In the simplest terms possible, what is machine learning?
Machine learning is just a part of what the data science team does. To put it into some context, machine learning is the process of training a model to make predictions.
It’s easy to describe machine learning if you think of it like table tennis. A data scientist is like a table tennis player, we have data coming in, in different formats, which is like the ball. This could be short or long, with varying amounts of spin. The bat we use to play the ball back over the net is like the machine learning model we produce. The model reacts to the data to produce a favourable outcome, and return the ball back to the other side of the net. Using a poor bat means you can’t return the ball well, and therefore reduce your chances of winning the game.
How might people interact with data science in their everyday lives?
Machine learning is already in people’s lives, it might be hard to notice because it fits seamlessly. When you talk to phone or smart home device, machine learning is used there to understand language. When you go to buy things on Amazon and there is a list of recommended products, that is using machine learning.
What’s your favourite thing about data science?
I find it fascinating that we can teach an algorithm to learn. It is also great to work with a business that is on board with it and see the impact of machine learning models at work when everything fits together.
How did you get into data science?
It was a natural progression, I studied maths at university, and I was always interested in generalising and problem-solving. When I started to learn how to code it was interesting, but I wasn’t that keen on writing the front end or backend of a website.
That’s when I discovered data science, where I can combine coding with problem-solving. So I studied a lot of that on my own, and practiced the skills on platforms such as kaggle (an open-source platform for coding challenges).
Can anyone be a data scientist? What advice would you give someone who wants to work in data science?
There are a lot of similarities between data science and sport. There are some sports that suit certain people compared with others. Anybody could play sport but it takes a lot of dedication and effort to fine-tune these skills and improve.
There are various pathways to gain the knowledge in data science available online, but the key skills required to succeed in the field are: Communication, keen interest in data, coding fluency and continuous practice.
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