The Top 5 Career Paths in Data Analytics in Australia
By Lumify Learn Team | October 8, 2024
The data analytics market in Australia is rapidly growing today and significantly contributing to the country’s economy. In fact, according to a study by Research and Markets, the market is predicted to reach a value of USD $3.798 billion (approximately AUD $5.5 billion) by 2028. This comes as organisations find data analytics as a critical tool to make important decisions.
Such growth is also driving a high demand for skilled data analysts and professionals, making now the best time to start a career in data analytics. In this blog, we will discuss the various career paths available in this field. We will talk about as well how Lumify Learn can help you gain the skills and knowledge necessary to thrive in the dynamic data analytics industry.
What is Data Analytics, and What is it Used For?
Let’s say you’re playing a role-playing video game, and you have collected a bunch of different items in your inventory. Each item represents a piece of data. On the surface, these may just seem like a bunch of random stuff. But what if there was actually a way to use these items to level up your character and defeat the boss?
This is the essence of data analytics. Data analytics is the process of collecting, organising, examining, and interpreting raw data to make informed decisions. By analysing and properly utilising the items (your data) in your game, you can gain a competitive edge, make strategic decisions, and ultimately achieve your objectives.
Businesses often use data analytics for the following:
Enhancing customer experience: Understanding customer behaviour and preferences helps organisations better tailor their services and products to meet client needs.
Optimising operations: Data analytics can help businesses identify inefficiencies and areas for improvement in their operations.
Personalised marketing: By analysing customer data, businesses can easily create targeted marketing campaigns.
Trend predictions: Predictive analytics can help forecast future market trends and consumer behaviour.
Risk management: This involves identifying potential risks (e.g., legal liabilities, financial uncertainty, and accidents) and developing ways to mitigate them.
What are the Career Paths in Data Analytics?
The data analytics field offers a wide range of career paths, each with its unique challenges and rewards. Let’s take a look at some of the most popular roles today:
1. Data Analyst
Behind every data analytics process are data analysts, who are responsible for gathering, cleaning, organising, and analysing a business’s information. Data analysts then present their findings to relevant company stakeholders through charts, graphs, written reports, or PowerPoint presentations to support business decision making.
Data analysts must possess the following skills:
Statistical analysis: This involves the knowledge of statistical methods to effectively interpret and analyse data.
Programming skills: Analysts should be familiar with languages like SQL, Python, and R for data manipulation and analysis.
Data visualisation: Data analysts should know how to create clear and insightful visualisations using tools like Microsoft Power BI or Tableau.
Business acumen: Analysts must know how to understand business problems and turn them into data analysis tasks.
According to the latest data from SEEK, employment opportunities for data analytics in the country is set to surge by 23.2% over the next five years. This makes the field one of the most promising and fastest growing career paths in the Australian job market today. While data analysts in Australia can earn around $90,000 annually, this can increase to $110,000 as their experience grows.
2. Data Scientist
While data analysts focus more on historical analysis based on past data, data scientists leverage robust technical skills to predict what will happen in the future. They design new data collection systems to solve business challenges using the following techniques, skills, and tools:
Predictive modelling: They use machine learning algorithms and statistical models to forecast future trends.
Data engineering: Data scientists build and maintain systems to collect and store data.
Advanced analytics: To extract deeper data insights, data scientists use complex analytical techniques like deep learning and natural language processing.
Programming skills: These professionals must know programming languages like Python, R, and SQL, as well as big data tools like Spark and Hadoop.
Communication skills: Data scientists should also be able to complex technical concepts to non-technical stakeholders.
Job opportunities for aspiring data scientists are expected to increase by 11% over the next five years. Those in the field can earn an average of $125,000 annually, making this one of the most financially rewarding career paths in data analytics.
3. Data Engineer
Data engineers build systems and develop algorithms that collect, manage, and turn raw data into information that data scientists and business analysts can easily interpret. Ultimately, they ensure that data is accessible so organisations can use it to evaluate and improve their performance.
To become a data engineer, you must have the following skills:
Data analysis: Having strong analysis skills can help data engineers build systems that gather information for use by analysts or scientists.
Data warehousing: Proficiency in designing, building, and maintaining data warehouses that centralise and organise large volumes of data is crucial. This involves storing large volumes of data efficiently, ensuring data quality, and making data easily accessible.
Database systems: Data engineers must have a deep understanding of database management, or the process of organising, manipulating, and controlling data in a database.
Coding: This skill allows data engineers to develop efficient solutions for processing large data amounts. It also helps ensure the quality and reliability of data.
Communication skills: Data engineers may sometimes need to share their findings and suggestions with colleagues without technical backgrounds. This makes strong communication skills a must.
According to Talent.com, data engineers earn an average of almost $130,000 per year, while experienced employees make up to more than $170,000 annually.
4. Business Intelligence (BI) Analyst
BI analysts transform data into actionable insights that enhance strategic decision-making. They use data visualisation tools to generate reports and insights that help organisations understand their performance and discover points for improvement.
It may seem at first glance that data analysts and BI analysts have the same roles. However, analysts take on a more proactive role in identifying potential issues and opportunities within the business. They use data to predict future trends and recommend actions to help the company stay ahead of the competition.
The skills required to become a BI analyst include:
Data visualisation: This involves proficiency in tools like Power BI and Tableau to generate clear and insightful data visualisations.
SQL: BI Analysts must have strong SQL skills to query databases and extract important data.
Business acumen: They must also understand business operations and know how to make recommendations from data findings.
Communication skills: Like data scientists and engineers, BI analysts must also be able to present insights to all types of stakeholders.
Job growth in the BI Analysis field is seen to increase by 23.2% over a five-year period. In Australia, BI Analysts can earn somewhere between $105,000 and $120,000, allowing them to enjoy a comfortable and lucrative lifestyle.
5. Machine Learning Engineer
Machine learning (ML) engineers create and implement machine learning models and algorithms to solve complex issues. They closely collaborate with data scientists to launch these models and ensure they work well in real-world situations. Moreover, they identify issues within these models and make necessary adjustments to optimise their accuracy and performance.
ML engineers typically possess the following skills:
Programming: This includes expertise in languages like Python, Java, and R.
Machine learning: ML engineers must have a deep understanding of machine learning algorithms and deep learning frameworks like PyTorch and TensorFlow.
Data engineering: It’s also essential for these professionals to know data preprocessing, feature engineering, and model deployment.
Problem-solving: Having strong analytical skills can help these engineers develop innovative solutions for business problems.
ML engineers have an average base salary of $144,000 per year, according to the latest data from Indeed.
What are the Industries Hiring Data Analytics Talent in 2024?
In 2024, about 402.74 million terabytes of data are created every day. This highlights the growing importance of data analytics within businesses today. Let’s take a look at the industries actively hiring professionals with data analytics skills:
Finance: Banks rely on data analysts for personalised marketing, customer segmentation, and fraud detection. Insurance companies rely on this technology as well to assess risk, pricing, and claims management.
Healthcare: Hospitals and clinics use data analytics to improve operational efficiency, reduce costs, and improve patient outcomes. They also use it to conduct clinical trials and analyse medical data.
Retail: Data analytics help retail businesses make personalised recommendations and forecast market trends.
Entertainment: Streaming services like Netflix use data to make better recommendations to customers, optimise the user experience and make cost-effective choices.
Start Your Data Analytics Career with Lumify Learn!
To pursue a career and thrive in the data analytics field, it’s crucial to invest in the right course. How about enrolling in Lumify Learn’s Certified Data Analytics Professional boot camp course?
Through this program, you will learn essential knowledge and skills in data visualisation, data modelling, data mining, and data warehousing. You will also gain the following certifications:
Microsoft Azure Fundamentals: This will teach you foundational knowledge on cloud concepts, core Azure services, security, privacy, compliance, and trust.
Microsoft Azure Data Fundamentals: Learn the fundamentals of database concepts in a cloud environment. Get basic skilling as well in cloud data services and build your knowledge of cloud data services through Microsoft Azure.
Microsoft Power BI Data Analyst: In this certification, you will discover various methods and best practices for modelling, visualising, and analysing data using Power BI.
Once you complete this online course, you can join Lumify Edge, our exclusive career support program. Through this initiative, we will help you develop your professional brand and apply to jobs advertised by Lumify Group’s industry partners. You will even have access to internships and webinars that can bring your data analytics career to a whole new level.
Ready to harness the power of data? Enquire with us on a course today.