Career journeys: becoming a Data Scientist

How to become a Data Scientist

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From my point of view, three main things helped me get to my current position of Lead Data Scientist at Google and I also believe these things can help other Data Scientists in their career:

Skills and experience

The right education will help you to know the right skills. I studied Mathematics for my undergraduate degree which provided me with a great foundation on Applied Mathematics, Statistics, and Programming. This foundation helped me to learn more about Data Science. My masters on Statistics with Data Science at the University of Edinburgh gave me a deeper root on specific topics such as big data, machine learning and optimisation.

Working on many projects has also helped me to understand the workflow in data science research. I started as a Data Assistant, moved to Data Analyst, then did more programming as I progressed as a Data Scientist. I believe that by taking small opportunities it will lead to bigger opportunities.

Soft skills

Many new Data Scientists focus too much on technical skills. Sometimes, we need to understand that we are working with other people, not just on data and coding. That’s why a Data Scientist needs to:

• Be more adaptable
• Know the bigger picture
• Be more creative
• Learn to collaborate
• Be a team person

Determination

Data science is a fast-changing world. Discoveries and ideas are being developed rapidly, so it’s necessary to keep up-to-date by learning new knowledge, joining new research and projects, reading the updates on topics such as machine learning or artificial intelligence. I’ve been trying to make sure that I continue to dig deeper on my knowledge of data science.

What advice would you give to students who are interested in your area of work?

Data science has become more and more popular, as well as competitive, in recent years. As someone who has studied and is currently working in this area, there are several things I’d like to highlight to those who are fascinated by it:

Learn the fundamental skills – the maths and statistics behind codes

Many people mistake data science as just computer programming. Data science is much more than that - it presents the mathematics from the past, present, and the future of your data. It is of the utmost importance that students should study and understand regression techniques, times series, decision trees, random forest, stochastics modelling and machine learning. If you cannot understand the maths and statistics behind it, then there is no use for any data being analysed.

Learn programming languages

Once you have a handle on your analytical brain you should familiarise yourself with programming languages such as R, Python, SQL as these languages allow you to communicate with the data. In order to explore a trend, draw conclusions and make predictions, learning how to communicate with these coding languages is essential.

Gather real experiences while learning

It is always difficult to find a job if you have never had a real experience in data science while you are still studying. Your university is filled with resources that can help you gain insights and experiences – use it! Ask an academic if you can join their research project, get an internship on using data science in an analytical piece, or even join data science competitions such as Kaggle. Anything that allows you to showcase skills and resilience will bring you so much more attention while pursuing further study or a career in data science.