We’re thrilled to introduce our latest woman of data, June He! Since earning her B.S. in Mathematics/Economics from UCLA and her MSc degree in Smart Cities and Urban Analytics from University College London, June now works at Datatonic as a Data Scientist.
Throughout my school years, I enjoyed many different subjects including math, social sciences, and design. During my college internships, I also found interest in things like civil service and UX design. Upon graduation, I decided I would be really happy if my work ticked the boxes of:
After that realization, it felt natural to end up at a machine learning start-up as a data scientist, where I am very happy!
If you find it interesting, go for it! Be proactive and find out what people in the industry are doing.
Personally, I think that meetups are a great, effective resource if you want to break into a field, especially if you don’t know anyone in the field already. And if you do take that step and sign up for a meetup, don’t be put off if you find that you’re one of the few women in attendance, because — since the industry is currently male-dominated — you very well might be.
I used to fear that I might not be “nerdy” enough for a career in data science because I didn’t grow up loving Star Wars or something — and look where I am now! Whatever your reservation may be, don’t let it stop you. This field is much too fun to let self-consciousness stop you from pursuing your interests. There is no one “archetype” (gender, background, personality, etc.) for the perfect data scientist, and that same thinking can be applied to other fields as well, so don’t hold back!
Yes, absolutely. Data is an empowering tool/credential that is more democratic in comparison to other alternatives. While the accessibility to STEM education still has a long way to go before reaching equality for everyone, with the ability to utilize data, everybody has the opportunity to amplify their voice.
Despite its objective appearance, however, data — particularly the interpretation and utilization of data — is hardly without bias. If anything, data can expose, reinforce, and amplify biases. It’s for this reason that it is so important that those that work in data collectively make an effort to mitigate bias and promote its use as a tool for learning and empowerment.
Today's prevalent harnessing of data, like through effective and democratized machine learning, has created many new jobs, many of which are to automate other jobs. This ironic phenomenon is not new, as past technological advancements have led to the same thing — working in data science simply makes this even more acute.
While I don't have an answer as to where it may bring society next, I think we as data practitioners should continue to think about and discuss this more.
Apply agile methodology to life! I used to be obsessed with having a perfect blueprint before action, be it writing code or choosing a career. Now, I've learned that it makes more sense to try and test things quickly and iterate with the feedback and learnings you harvest along the way.