How To Become A Data Scientist In Australia?

 

Do you possess good logical and critical thinking abilities and want to use them to solve real-world problems? If yes, then you can consider becoming a data scientist.

 

Data Scientists are proficient in information analysis. They excel at identifying patterns and trends in large raw data sets and use this knowledge to solve real-world issues.

Australian employers usually look for postgraduate students in data science. The ideal pathway to complete an undergraduate degree in a data science-related field is to continue with a Graduate Diploma or Master of Data Science course.

 

Continue reading to learn about data scientists and how to become one, including their responsibilities, jobs, courses, degrees, salary, qualifications, demand, and how to get into data science in Australia.

 

 

1. What Is Data Science?

 

Data is an invaluable resource for analysing a business, tracking customers' behaviour and forming better decisions that are crucial for the growth and development of the company.

 

Data science mainly focuses on amassing and analysing valuable data to play a vital role in the growth and development of a business. Data science links firms and their consumers.

 

Businesses use it to gather and transform large-scale raw consumer data into actionable insights to serve their consumers better.

 

 

2. Who Are Data Scientists?

 

Data Scientists possess the qualities of statisticians, programmers, psychologists and experimental scientists.

 

They are critical thinkers with proficiency in mathematics models and statistical programming. Data scientists also hold expertise in analysing complex data, recognising patterns and designing IT systems.

 

Data scientists use mathematical knowledge to develop and apply algorithms to identify patterns and trends. They do this to solve real-world issues, target potential customers and forecast business outcomes.

 

These professionals implement creative thinking to analyse and understand collected data. They use it to enhance search engines, increase sales in a retail business, foresee market trends and develop future technologies.

 

 

3. Top Reasons To Become A Data Scientist In Australia

 

Data science is among the most sought-after technologies to automate people's lives. When considering the digital and data science revolution, there is no doubt that data scientists will lead the future. 

 

Here are the top reasons why you should pursue this profession:

 

 

Massive Demand

 

Nearly 2.5 billion GB of raw data is produced daily via web searches, smartphones, social media, digital photographs and other sources.

 

There is an intense demand for qualified data scientists to translate the vast amount of raw data into actionable insights and meet the wide gap between demand and supply chain.

 

More businesses now use data science algorithms to boost their businesses. It results in more internships, job opportunities and high wages and benefits packages for data scientists.

 

 

Flexible Working Conditions

 

Data scientists work together with software firms and enjoy the benefits of flexible and remote work arrangements.

 

 

Diverse And Interesting Projects

 

Data science knowledge benefits several sectors, including businesses, non-profit organisations, healthcare providers, government, meteorology, the automobile industry, and retail.

 

Data scientists develop precise climate models that enhance weather prediction technology. They can effectively predict natural disasters such as tornados, floods, storms, and hurricanes and give timely warnings.

 

These professionals help save lives and improve global ecological balance by lessening the effects of climate change and mitigating natural disasters worldwide. They also contribute to the agricultural sector by analysing crop harvest production and methods to produce more food to help farmers.

 

In transportation, data scientists optimise software and hardware to build innovative, timely, and cost-efficient public transportation systems for governments and the public worldwide.

 

A data scientist can benefit retail businesses by identifying and targeting potential customers by revealing insights about their buying history, income band, browsing habits, interests, and more. This valuable information can help businesses expand and retain their customer base and increase sales and revenue.

 

Marketing is another field where data scientists help. Companies obtain customer data via social media channels providing insights about their visitors, such as the total time spent on different web pages, shared content, and more.

 

 

High Pay Package

 

Given the demand for data scientist professionals, the sector offers outstanding earnings and compensation to experienced and beginner-level data professionals. As per the PayScale data, a data scientist in Australia earns an average annual pay of $ 120,000.

 

The starting salary of a data scientist in Australia is between AU$52k to AU$94k per year. A data scientist with five years and above experience draws around AU$79k to AU$147k in a year. The hefty pay package is another compelling reason to make a career in data science.

 

 

Help Enterprises by Making Critical Decisions

 

Data scientists serve as a vital link between BIG data and decision-makers. They help companies solve existing and new issues by gathering and interpreting data.

 

 

Get Hired In The Top Tech Firms

 

If you want to get into a tech firm, then becoming a data scientist can be the way to fulfil your dream. Continued expansion of mobile devices and the networking industry, many tech firms now look to hire data scientists to meet their increasing demands.

 

 

Incredible Access Throughout The Business

 

Data scientists are involved in modelling business strategies and predictions to enhance productivity in the business. 

 

This requires them to gain access to key departments in a company. Many professionals even get to work with senior management early in their careers.

 

 

Automate Routine Works Using Artificial Intelligence

 

A data scientist implements AI to automate the monotonous aspects of a business,s thereby helping people to adjust and succeed and making the company more efficient.

 

 

4. What Does A Data Scientist Do?

 

Data Scientists use various tools to uncover patterns and reveal the facts in data sets to form the right business decisions.

 

They employ advanced concepts such as machine learning, linear algebra, statistics, computer science, software engineering and human behaviour to mine databases and parse their findings into visual presentations for department heads and business leaders.

 

 

Here is a comprehensible list of data scientist duties:

 

• Collect large-scale organised and unorganised data

• Formulate and apply algorithms and mathematical models

• Authenticate data to ensure its completeness, uniformity, and precision

• Analyse raw data from multiple angles to spot patterns, relationships and trends and derive critical insights to resolve complex issues 

• Use statistical analysis, Artificial Intelligence, data visualisation, machine learning, programming languages, and data-driven tools.

• Transform data into visualisations and data models to easily communicate findings to personnel and stakeholders

• Applying algorithms to explore, analyse and interpret patterns in the data. They use these techniques to find patterns, trends, and hidden insights within large amounts of raw data.

 

 

5. What Subjects Do You Need To Study To Become A Data Scientist?

 

To become a successful data scientist, candidates must have strong mathematical abilities, excellent computer programming skills, and a thorough understanding of statistics and probability.

 

Programming skills are desirable as they help analyse datasets and improve statistics skills. To work as a data scientist, you must be proficient in using statistical programming languages. These include SQL, R, Python, Java, C/C++, Matlab, and tools like Tableau, Paxata and RapidMiner.

 

Knowledge of machine learning is also essential as it provides techniques to extract relevant data and methods to gain important information from the gathered data. A data scientist must have a good understanding of machine algorithms that help to predict future trends and arrive at meaningful conclusions.

 

Aspiring data scientists should also have good knowledge in designing IT systems, evaluating complex data, and detecting patterns.

 

Statistics knowledge includes hypothesis testing, statistical inference, regression and experimental design. It is required to make detailed data predictions and analyses to arrive at better conclusions.

 

A good understanding of Linear Algebra, Calculus, Linear Regression, Mathematics and Quantitative Analysis is advantageous to pursue this career path.

 

 

6. What Skills You Need To Become A Data Scientist In Australia

 

A career as a data scientist requires a combination of technical expertise and soft skills. Along with data-driven and technical skills, a data scientist should also have good soft skills to interact effectively with colleagues, clients, and customers.

 

The following are the most desirable soft skills to become a successful data scientist:

 

• Excellent communication skills to present findings in an understandable way to non-technical personnel.

• Team player skills to work selflessly, accept feedback and share knowledge within the team. 

• You need an analytical and critical mindset to identify problems/opportunities from data and arrive at solutions.

• Incessantly curious, imaginative, and intuitive to approach issues from different angles

• An innate quest for answers through hidden trends, opportunities, and patterns

• Good observation and pattern recognition skills.

• Good intuition to understand human nature 

• A positive attitude to determine sense from massive raw data translates assumptions into actionable ones and inspires the team to act.

• Problem-solving skills to figure out logical flaws via objective analysis.

• Adaptability to numerous tools and methodologies to arrive at a workable solution

 

 

7. Qualifications To Become A Data Scientist In Australia

 

To get employed as a Data Scientist in Australia, you must be a postgraduate in Data Science.

 

For this, you must complete an undergraduate degree in Information technology, econometrics, mathematics, or any other data science-related field.

 

Follow it up with a Graduate Diploma in Data Science or a Master of Data Science.

 

 

8. Steps To Become A Data Scientist In Australia

 

Becoming a data scientist demands a commitment to gaining qualifications, developing skills, and learning on the job.

 

Follow the below steps in sequence to make a successful start in this occupation:

 

 

Step 1: Complete An Undergraduate Degree in Data Science

 

As per Job Outlook, around 45.4% of professionals employed as data scientists hold a graduate diploma or postgraduate qualification, and the remaining are bachelor's degree graduates.

 

This shows that university-level education is a must to enter the field. Those who desire to become data scientists should complete an undergraduate degree in data science-related fields such as computer science, IT, mathematics, or statistics.

 

 

Step 2: Build Core Skills In Data Science

 

Suppose your goal is to become a successful data science professional. In that case, you must focus on gaining an in-depth knowledge of the following hard skills:

 

• Computer programming languages (particularly Python and R)

• Linear Algebra

• Statistical analysis

• Machine learning

• Social network analysis

• Data mining, projecting, and interpretation

• Business strategy analysis

• Database administration and maintenance

 

 

Step 3: Complete A Post Graduate Program in Data Science

 

The next step is to study for a postgraduate degree in data science, like a Master of Data Science, or an advanced-level course like a Graduate Diploma of Data Science.

 

Online data science courses offer extensive training in the critical cloud, database, and AI concepts. They help to prepare aspiring data scientists for an entry-level jobs in data science.

 

 

Step 4: Build A Robust Portfolio

 

You need substantial practical exposure to succeed in the employment sector, along with formal education. As data science is a complex field, employers in Australia look for highly talented applicants who have worked on many projects.

 

Having an impressive portfolio of completed data science projects under your belt improves your employability for the job. Working on projects and building solutions also helps deepen your knowledge and understanding of the working of data science in the real world.

 

You can choose between applying for an internship program or building your projects to implement your knowledge into practice. You can find a few institutions in Australia that offer traineeship or apprenticeship programs for budding data scientists to provide them with practice exposure to the field.

 

 

Step 5: Do Networking

 

Once qualified and skilled, the next step is to expand your network and make more relevant connections to seek employment.

 

Internships, online programming communities, competitions, volunteering, etc., are a few ways to get in touch with industry professionals and showcase your talent. Communities like GitHub provide a platform for candidates to upload their university assignments, projects, etc., to demonstrate their talent.

 

Participating in data science competitions helps refine your skills, learn from other professionals, and get noticed by potential employers.

 

 

Step 6: Keep Updated on The Latest Trends and Developments

 

Data science is a continuously evolving and ever-changing field. To make a sustainable career, you must constantly adapt to new technological advancements.

 

Analytics tools are effective at structured data. However, modern-age data scientists are striving to develop innovative ways to examine and extract meaningful information from unstructured data.

 

Thus, data scientists can become more competent when seeking jobs and raise by keeping themselves updated on their knowledge and skills.

 

 

9. Steps To Become A Data Scientist Without A Degree In Australia

 

Although most employers look for degree holders for data science jobs, you can still become a Data Scientist without a degree and qualify for limited entry-level roles.

  

Follow these steps to become a data scientist without a data science qualification:

 

 

Step 1: Obtain Prerequisite Knowledge

 

To make a career in data science, you must grasp mathematics, statistics, and computer science. 

 

Learn mathematical concepts such as calculus, probability, discrete mathematics, and linear algebra. Get hands-on experience in working with programming languages such as Python and R.

 

 

Step 2: Learn Data Science

 

Learn the various components of data science, such as data extraction, cleaning, transformation, prediction, visualisation, and storytelling.

 

Understand the concept of Machine Learning and popular machine learning algorithms such as linear regression, k-means clustering, logistic regression, and more.

 

 

Step 3: Research Real-Time Case Studies

 

Once you gain a good knowledge of Data science, now is the time to use this knowledge in practical, real-life applications.

 

Research and read multiple case studies to understand how data scientists identify issues and implement methods to resolve them and improve sales and profit generation of the business.

 

 

Step 4: Find Opportunities To Work on Real-Time Projects

 

It can be tough to get an entry-level data science job when you are a fresher when considering the competition. What can help you set yourself apart from your competitors is a strong portfolio that talks about your knowledge and skills to the employer. 

 

Live Data Science projects are a great way to get hands-on exposure to solve real-world problems and hone your skills.

 

 

Step 5: Obtain Certification

 

Official certification from a reputable, nationally accredited organisation is an excellent step to validate your Data Science skills and increase your job chances as a Data Scientist.

 

The following certifications will prepare you for an entry-level role in data science:

 

• SAS Certified Data Scientist

• Cloudera Certified Associate

• Microsoft Certified Azure Data Scientist Associate

 

 

Step 6: Invest Your Time In Networking

 

List all projects and certifications in your portfolio and upload them on popular job portals and communities, including Linkedin, Tableau Public, Kaggle, Github, etc. These are the best places to reach several job recruiters in one place.

 

 

Step 7: Participate in Competitions

 

Online platforms like Kaggle allow candidates to participate in data science competitions. It helps you gain experience in solving real-world issues and catch the attention of prospective employers.

 

 

10. How To Become A Data Scientist For Free In Australia?

 

Suppose you want to enter the data science industry without spending on a degree. The possible courses could be free online or self-teach yourself through online videos and tutorials.

 

The easy accessibility to massive online data science learning materials and resources makes it now possible to become a data scientist without spending a penny.

 

 

Step 1: Identify Desired Technical Skills

 

To start learning from online resources, you must first understand what technical skills you need:

 

• Programming languages like SQL, Python, and R

• Linear Algebra and Calculus

• Probability, Descriptive statistics, Inferential statistics, A/B Testing

• Data Analytics Skills that include Data wrangling, visualisation, and technical writing

• Machine Learning

• And Data Science Tools

 

 

Step 2: Learn Python And Its Libraries

 

You can find websites, e-books (at Amazon), YouTube tutorials, support forums, and free online courses that offer ample resources to learn Python for free.

 

Udacity, Udemy, UpGrad and Coursera are a few top sites that offer free learning courses to teach the basics of programming languages and their practical applications.

 

Some of the helpful Python courses are:

 

• Introduction to Computer Science

• Introduction to Data Science in Python

• Applied Machine Learning in Python 

• Applied Plotting, Charting & Data Presentation in Python 

• Applied Text Mining in Python 

• Applied Social Network Analysis in Python

 

 

Step 3: Learn SQL

 

SQL is another essential requirement for data scientists. Here are the sub-components of SQL that you need to study to gain a thorough understanding of SQL:

 

• SQL for data science

• Data Wrangling, Analysis, and AB Testing with SQL

• Distributed Computing With Spark SQL

• SQL for Data Science Capstone Project

 

 

Step 4: Keep Learning

 

Advanced learning in Python and SQL will prepare you to apply for data science jobs. However, you need to master many skills (as mentioned at the start) to get complete data science knowledge.

 

Like Python and SQL, you will find plenty of learning resources and free online courses on machine learning, mathematics, R Programming, statistics, A/B Testing, and other fields in data science. 

 

So, keep learning. The more you know, the more skilled and employable you will become.

 

 

11. How To Become A Data Scientist With No Experience In Australia?

 

Are you seeking employment as a data scientist without previous experience? Check these helpful tips to realise your career goal.

 

 

Step 1: Improve Your Math Skills

 

Sound knowledge of mathematics is highly required to analyse data, write efficient code, and draw accurate conclusions.

 

You must master the following mathematical concepts to find correlations and trends between changing variables:

 

• Algebra and Linear algebra

• Statistical methods, modelling and fitting 

• Probability theory and distributions

• Multivariable calculus

• Data summaries 

• Regression analysis

• Descriptive statistics

• Hypothesis testing

• Markov chains

 

 

Step 2: Master Python, SQL, and R Programming Languages

 

Once you are good at math, the next step to becoming a data scientist out of college is to gain expertise in the programming languages, such as SQL, R, and Python.

 

Python is the most popular scripting language (with libraries) that allows professionals to wrangle, sort, and convert big and unorganised data.

 

R is the ideal language to visualise data and perform complicated mathematical and statistical calculations. As it is an open-source code programming language, you can find plenty of valuable learning materials and extensive community support online.

 

SQL is a database management tool that helps obtain desired data from numerous tables and databases.

 

 

Step 3: Apply For Internships or Work on Side Projects

 

It is hard to get a job just by gaining technical expertise. Employers also look for relevant work experience to assess your skills and determine your job suitability.

 

Thus, you need to augment your resume with practical professional experience. The internship is a great option to get work exposure and boost your resume. 

 

Alternatively, you can find freelance projects on platforms such as Freelancer and Upwork. Other possibilities are participating in competitions on Kaggle and joining a community like GitHub. It will improvise your knowledge and develop connections.

 

 

Step 4: Prepare for an Interview

 

Besides your data science experience, you can also list any past roles where you have used your business, communication, collaboration, and other skills.

 

List your past work samples on your website, LinkedIn, and online communities like GitHub to show yourself as the ideal candidate for a job role.

 

Ensure you go through frequently asked data science interview questions and solve plenty of coding problems before arriving for an interview. This advanced interview preparation will boost your confidence and improve your chances to get success on the first attempt.

 

 

12. Data Science Relevant Degree, Certifications And Diploma In Australia

 

The conventional way to seek a data science position is to obtain a bachelor's degree. However, students with a background in data analytics or computer science benefit from certification courses.

 

These courses are an excellent step to seeking a data science job as they equip students with the essential skills to transition to being a data scientist.

 

 

• Graduate Diploma of Data Science IoT - James Cook University (TAFE)

• Master of Data Science - University of New South Wales (Online) (TAFE)

• Master of Data Science Strategy and Leadership – RMIT Online (TAFE)

• Graduate Certificate of Data Science – James Cook University (TAFE)

• Master of Data Science (Applied) - The University of Adelaide (TAFE)

• Master of Data Science - James Cook University (TAFE)

 

 

13. How Long Does It Take To Become A Data Scientist In Australia?

 

It takes 4 to 6 years to become a qualified data scientist in Australia.

 

• University-Level Undergraduate Degree In Data Science: 3 to 4 years

 

• Master's Degree In Data Science: 1 to 2 years.

 

 

14. Employment Opportunities For A Data Scientist In Australia

 

Data science is a rapidly growing and constantly emerging industry. The sector offers only a handful of career paths but endless possibilities for aspiring and experienced data scientists. 

 

You can become a leader in your field and career by refining your skills in a niche segment. A career in data science gives you the flexibility to work within the country or abroad.

 

They can work in a company or as self-employed by setting up your innovative start-up. Presently, industries operating in the finance sector are presenting the most significant opportunities in data science. 

 

Other areas where there is heavy demand for these professionals are:

 

• Academia

• Medical science and research

• Government research bodies

• Technology start-ups and the IT industry

• Public administration and safety

• Retail sector

• Agriculture and environment

• Advertising and marketing

• Economics

• Real estate

• Urban planning

• Geology

 

 

Below are the top job roles, along with estimated annual earnings for Qualified data science professionals in Australia:

 

• Data Scientist: between $63K and $141K

• Data Analyst: between $50k and $103k

• Statistician: between $58k and $120k

• Data Engineer: between $61k and $135k

• Machine Learning Engineer: between $50k and $136k

• Analytics Manager – $92,249

• Research Scientist – $77,330

• Business Intelligence Analyst – $66,003

• Data Architect – $112,790

• Research Analyst – $52,970

 

 

15. Job Outlook of Data Scientists in Australia

 

Data Science is an emerging hot sector that enjoys a huge demand worldwide,e including in Australia.

 

The sector has spurred huge technological advancements (especially in AI, big data, and machine learning). It presently plays a critical role in the growth and development of business.

 

Presently, the sector offers umpteen employment opportunities for professionals with the desired skills and passion. 

 

As organisations increasingly realise the importance of the data they amass, there is a projection of extraordinary global demand for data scientist professionals in the coming years.

 

 

16. How Much Does A Data Scientist Earn In Australia?

 

The primary responsibility of a data scientist is to discover insights from enormous amounts of unstructured and structured data to meet specific requirements or objectives of a business. As the field continuously evolves, the significance and demand for data scientists are also increasing quickly.

 

The data scientist's salary in Australia per month is decided based on their academic qualification, work history, geographic location and management-related experience.

 

Based on the last three months' data, data scientist salary in Australia ranges between $110k and $140k per year. The average annual graduate data scientist salary in Australia is $ 120,000.

 

Melbourne and Sydney are the top cities in Australia that offer most of the work for Data Scientist roles. However, you can also find a few opportunities in other Australian capitals.

 

 

Here Are The City-Wise Earnings of a Data Scientist in Australia:

 

 

Sydney, NSW

 

• Starting data scientist salary NSW – between AU$52k and AU$94k

• 5yrs experience – between AU$79k and AU$147k

 

 

Melbourne, VIC

 

• Starting salary – between AU$51k and AU$94k

• 5yrs experience – between AU$77k and AU$143k

 

 

Canberra, ACT

 

• Starting salary – between AU$51k and AU$94k

• 5yrs experience – between AU$78k and AU$148k

 

 

Brisbane, QLD

 

• Starting salary – between AU$49k and AU$90k

• 5yrs experience – between AU$77k and AU$143k

 

 

Perth, WA

 

• Starting salary – between AU$51k and AU$94k

• 5yrs experience – between AU$81k and AU$155k

 

 

Hobart, TAS

 

• Starting salary – between AU$44k and AU$81k

• 5yrs experience – between AU$68k and AU$127k

 

 

Darwin, NT

 

• Starting salary – between AU$49k and AU$90k

• 5yrs experience – between AU$76k and AU$143k

 

 

17. Conclusion

 

Considering the Big Data explosion across businesses, there will be a vast and steady demand for data scientists in Australia.

 

By becoming a data scientist, you gain the necessary skills to amass data and transform it into an amazingly significant asset to lead the management and decision-makers in the company.

 

So, why wait? Get qualified in data science to access the limitless career possibilities in this fast-growing industry.

 

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