How to become a Data Scientist in Australia
Data Scientists are trained masters of information analysis. They are multidisciplinary IT professionals, part statistician, part programmer, part psychologist and part experimental scientist. Companies typically employ data science to solve a problem or gain insights into how to achieve a goal, such as increasing sales or improving conversion rates.
A Data Scientist takes the base problem and poses questions that will enable them to solve the problem. Then they collect, organise and analyse large amounts of raw data, creating and applying algorithms that identify trends and patterns and attempt to gain insight and answer their posed questions. Once they have obtained their answers, Data Scientists create visualisations of the analysed data and present their findings in a way that is accessible and understandable to the non-technologically savvy.
Data Scientists employ many different techniques in their pursuit of meaning within information, from statistics, linear algebra, machine learning and computer science to software engineering, data intuition and analysis of human behaviour. Due to the massive amounts of data that most companies produce these days, Data Scientists are engaged in many different industries. They help to improve search engines, increase retail sales, predict market trends and are even involved in the development of future technology, such as self-driving cars.
Are you fascinated by data, statistics, programming and psychology? Do you want to put your logical, analytical and technical abilities to the ultimate test? Here are some of the steps you will need to take to become a Data Scientist.
Step 1: Study linear algebra and programming.
There are many different skills that you will need to learn on your way to becoming a Data Scientist, so it is recommended that you start early and learn the basics online. Linear algebra is a branch of mathematics that is widely utilised in data science and is, therefore, an important theory to familiarise yourself with. It is also recommended that you learn the basics of some of the programming languages most commonly employed by Data Scientists, primarily Python and R.
There are many online tutorials and resources to help walk you through these skills, as well as Massive Open Online Courses (MOOCs). Another option is to attend intensive training sessions, known as Bootcamps, which are becoming increasingly popular. When starting out with programming languages, you should sign up to GitHub, a huge online programming community and an excellent resource. Here you can post your projects and gain feedback from experienced programmers, and it will also serve as a portfolio of your work that potential employers can view.
Step 2: Learn probability and statistics.
The next important concepts you will need to master are probability and statistics. Although you can learn the fundamentals through online resources, it is recommended that you gain formal training in these concepts to better equip you for enrolment into the Graduate Diploma or Master of Data Science course later on. There are a number of introductory and short course options to learn statistics and probability but if you wish to continue on to higher level study of data science, it is recommended that you complete a bachelor’s degree in either science or mathematics with a focus on probability and statistics.
Step 3: Complete a course in data science.
The final step in your education is to complete a specific course in data science. There are two main options for this training; you can undertake a Graduate Diploma of Data Science or go the extra step and enrol in the Master of Data Science course. Because the role of Data Scientist is so important and complex, most employers will look for candidates with a master-level education. These degrees will give you a thorough understanding of the many aspects of data science and how best to utilise them in real-world situations.
Step 4: Enter competitions and apply for internships.
Now that you have completed your education and are equipped with the necessary skills, you can start applying for work as a Data Scientist. Many Data Scientists begin their careers by undertaking an internship or even volunteering, so don’t be afraid to start small and work your way up. Make sure you have an active and full GitHub account with plenty of examples of projects that you have created, whether they are from university assignments or just from practice problems that you have tackled along the way. Offer to do work for friends and family to build up your portfolio and make sure you are active in online data science communities, as this is a great way to hear about job opportunities.
It is also recommended that you enter data science competitions. There are a number of these online and they are a great way to improve your skills, meet and learn from other professionals and also gain the attention of potential employers.
What does a Data Scientist do?
Data Scientists collect, sort and analyse information from various sources in order to answer questions and solve problems. They generate and apply algorithms, use statistical analysis and employ advanced concepts such as machine learning and data visualisation. They use these techniques to find patterns, trends and hidden insights within large amounts of raw data. A Data Scientist is trained in a number of disciplines, such as mathematics and statistics, computer programming and AI, human behaviour and the use of the scientific method. They pose questions that will enable them to solve problems or achieve goals set by a company or client, gather and analyse data from multiple angles to find meaning and answers, then parse the results into visualisations and presentations that can be understood by business leaders and department heads.
- Collecting, cleaning and organising massive amounts of data.
- Determining client needs and framing relevant questions.
- Creating and applying algorithms and other complex techniques to glean insights from data sets.
- Exploring and analysing data from multiple angles to determine trends and patterns.
- Devising data-driven solutions to complex problems.
- Generating visualisations and models of data and findings.
- Effectively communicating results to non-technical personnel.
Skills for Success
Data Scientists need a combination of technical and creative skills and attributes. They must be naturally curious and inventive, intuitive and able to approach problems from multiple angles. To be successful, Data Scientists must be analytical, logical and critical thinkers with good pattern recognition and observation skills. They also require great computer programming skills, in-depth understanding of probability and statistics and an affinity for mathematics. In addition, a Data Scientist must be able to present their findings in an accessible and understandable way, so they need excellent communication skills in order to present their findings and results to non-technical personnel.
Skills & Attributes
- Passion and dedication.
- Intuition and creativity.
- Deductive reasoning.
- Excellent problem-solving skills.
- Logic and critical thinking.
- Computer programming proficiency.
- Affinity for mathematics, statistics and probability.
- Ability to recognise patterns and trends.
- Great communication skills.
What do Data Scientists earn in Australia? In Australia, the average salary of a Data Scientists is around $92,667 per year. This varies greatly depending on a number of factors and is intended as a reference only, from Payscale 04/18