How to Start Your Journey as a Data Analyst
Data science is a rapidly expanding industry sector, and anyone considering becoming a data analyst can look forward to a rewarding career with many opportunities for growth and development.
Recent estimates quoted in a Deloitte Access Economics report suggest that every year, an additional 2.5 quintillion bytes of data are being created worldwide. To make use of that data, the world will need skilled data scientists.
The report also predicted above-average job growth in the data science sector, which was backed up by SEEK’s projection of 27.7 per cent job growth over the next five years.
For many sectors, wage growth remains stagnant, but this doesn’t seem to be the case for data scientists. In Australia, the average salary of a data scientist is around $92,667 per year, according to Career FAQs.
The Martec put the salary scale at $51,000 to $94,000 for a data scientist starting their career in Sydney or Melbourne, and at $77,000 to $147,000 for a data scientist with five years of experience.
SEEK listed the most common salary for data scientists in Australia as $130,000, and also returned surveys from current data scientists who reported high levels of job satisfaction.
With all this great data, you may conclude that you have found the perfect career. But where do you start?
Learn to Code
Kshira Saagar is the chief data officer at Latitude Financial Services, and he has some great advice for anyone who wants to find out how to start their journey as a data analyst.
Kshira advises that while you can work in data analysis without programming skills, you will be able to work on more interesting tasks if you can code.
“Learn coding constructs, not just the particular coding language,” Kshira says.
Coder Academy educator Mike Dane had similar advice.
“All web development and data analysis will hinge on the same core foundations,” Mike says.
If you are curious about data analysis or thinking about switching careers, then learning a bit about coding can be a great way to dip your toes in the water.
You might want to start out with some free online courses that introduce you to programming. Coder Academy’s Learn to Code: Free Taster Course teaches you how to build a simple terminal application using Ruby programming language.
Once you have a taste for coding and data analysis, it might be time to consider pursuing some formal qualifications. At Coder Academy, you can study a Diploma of Information Technology in a bootcamp format, preparing you to embark on an exciting new career in a matter of months.
Learn Some Data Science Basics
Learning the basics of data science will give you a solid foundation that you can always come back to.
Study statistical analysis, learn how to find and choose data sets, become a competent science communicator, and learn how to visualise data well.
Mike Dane suggests that visualising data is a particularly important skill, as it allows you to show the results of all your hard work.
“After all, this is why they hired you,” Mike says.
Kshira Saagar also emphasised the importance of visualisation, listing it alongside other essential soft skills, including communication and collaborative skills.
At last year’s WomenTech Global Conference, data analyst Simran Yadav gave a talk offering advice for new data scientists. Simran also stressed the importance of visualisation and communication skills.
“Put together a story. The storytelling aspect is really undervalued in data science,” Simran suggested.
Tableau is a visual analytics platform that Simran particularly likes for visualising and sharing data. However, it’s more important to learn the skills than any particular system.
Learning to communicate effectively will mean taking an interest in things outside the world of data science.
“Get some domain knowledge,” Simran says, explaining that she has been better able to translate concepts within a given business setting by combining her previous marketing experience with her analytics knowledge.
Join the Data Science Community
If you are just starting your journey in data analysis, then you will find that there is a great community to become a part of. It’s never too early to start making industry contacts, to learn from others, and to find out about the resources available – from open source frameworks to publicly available data sets.
You will find networking opportunities and meet up groups in most major cities. Brisbane, Melbourne, and Sydney each have their own data science groups on Meetup, while forums like Reddit can help you to connect to the community virtually.
You can begin building your portfolio on development platform GitHub; find data sets, build models, or enter competitions through Kaggle; or use open source software frameworks such as IBM’s Apache Hadoop. There are numerous blogs and publications to read including Towards Data Science on Medium.
The idea is to start building your skills, and to become connected to others in the data science community. You will find many other groups and opportunities out there. Some will be hyper-local, and others will allow you to connect with people from across the globe.
All the time you are learning new skills, you are also building a potential portfolio of projects. Mike Dane suggests that you also take the opportunity to document the learning process itself.
“Learn in the open,” Mike says.
Documenting all that you learn and achieve is a form of personal branding, but it will also help you to form meaningful connections within the data science community, because you are giving back to that community by providing valuable guidance for others.
By focusing on the process of learning new things (rather than just seeing it as a means to an end) you won’t have the same fear of being a beginner, and you should be less likely to experience the stress of imposter syndrome.
Another tip to help those who don’t feel like real data analysts yet is to embrace any previous experience that you may have, whether it is in a related field or not.
Simran found that her marketing background helped her to become a better data analyst for her company, and Kshira observed something similar with colleagues in his own company who had only moved into data analysis later in their working lives.
If you’re still not sure whether you have the skills that you need, then spend a bit of time looking at job postings. Simran suggests jotting down the most common requirements for the roles that interest you. You then have something to work towards, and a way to know that you are becoming job-ready.
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