How to become a true data scientist
“Real-world data is rarely delivered to us in a perfect spreadsheet” warns Dr Rebecca Vivian, a computer scientist at the University of Adelaide.
“Experience working with messy data is necessary to grow your skills in selecting, managing and organising data with confidence”.
The path to becoming a data scientist always starts in the classroom, but you won’t survive in the wild unless you have experience working on real industry data. The world produces 2.5 quintillion bytes of data each day and it comes in infinite forms. Searches, photos, tweets, speech, weather patterns, money transactions, music streams, ride-hailing trips or surveillance footage. The list is quite literally endless.
Getting your hands dirty with projects and case studies will prepare you to be more nimble and ready to tackle the challenges that inevitably come with anything that happens outside of a controlled environment.
Besides, it wouldn’t be any fun if it was easy.
“Data science is not a spectator sport,” says Dr Lewis Mitchell, a data scientist at the University of Adelaide. Applied Data Science is about problem-solving and getting to the bottom of how something really works. Whether it’s how a disease spreads, how weather happens or how fake news travels - mathematical models help us understand what’s going on and predict the future. And that type of data sometimes doesn’t play by the rules.
Some may argue that data science has never been easier with the explosion of machine learning and other accessible software that can take care of the heavy lifting.
“All the machine learning in the world won't help you as a data scientist if you can't take a messy dataset, clean it, analyse it, visualise it, understand and interpret your results,” Dr Lewis Mitchell said.
Dr Rebecca Vivian agrees. “Practical skills in data science provide the tools you need to explore and investigate data rather than relying on someone else or an application to do this for you. It is empowering to have practical data science skills to look at your data and seek the answers to your questions”.
It also helps you spot the issues only a skilled human can.
“In some cases, the data we use and rely on may be biased or flawed. Good data scientists are always actively and critically thinking about the data they collect and use to ensure solutions and decisions that rely on data are ethical and have the best outcomes for all involved”.
Practical skills are also what employers are seeking. The job market for data scientists is booming at the moment, but employers still want to see you demonstrate your talents. This is why applied learning, projects, case studies and a portfolio is so important.
If you want to be respected as a top data scientist, you need to have both the theoretical foundations and practical skills. The opportunity is there, but you need the expertise to be able to make the most of it. Or in Dr Lewis Mitchell’s words:
“Data is the new oil, and now is the time to learn how to mine it”.
If you’re interested in becoming a Data Scientist, a Master of Data Science (Applied) at the University of Adelaide is your practical and industry-aligned option. It’s also 100% online (which means assessments instead of exams!).