Ravit Jain is a data science evangelist, and the founder and host of ‘The Ravit Show’ – a community of over 300,000 data science and AI professionals. If the last few years have shown us anything, it is that data science and AI will be driving a lot of the technological advancements in the coming times. It is also a field that is in constant flux, which means staying up to date is a lot easier to do when you have a community to fall back on when you hit a hurdle.
Are there any common misconceptions about data science? Many people, Ravit says, believe data science is all about coding. While programming, he says, is an important part of data science, maths and statistics play an important role too. “Data science involves analysing and interpreting data, which requires a strong understanding of both maths and statistics. Make sure to study topics such as probability, linear algebra, and multivariate calculus,” he says.
Being a good communicator, having the ability to present findings to non-technical audiences, and having the capability to tell a story with data – called data storytelling, which refers to communicating insights from a dataset using narratives and visualisations – are other key skills needed.
Another misconception, Ravit says, is assuming that you can’t be a data scientist without a PhD. “I have seen people who just have a Bachelor’s degree turn out to be such great data scientists,” he says.
And being a data scientist doesn’t mean you work only with ginormous datasets. It also involves working with small and medium datasets, along with qualitative datasets.
In technology, should you be a generalist or specialise? Generalists, Ravit says, tend to be more adaptable, while specialists have deeper understanding of a particular space. “The best approach is to find the right balance between the two. Keep upskilling with new and emerging trends, along with being proficient in hard core skillsets. Always keep a close eye on the job market and the type of role that is in demand, this will help you focus on important areas and be up to speed with the ever-evolving tech space,” he says.
The best way to learn data science, he says, is to get hands-on experience working on real-world projects. “Also, don’t be afraid to ask questions and try new things, even if you don’t have all the answers.”