How to Become a Data Scientist in Australia
Learn from Eliiza’s experts
What does a data scientist actually do?
What degree do you need to become a data scientist?
Data science is a diverse and dynamic field, and there is no set path to starting a career in data science. Data scientists come from a diverse range of professional backgrounds and fields of study. Historically, data scientists have completed a university degree in fields such as statistics, computer science, and engineering. However, it is also common for data scientists to come from non-traditional fields and learn through alternative paths.
For example, at Eliiza, our team includes individuals from computer science, marketing and finance, mechanical engineering, mathematics, and biology research, some with a PhD. Despite these differences, a shared interest in problem-solving and a passion for data is commonly found among data scientists.
The amount of data being generated is constantly increasing, and as a result, the demand for data science skills is projected to grow along with the increasing use of machine learning and AI. We asked a few members of our team about their journeys, and in this blog we’ve summarised the necessary skills, and practical steps one can take in order to embark on a successful and fruitful career in data science.
Watch the following video to hear about the background of some of our data scientists, and learn about their journeys:
“If you want to be a good data scientist you have to think as if you were a business owner, but code as if you were a software engineer.”
Adapting to target audiences
Translating business problems to technical solutions
The ability to ask the right questions to identify the core of the business problem and then translate it into a technical solution is a vital skill for a data scientist.
Python is a versatile and flexible programming language that can be used for a wide range of tasks, from web development to scientific computing, with a rich ecosystem of libraries and active community support. As a data scientist, you can use Python to tackle the entire data science workflow, such as data wrangling, data exploration, machine learning model development, visualisations, etc. We suggest you familiarise yourself with the most common libraries used in data science, such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, and PyTorch, which make it easy to perform these tasks effectively and efficiently.
SQL is essential for data scientists to work with databases and extract meaningful insights from data. It is primarily used for data extraction and manipulation, such as joining tables, creating aggregates etc. More recently, cloud platforms even offer the capability of generating machine learning models directly using SQL (e.g., in BigQuery), making it an accessible and convenient option for data scientists.
Even if you don’t have a computer science background, getting your hands dirty and practicing your skills will go a long way. Building a portfolio of projects to showcase your work is also beneficial.
It’s not enough to be able to code and simply apply existing machinery to data.
You need to make friends with the data first.
It is important to have a deep understanding of data, which includes various skills such as statistics, analytics, visualization, and creating data pipelines. A genuine interest in the data and the willingness to experiment with different approaches is crucial when solving problems, especially when working with unusual or noisy data. Exploratory data analysis, feature selection, and feature engineering are all important aspects of the development process and you may even find that you need to come up with your own solution to solve a tricky problem.
It is also essential to have a good understanding of the entire data pipeline, including where the data comes from, how it is stored, and how to access it. Knowledge of best practices for setting up a data pipeline is also important.
To be a successful data scientist, it is important to have a comprehensive understanding of the various models available and the specific problems they can solve. A solid understanding of the different business cases each model is applicable to is also important.
Additionally, simply inputting data into a model is not sufficient. It’s necessary to apply basic statistics to understand the data and features first. In other words, walk before you run. There are usually a few models you can choose from, but having a deep understanding of what’s happening under the hood is crucial in selecting the right one and interpreting the output of the model.
Practical steps you can take today
Want to know the expert tips from our data scientists for launching your career in this field?
Watch the following video:
Finding where your passion lies
If you love what you do, you’ll be naturally more curious and inclined to research and experimentation, which leads to better results.
Challenging imposter syndrome
“Because I did not get formal data science training, the first thing would be imposter syndrome. For example thinking that I would not be qualified for a job, or that I would not be able to find a solution for a particular problem.” – Vivian Mai
Being an active field of research, data science evolves every day, and there are regularly new technologies and problems emerging. It’s not necessary to know everything. Even if we don’t know something today, a great data scientist can identify knowledge gaps, learn those skills, and come back tomorrow having achieved something new.
Building a portfolio
The most effective way to learn is by gaining practical experience. Websites such as Kaggle offer a great opportunity for this, particularly for those just starting out and looking to build a portfolio to showcase to potential employers. There are a variety of challenges, free data, and a helpful community to support you. The more varied the projects, the better, as it enables you to use different techniques and tools. Additionally, if you are struggling with a specific challenge or in need of inspiration, the community is there to help.
Seeking out opportunities
Do what you can to flex those data science muscles. Seize any opportunity which allows you to employ your data science skill set, whether it be through volunteering for tasks within your team or reaching out to others for help. It’s not a title which makes you a data scientist, but the nature of your job. To progress in your career, it’s recommended to try new things and continuously learn by taking courses or participating in challenges like Kaggle. This will not only help you improve your skills, but also discover new passions within the field that may lead to new opportunities.
Building your network
Networking and building a community is invaluable when starting a career in data science. Joining a professional networking platform like LinkedIn and reaching out to people in the field can open doors to new opportunities. When connecting with people, it is important to personalize your message, rather than simply adding them as a connection. This will increase your chances of getting a positive response and hearing about more opportunities.
Staying up to date with new technology
How long is it going to take to become a data scientist?
Given the complexity of backgrounds that data scientists come from, and the different paths one can take, the timeline is not well-defined and can vary greatly for each individual. Some may be fortunate enough to secure a position immediately after graduation, while others may take months or even years, depending on their current commitments and the amount of time they can dedicate to learning.
Becoming a data scientist takes time and effort, but having a plan and being consistent in your learning will help you achieve your goal. To maximize your chances of success, it is best to clarify your career goals early on, and take proactive steps to build a strong portfolio, expand your network, and gain hands-on experience through data science projects. Initially, focus on understanding the general concepts and gradually build your knowledge, and try to get hands-on experience as much as possible. Make sure to educate yourself from credible sources, whether that be through a university program, online courses, or learning from colleagues.
Don’t be afraid to take on new challenges and put yourself out there, as it will only make you a better data scientist.
Check out Mantel Group’s Emerging Talent Programs to get a head start on your career journey, and have a look at the resources below to get started.
Check out some of the resources mentioned by the Eliiza team:
- AI Australia Podcast
- Data Science Central: https://www.datasciencecentral.com/
- LinkedIn influencers: Cassie Kozyrkov and Allie K. Miller
- For online courses:
- Toward DS https://towardsdatascience.com/
- Roboflow – https://blog.roboflow.com/
- Vicki Boykis Blog – https://vickiboykis.com/
- Reddit – https://www.reddit.com/r/datascience/