3 High-Paying Careers in Data Science You Must Take Advantage Of
By Lumify Learn Team | January 16, 2024
Have you ever wanted to start a career journey that combines your passion for problem-solving with the appeal of impressive earning potential? Look no further than the dynamic world of data science. As industries across Australia become increasingly reliant on data-driven insights, the demand for skilled professionals in this field has skyrocketed, offering not just job opportunities, but high-paying pathways to success.
The Australian job market has evolved to embrace data science as a cornerstone of progress. From the bustling finance hubs to the innovative tech startups, data science is reshaping the way decisions are made and strategies are formed. In this blog post, we'll list down some of the most high-paying careers in data science and discuss what you need to do if you want to join the industry.
What Are the Most High-Paying Careers in Data Science?
Data Scientist
Data scientists are professionals who use data to solve complex problems and generate valuable insights. They are often considered the “unicorns” of the data science field, as they combine a variety of skills and knowledge to handle large and diverse datasets.
Data scientists’ main responsibilities include:
Collecting data from various sources, such as databases, application programming interfaces, web scraping, surveys, etc.
Cleaning, processing, and transforming the data to make it suitable for analysis.
Exploring and analysing the data using descriptive and inferential statistics, hypothesis testing, and data visualisation techniques.
To become a successful data scientist, you need to have a combination of technical expertise, analytical prowess, and domain knowledge. This multifaceted skill set empowers you to navigate the intricate world of data and extract meaningful insights that drive strategic decisions.
Data scientists are in high demand across various industries, as they can help organisations leverage data to gain a competitive edge, optimise operations, enhance customer experience, or create new products or services. According to Indeed, their average salary in Australia is $113,000.
Machine Learning Engineer
A closely related career within the realm of data science is that of a machine learning engineer, who specialises in designing, building, and deploying machine learning models and systems. While data scientists focus on extracting insights from data, machine learning engineers concentrate on building the infrastructure to transform those insights into real-world applications.
Their main responsibilities include:
Collaborating with data scientists to understand their model requirements and objectives.
Developing machine learning models that are scalable, efficient, and can be integrated into production systems.
Optimising and fine-tuning models for performance and accuracy.
Deploying models into production environments, ensuring they function reliably and effectively.
Monitoring and maintaining deployed models and making necessary updates and improvements.
Machine learning engineers must have a strong foundation in programming, machine learning algorithms, software engineering practices, and cloud computing platforms.
The demand for these engineers has surged as businesses recognise the potential of machine learning technologies to drive innovation and automate processes. In Australia, these professionals command an average salary of around $130,000, making it a rewarding career path.
Data Analytics Manager
A data analytics manager oversees and coordinates data-driven strategies and initiatives. They are responsible for managing a team of data analysts and scientists, ensuring that the organisation's data-driven efforts align with business goals and deliver actionable insights. They also:
Lead a team of data analysts and scientists, setting goals, and providing mentorship.
Collaborate with various departments to understand their data needs and priorities.
Define and implement data analytics strategies to address business challenges.
Oversee the collection, analysis, and interpretation of data to drive decision-making.
A successful data analytics manager needs a deep understanding of data analysis techniques, strong leadership and communication skills, and the ability to translate complex technical concepts for non-technical stakeholders. They act as a bridge between technical experts and business leaders, ensuring that data insights drive strategic outcomes.
As organisations increasingly rely on data to inform their strategies, data analytics managers play a crucial role in shaping the direction of the business. Their expertise can lead to improved operational efficiency, customer experience, and overall business performance. In Australia, the average salary for a data analytics manager is around $145,000, reflecting the level of responsibility and strategic impact this role carries.
How can you start a career in data science?
The field of data science offers abundant career opportunities for those who are skilled and passionate about it. If you want to pursue a career in the field, here are some steps you need to take:
Know what to study
There are three main areas of study any data scientist is expected to learn, such as:
Programming: Python and R are the most recommended programming languages for data science. Python is a versatile, easy-to-learn, and readable coding language that is widely used. Data scientists can avoid problems with interfacing or compatibility thanks to its popularity. R, on the other hand, is more suited for statistical and academic work, as it helps data scientists perform in-depth analyses easily.
Machine learning: Data scientists need to know how to use popular machine learning algorithms to deal with and analyse large volumes of data. Machine learning enables professionals to improve data processing and draw more accurate predictions and conclusions from the information.
Statistics: To perform data analysis well, you need to know descriptive statistics, probability, inference, and how selection bias affects the conclusions. Other important topics include regression, hypothesis testing, statistical inference, and experimental design.
Enrol in a course
It’s also important to pursue a formal qualification. Most professionals start out with a completed undergraduate degree in a related field (i.e., mathematics, statistics, or information technology) before moving on to a post-graduate course.
However, you can also enrol in online programs in data science. Our Certified Data Science Professional course, for instance, teaches you the basics of database, cloud, and AI. It also gives you four Azure certifications from Microsoft, which are recognised all over the world. You’ll also learn to use your data skills in a Microsoft Azure environment, so you can apply them to real-world situations.
Pursue personal projects
Employers will likely favour applicants with a robust portfolio showcasing their skills, so experts recommend pursuing a multitude of projects. You can start with small projects (like machine learning solutions) and then move on to bigger ones (like predicting the stock market). This helps you apply your knowledge and determine what you need to improve on.
It also helps to network with other data scientists. You can connect with them online or in real life and learn from them and get feedback.
Start your data science career with Lumify Learn
As previously mentioned, Lumify Learn offers a Certified Data Science Professional course, which will teach you the fundamentals of database, cloud, and AI concepts. With our course, you can graduate with globally-recognised credentials often highly-valued by employers. It can even help you if you are an international student struggling to find work due to the lack of local work experience.
And as soon as you finish your course, you can join Lumify Edge, which connects you to industry professionals and prospective employers actively looking for professionals like you.
Start your journey to a fulfilling and prosperous IT career in data science. Enquire with us on the course today.