Maria Smith is a PhD student in Sociology, a D-Lab Data Science Fellow, and Chancellor's Fellow, and works at the Institute for Research on Labor and Employment (IRLE), where she assists underrepresented students with data analysis and provides training in statistics and programming.
Q: When did you first get interested in Data Science?
A: I was mostly motivated to code after enrolling in a Sociology of Gender course. The lesson was about how as a society we usher boys and girls into different career paths based on our own preconceived ideas of gender. My professor read a quote to the class, and I will never forget it. The quote said something along the lines of, 'there would be more women in computer science if women in the social sciences quit complaining about inequality in STEM fields and learned to code.' Well, it lit a fire under me. The next semester I picked up information science as a minor, not because I thought there was any truth to what he was saying but because I've always had an 'I'll show you' attitude. There, I learned to code in python/R and took more advanced statistical courses.
Q: Are you planning on using Data Science for your future career?
A: I certainly hope to continue sharpening and expanding my knowledge in data science as I progress as a PhD student and academic professional. I am interested in continuing my research on the social implications of predictive analytics but in the future, I would like to one day implement reverse engineering methods in order to understand discriminatory classifications in machine-based decision making.
My advice would be to not give up. To be kind and patient with yourself as you begin to learn new concepts.
Q: What advice would you give students who are interested in data science?
A: My advice would be to not give up. To be kind and patient with yourself as you begin to learn new concepts. I would also encourage new students to get involved in a project that will draw on new skills right away. DO NOT WAIT! You will never know everything, technology moves too fast for any of us to stay on top of everything new in data science. But most importantly, I would encourage new students to get involved because your best teacher is practice. It really is the only way to get better.