I’m Yuhe, and I am a data analyst in the Global Data Department at Bloomberg.
Mathematics has always been my favourite subject. I enjoy resolving problems through different approaches and am attracted by how models can resolve complicated real-life scenarios.
I studied Mathematics and Statistical Science for my bachelor’s degree and Risk Management and Financial Engineering for my Master’s.
The analytical, critical thinking and problem-solving skills obtained in my degree are transferable in many different areas.
Team-working skills through many collaborative projects, presentation skills and project management skills can also be applied in most roles.
They make sure you can adapt to the rapid changes within technology, and help you to be more rational when dealing with many problems in real life.
STEM degrees are great because they give you more insight into the latest technologies and innovations.
Maths is a good starting point if you want to be a data analyst
To become a data analyst, I really recommend learning mathematics and statistics.
You will gain a firm understanding of how to interpret and analyse data, and the ability to refine your logic to solve problems.
Data can be easily misinterpreted, so it is important to be sceptical and think through before reaching a final logic.
I also recommend learning some programming languages, such as python or R. They are very useful when conducting analysis on large datasets and improve the efficiency of analysis.
The application of skills is also very important. Instead of focusing on more theoretical knowledge, I recommend learning by doing.
Being a woman working in data isn’t something I tend to notice
Being a woman working in data isn’t something I tend to notice at Bloomberg. Men and women are given equal opportunities and everyone’s ideas are appreciated and supported. Now, I just want to leverage my skills to bring great impact in this industry. I want to improve on my technical skills further and work on projects with innovative technology – it is developing so fast!
