The world we live in is full of data, and data continues to grow in both relevance and value. IDC predicts the world’s data will reach 175 zettabytes by 2025. If we were to store all this data on DVDs, the stack of DVDs would circle the Earth 222 times – so basically, a lot of data! Our devices, transport and even our brains process data to record and make decisions in relation to the world around us.
There is a lot of hype around the term “data science” today. Sometimes when I tell people what I do, they seem intrigued but also a little bit confused. So let’s start by demystifying what data science is.
For me, at its core, it’s a way of solving problems using data. That could be through adding value to companies by helping them make better business decisions, supporting social change using data for good or even figuring out what TV show to watch next. The realm of data science is seemingly exponential in its growth and application. A lot of opportunities are becoming available, putting you at the heart of any company’s decision-making process.
How did I get into data science?
My knowledge of data science was not very apparent when I was younger. However, my ambition to solve problems and change the world around me was. As I got older, I decided that I wanted to make a positive difference in the world using technology – sounds cheesy, I know but it’s true! I decided to study electronic engineering at university, and during my final year I ended up taking a computer science module called The Foundations of Machine Learning. This opened my eyes to the power of machine learning algorithms and their range of potential applications.
It was a combination of this module and an electronics module on signal and image processing that inspired me to explore computer vision for my third-year project. At the end of that year, I graduated with a bachelor’s degree and started as a data scientist at SAS UK & Ireland.
There is no perfect way to get into data science – there are so many different opportunities and routes into this field. But here is my advice from what I’ve learnt so far in my career.
Be curious and willing to learn
It doesn’t matter what your background is as long as you’re curious and willing to learn. Coming from an engineering background and being just six months in the data science field, I’ve learnt about the importance of data preparation and exploration, improved my knowledge of statistics, developed my understanding of business intelligence, learnt a whole new programming language and somehow started to become a bit of an NLP nerd (that’s natural language processing).
This is just a fraction of the things that I’ve learnt so far, and I’m still learning. Being curious and willing to learn, as well as not being afraid to ask for help, is the perfect combination to make the most out of any opportunity.
Where to start?
Data science is such as a broad field, there’s so much that you could explore. As a result, trying to figure out where to start can feel a bit overwhelming. But you don’t have to try and learn everything all at once. In fact, it’s probably impossible for one person to know everything there is to know about data science. The best thing to do is to start small, but start now.“The man who moves a mountain begins by carrying away small stones.” Confucius
If you want to build something brilliant, you’ve got to be willing to start small. You could start by simply picking a topic that piques your interest. A lot of people start by using e-learning and online courses to help learn the fundamentals of data science, but you should do whatever works best for you. Maybe that’s through reading, listening to podcasts, watching YouTube videos or learning face-to-face.
Make it fun
Although theoretical knowledge is great and very useful, it’s always good to get some hands-on experience. Creating your own personal projects allows you to do that and choosing something that interests you will make it a lot more fun. It’s a great way to learn new skills and definitely something I want to do more of.
Think of a problem or a question that could be answered using data then get your hands on some of that data. You could look on places like Kaggle, scrape it from a website or even collect it yourself. Also, telling people about your projects and your findings is good practice because as a data scientist, you need to be able to explain what you have discovered. In my role, communication skills are very important, especially when it comes to explaining insights to people in the business who may not be data science savvy.
There are a number of different ways to get involved; attending meetups and conferences (which can also be done virtually), finding an online community or using LinkedIn and Twitter. Networking and attending events is great as it’s another way to learn and also transfer knowledge.
Choosing the right tools
There is a wealth of tools, software, courses, books and online learning available. SAS, for example, has a lot to offer and is open source friendly. Here are some links to explore more:
- If you’re at university, find out how to expand your data science skills by getting free access to advanced analytics software like SAS Viya for Learners.
- If you’ve just started this journey like me, check out this rich source of information for developers on using SAS and open source
What makes being a data scientist great is a mixture of things, so it will vary from one person to the next. What I want you to take away from this is that there is no one-size-fits-all approach to becoming a successful data scientist. Your journey could look very different from mine, but I think as long as you’re curious, willing to learn and get involved, you’re on the right track – especially if you can have a little fun along the way!