Hello, Aishu! How did your interest in Data Science evolve?
After finishing my undergraduate degree in electronics engineering, I realised I loved research and wanted to push it further. I started my career in academia, with a focus on satellite and wireless communications since I truly loved working with antennas and tracking satellites. At the same time, a friend introduced me to programming and I became fascinated by what a few lines could do.
When my family moved to Japan, I landed an internship at a software company and learned Python. This ended up being a great stepping stone for a career in data science. I realized that I really enjoyed going from messy data to clean insights and finding patterns to make better decisions.
Long story short, I attended a
Le Wagon workshop for data analytics and really enjoyed Trouni’s (one of our Lead Teachers) teaching style. When I learned that a full
Data Science bootcamp would be taught the next semester, I immediately decided to apply.
What did you like about the bootcamp?
The whole curriculum was challenging and well-organized. Learning all of that stuff laid a solid groundwork not only for my graduation project but also for all my personal projects afterwards.
I particularly loved the data analytics module. Never having learned statistics before and discovering some patterns in data was a blast.
It was sometimes challenging for me to understand the theoretical part of some machine learning models like linear regression, but
Le Wagon teachers managed to break down the subject in a very nice and clear way. The greatest thing about the bootcamp is that even the most difficult technical knowledge can be taught to people coming from non-math backgrounds like me.
What did you do for your final project?
Together with my teammates Arron and Njeri, we worked on an application that could split any song into different musical instruments. At first, we researched models that could be a good case for separating tracks, and after that we split up roles to speed up the development process. I was working on the front end (using Streamlit) and some back-end on limited parts. I am quite proud of our pitch during Demo Day, and I am pretty sure it caught some people’s attention!
What did you do after graduation?
After the bootcamp, I started working on a personal project with a goal to predict poverty. I was inspired by a few Stanford students who wanted to use satellite images to map poor regions. It helped me realize that my passion lies in working with meaningful projects and I started to apply for jobs with that in mind.
Eventually, I received two job offers and accepted a junior data scientist position from
Virtusize, my current employer. As a shopaholic, I am very familiar with the frustration of buying clothes that do not fit and having to return them. Virtusize solves this problem by developing a virtual fitting solution that enables online fashion retailers to illustrate size and fit for consumers. I am very excited about the product and will work doubly hard to help Virtusize become the standard solution for size and fit in the Asian online fashion industry and beyond.
Over 50% of our company are women. Let me tell you more: the whole data science team are women! I’m proud to work in a supportive environment where everyone is encouraged to pursue their passions regardless of gender and background.
Sounds amazing! Thanks for the talk, Aishu. Wishing you all the best in your career journey.
