How to become a Data Analyst in Australia

Data Analysts are IT professionals who are trained to collect, organise and interpret data in order to gain insights and information for businesses and organisations. They liaise with managers and department heads to outline problems and questions and determine desired outcomes. Data Analysts then use computer software and programming skills to carry out statistical analysis of the collected data to enable companies to make more informed business decisions regarding anything from logistics and transport costs to improving sales and creating new products.

A Data Analyst determines how information can be used to solve problems and answer questions posed by management. They identify trends and patterns that enable them to make predictions and forecasts, gain insight into customer behaviours and discover opportunities for system improvements and market growth.
Modern businesses gather massive amounts of raw data, all of which needs to be pruned, organised and ordered by a Data Analyst. Once the data is ready for use, Data Analysts parse it through a number of analytic and modelling programs, approaching it from multiple angles to glean as much information as possible. They then translate their findings into presentations, graphs and simulations that can be easily understood by management and other non-technical personnel.

If you want to be one of the new breed of detectives and put your logic, intuition and technological abilities to work, then here are some of the steps you will need to take to become a Data Analyst.

Step 1: Learn some programming languages.
The first step towards your career as a Data Analyst is to become familiar with some of the programming languages used, the most common of which tend to be Python, SQL and R. There are many online resources and tutorials that can help you learn these languages or you can find compressed training courses, known as Bootcamps which focus on one or more of them. It is also recommended that you join GitHub, a massive online resource and community for programmers. GitHub allows you to post projects and gain feedback from professional programmers as well as functioning as a portfolio for you to showcase your work to potential employers.

Step 2: Obtain your bachelor’s degree.
The next big step is to formalise your education by undertaking a bachelor’s degree. Studying a Bachelor of Information Technology and Data Analytics or similar will give you a much greater understanding of the techniques and ideas involved in data analysis. Because data analysis is such a huge and important part of modern business, many employers prefer candidates who have also completed postgraduate studies, so it is recommended that you consider continuing your education and undertaking a master’s course in data analytics or a related field to increase your job prospects and earning potential.

Step 3: Volunteer, intern and gain experience.
Now it’s time for you to start to gain experience. Make sure your resume and GitHub account are neat, full and up-to-date before you start to apply for entry-level Data Analyst positions. Due to the growth of data analysis, there are a good number of job opportunities but it can be hard to get started when you have no experience, so don’t be afraid to volunteer your services, enquire about internships and promote yourself and your abilities. The more experience and projects you can show an employer the better your chances of landing your dream job.

Step 4: Keep current and market yourself.
Because the industry is constantly and quickly evolving, it is important that you maintain up-to-date knowledge of the current technologies, processes and techniques used in data analytics. Make sure you are always active in online forums, read industry articles and research emerging trends in data analytics to keep yourself at the top of your game. Another important skill to master is self-marketing, so make sure you have a solid Linkedin profile, maintain and add to your GitHub account regularly and don’t be afraid to promote your skills, you never know who might come across your online profiles.

What does a Data Analyst do?
Data Analysts are the detectives of the information age. They collect raw data gathered by businesses then clean and organise it to ensure there are no incomplete or irrelevant pieces. Data Analysts liaise with department heads and managers to determine a specific problem or question to be addressed, then apply complex statistical analysis to find hidden insights, information and patterns. They employ computer programming skills to apply algorithms and run simulations then produce models, reports and visualisations of their results.
The information that is produced by Data Analysts can be used in many different aspects of a business, such as identifying consumer behaviour and market trends, outlining transport and logistics issues or determining the feasibility and potential success of new products. Once they have gleaned information and answers, a Data Analyst must also be able to translate and communicate their findings in a manner that is accessible and understandable to non-technical managers and personnel.

Tasks

• Liaising with management to determine project guidelines.
• Gathering and processing raw data.
• Applying algorithms and statistical analysis to cleaned data.
• Creating models and simulations, making predictions and forecasts based on results.
• Identifying patterns, trends and anomalies within data-sets.
• Collating information and producing graphs and visualisations.
• Translating and presenting findings in an accessible format.

Skills for Success
Data Analysts are logical and critical thinkers with exceptional technical and problem-solving skills. They must be intuitive and analytical with the drive and ability to approach tasks from multiple angles. A Data Analyst needs to be extremely observant and curious, with great observation and pattern recognition skills. They must be proficient at computer programming and possess a strong understanding of statistics and mathematics. Additionally, a successful Data Analyst needs to be able to translate and present their findings in an accessible and meaningful way, so they require great verbal and non-verbal communication skills.

Skills & Attributes

• Organised and meticulous work habits.
• Pattern recognition skills.
• Excellent problem-solving abilities.
• Critical thinking and logical mind.
• Programming and technical skills.
• Strong mathematical and scientific understanding.
• Great verbal and non-verbal communication skills.