who want to get a Master's in Data Science because of its high-quality education system and diverse culture. In this blog post, we'll talk about why getting a Master's in Data Science in Australia can be a good idea. We'll talk about the benefits, the best universities that offer this programme, the requirements for getting in, and the possible job possibilities.

What is a Masters in Data Science Course About?

A Master's in Data Science is a specialised programme that teaches students how to find useful information in big datasets, make decisions based on the data, and create predictive models. The course is mostly about statistical analysis, machine learning, visualising data, and thinking about ethics in data science.

Who Needs to Study?

The Master's in Data Science course is perfect for people with a background in computer science, mathematics, statistics, or engineering who love working with data and want to work in data analytics, machine learning, or data-driven decision-making. The course is good for both recent graduates and professionals who want to improve their skills or switch to a job in data science.

(Read More: What is the Scope of Data Analytics in Australia?)

Why Learn?

There are many perks of getting a Master's in Data Science in Australia. The course gives students a deep understanding of the latest techniques and tools used in data science. This helps them meet the needs of the business. It gives students real-world experience through projects and partnerships with partners in the business world. Also, studying in Australia gives you access to faculty and research facilities that are among the best in the world, as well as a multicultural atmosphere that helps you grow as a person and in your career.

Quick Facts about the Masters in Data Science Course

  • Usually, it lasts between 1.5 and 2 years.
  • Statistics, machine learning, data visualisation, and data ethics are the core subjects.
  • You must have a Bachelor's degree in a related area and be able to speak English.
  • Skills learned: data analysis, programming, machine learning, critical thought, and communication.

What are the types of Masters in Data Science?

Full-time: The full-time Master's in Data Science programme is for people who are able to study full-time. It has a full curriculum and gives students the chance to finish the programme in less time.

Part-Time: There are choices for people who want to study while also working or taking care of other things. Most part-time classes have more open schedules, so students can take longer to finish the programme.

Diploma: Some universities may give a diploma or graduate certificate in Data Science. This is a shorter, more focused programme for people who want to learn specific skills in data science without getting a full master's degree.

Know the Course Admission Process

Requirements for admission can vary, but a recognised bachelor's degree or an equivalent qualification in a relevant area is usually needed. A good grade point average (GPA) and being able to speak and write in English are also needed.

Entrance Tests: Some universities may require students to take standard entrance tests like the Graduate Record Examination (GRE) or the Graduate Management Admission Test (GMAT). Not all colleges, though, have this rule.

(Read More: How to Prepare for GMAT Exam to Study Abroad?)

Tips for Entrance Exams: Make sure you are well-prepared for entrance exams by reviewing the relevant subjects, practising sample questions, and getting used to the style of the exam. Think about taking classes to prepare or getting advice from experts.

Course Outline for the Masters Degree in Data Science

The curriculum for a Master's in Data Science may be different at different schools, but it usually covers the following basic topics:

tatistical modelling, machine learning, data visualisation, big data analytics, data mining, natural language processing, and deep learning.

  • Thinking about ethics in data science

Important Books for Masters in Data Science Course

Here are some books that can help you learn more in a Master's in Data Science programme:

1. Trevor Hastie, Robert Tibshirani, and Jerome Friedman's "The Elements of Statistical Learning": This book gives a complete introduction to statistical learning methods, such as linear regression, decision trees, and support vector machines. It talks about both theory and how to use it in real life, which makes it an important tool for learning the basics of statistical modelling.

2. "Pattern Recognition and Machine Learning" by Christopher Bishop: This book goes into detail about how methods for recognising patterns and learning from them work. It talks about Bayesian methods, neural networks, and kernel machines, among other things. It gives clear explanations and examples that can be used in real life to help readers understand difficult ideas.

3. Foster Provost and Tom Fawcett's "Data Science for Business": This book is about the useful side of data science and how it can be used in business. It looks at how to get useful information from data, including how to collect data, clean it up, model it, and evaluate it. It also stresses the importance of ethics in data science.

4. Wes McKinney's "Python for Data Analysis": In the area of data science, Python is a popular programming language. This book shows you how to use Python's tools for manipulating and analysing data, like NumPy and pandas. It shows you how to clean, wrangle, and display data with real-world examples and methods.

5. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, "Deep Learning": Deep learning is a subset of machine learning that focuses on training neural networks with many levels. This book gives a broad look at deep learning methods. It talks about convolutional networks, recurrent networks, and generative models, among other things.

6. Kieran Healy's "Data Visualisation: A Practical Introduction": To share insights and results, it's important to visualise data well. This book looks at the ideas and best practises of both static and interactive data visualisations. It also tells you how to choose the right kind of visual image for different kinds of data.

Top Schools in Australia for a Masters in Data Science

Australia has a number of universities with great Master's programmes in Data Science. Here are some of the best colleges to get a good education in Australia in data science:

  • The University of Melbourne: The Master of Data Science programme at the University of Melbourne combines coursework, industry projects, and a capstone project to give students a complete understanding of data science concepts and how they can be used.
  • The University of Sydney: The Master of Data Science programme at the University of Sydney works on developing skills in data analysis, data mining, and machine learning. Students can work with some of the best experts in their field.
  • The University of New South Wales (UNSW Sydney): The Master of Data Science programme at UNSW Sydney uses courses from computer science, maths, and statistics to take a multidisciplinary approach. Students learn advanced skills in data mining, prediction analytics, and optimisation.
  • Monash University: The Master of Data Science programme at Monash University works on practical applications and gives students skills in statistical analysis, data mining, and machine learning. There are also chances to get work experience through the programme.

How much does it cost to study a Masters in Data Science?

Australia's education costs for a Master's in Data Science vary by university and programme. On average, tuition fees for foreign students cost between AUD 30,000 and AUD 45,000. It's important to remember that fees can change, and you should also think about things like living costs, texts, and other materials.

What are the Scholarships for studying Masters in Data Science in Australia?

There are a number of scholarships for foreign students who want to get a Master's in Data Science in Australia. Some scholarships that are especially for data science or areas related to it are:

  • Australia Awards: The Australian government pays for these scholarships, which are given to the best students from involved countries. They pay for things like school and living costs.
  • Endeavour Postgraduate Scholarship Awards: International students who want to get a postgraduate degree in Australia can apply for these grants. They help pay for things like education, travel, an allowance for setting up a home, and health insurance.
  • Scholarships and grants offered by universities: Many universities in Australia offer scholarships and grants to foreign students. These can be different in terms of who can get them, what perks they offer, and how to apply for them. It is best to look at the websites of each university to see what choices are available.

Average Salary for a Masters in Data Science Graduate in Australia

The average pay for a data scientist in Australia varies based on experience, job role, and location, among other things. In Australia, data scientists make between AUD 80,000 and AUD 120,000 per year on average. But professionals with a lot of knowledge and skill can make much more money, especially in fields like finance, technology, and consulting.

Jobs after Studying Masters in Data Science in Australia

After getting a Master's in Data Science, graduates can choose from a variety of job paths. Some possible paths include:

Data Scientist: Data scientists work with big amounts of data, look at complicated problems, and build models to find useful information. They are a key part of letting organisations make decisions based on data.

Data analyst: A data analyst's job is to collect, organise, and make sense of data to find patterns and trends. The Data analysts pursue Data Analytics as the core subject and use statistics methods and tools for visualising data to get their point across.

Engineers who work on machine learning create and use algorithms and models that let machines learn from data and make guesses based on that data. They build and improve tools that help computers learn on their own.

Business Intelligence Analyst: Business intelligence analysts use data visualisation tools to make reports and screens that help them make strategic decisions. They look at data to find business trends and give tips that can be used.

Lucrative Jobs for graduates with a Masters in Data Science

Graduates with a Master's in Data Science can look for work in a variety of fields, such as:

Banking and Finance: The banking and finance business needs data scientists to look at customer behaviour, spot fraud, and make risk models.

Healthcare: Data scientists are very important when it comes to analysing medical data, doing research, and making predictive models to improve care for patients and health results.

Technology: Technology companies need data scientists to make programmes, improve recommendation systems, and study user behaviour so they can make better products.

Consulting: Data scientists can work in consulting firms, where they give expert help on data-related strategies, find growth opportunities, and solve difficult business problems.

Masters in Data Science vs. Other Courses in the Same Field

Even though a Master's in Data Science is a popular choice, students may also want to look into other related classes. Here is a comparison of a few lessons that are similar:

  • Master's in Computer Science: A Master's in Computer Science covers a wider range of areas, such as algorithms, software development, computer systems, and data science. It gives a strong grounding in the basic ideas and uses of computer science.
  • Master's in Business Analytics: This course mixes business and data science to help students make better decisions. It focuses on using methods for data analysis to solve business problems and make processes run better.
  • Master's in Artificial Intelligence: A Master's in Artificial Intelligence works on the development and use of AI technologies, such as machine learning, natural language processing, and robotics. It goes into more detail about the idea and use of AI.
  • When deciding which path to take, it's important to think about your interests, your job goals, and the focus of each course.

Masters in Data Science: Frequently Asked Questions

Q: Is a Master's in Data Science good for people who don't have a background in technology?

A: A technical background is best, but some universities offer bridge classes or preparatory programmes to help people who don't have a technical background.

Q: Can I work and get a Master's in Data Science on the side?

A: Many universities do have part-time choices for people who are already working. These programmes let you set your own schedule and can be done over a longer period of time.

Q. What are some popular programming languages used in data science?

A: Python and R are two computer languages that are often used in data science. They have a lot of apps and tools for working with data, analysing it, and teaching computers to learn on their own.

Q. What kinds of jobs might I be able to get with a Master's in Data Science?

A: Across all businesses, the need for people with data science skills is growing quickly. Graduates can find jobs in areas like banking, healthcare, technology, consulting, and the government.

Q: Can I get a Ph.D. in Data Science if I already have a Master's?

A: After getting a Master's in Data Science, you can go on to get a Ph.D. to specialise in a certain area of data science and help with study and new ideas.


Getting a Master's in Data Science in Australia will set you up for a successful job in data science. Australia is a great place to learn about data analytics and machine learning because it has a high-quality education system, top-ranked universities, and a multicultural atmosphere. You can make an informed choice about getting a Master's in Data Science in Australia if you understand the course structure, admissions process, job prospects, and other information in this blog post.

Frequently Asked Questions

The common notion is that foreign universities are expensive, although with scholarships this is an exaggerated issue. What many fail to consider are time and effort. Studying in an Indian college leads to a lower average salary than earned by our western counterparts. This salary sets the tone for all future promotions since companies often look at your previous salaries during compensation appraisals. Additionally, most senior positions tend to be scooped up by individuals who have an international outlook through global exposure.  Finally, studying in a foreign country is an incredible learning experience helping students get a better academic as well as soft skill development.

Canada has been one of the market leaders in education for decades. However, what currently sets Maple Country apart is immigration. Canada is wholeheartedly inviting students to study in their universities to fill job vacancies that are always growing in the country. Students who study in Canada (especially a Master’s Degree) are practically assured of a Permanent Residency Visa.

A good score doesn’t automatically qualify a candidate for a scholarship, however, it is one of the primary factors that the admissions department looks for in a scholarship application. There are other attributes such as a candidate’s academics, extra-curricular, financial background, and application essays (SOPs).

We get this question a lot. Many candidates know which career path they want to take, but are at a loss for how to take that journey. Understanding the job role and the industry that you want to get into is the first step to picking the right university.

Carefully consider the immigration options of the country that the university that you are considering is in. This is more important when you intend to stay back in the country after the completion of your course.

Also, review the placement history of the university as well.

The first step is to list your preferences. By listing your preferences and strengths, describe your course of preference. Extensive research on the modules, software and its length for your preferred subject is necessary.


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