Join Lillian Pierson, P.E. for an in-depth discussion in this video What is the biggest obstacle you've overcome along the way?, part of Insights on Data Science: Lillian Pierson.
- Some people, I think, really have a big visibility on the Internet, and sometimes there are people that will say things to try to hurt me, and I have to just kind of look the other way, because, usually these are small-minded people, and it really has makes no difference, because, so long as I'm doing a good job at the work I have, and my clients are happy, what these other people have to say, is totally irrelevant to me. In my career, I've had instances, in my, my female colleagues have also have instances where, maybe we weren't taken seriously, or we were treated like differently.
Either based on our gender, or based on the way we looked, and it's hurtful. Not only is it hurtful, it's discouraging. It helps us feel like we need to not be who we are in order to succeed, and that doesn't work. And in order to succeed, we have to be who we are, and I think the way around this is to, for me has been to band together with other strong females, and if your work stands on its own, then those other people will fall to the side.
It's a matter of not letting one's self be pigeonholed, and not being, letting one's self be pushed down for being who you are. Because if your work stands on its own, then you're going to do great. I did a lot of research, and I was very active already in the online community for data science, so I figured out what I wanted to do, and how that could benefit my workplace, and I did it. And I didn't care. I didn't let the title hold me back. I found solutions, and I implemented them.
And my workplace was really grateful for that, and they encouraged it. They supported me to continue education, and all of that, and I think the most important thing for people is don't get caught up in the drama. You just figure out what it is that you want to do, and go for it, and let the rest sort itself out. Cause it will.
Lillian began her career not as a data scientist, but as an environmental engineer. Here, she shares her story, discussing how she taught herself to code in Python and R, and work with data science methodologies. As a result of her own experiences, Lillian is passionate about helping those interested in data science—but who may lack a four-year degree in the discipline—get started in the field. She shares practical ways to acquire the skills and experience needed to become a data scientist, and best practices for landing a job. Lillian also dives into grappling with the challenges that occur in rapidly evolving tech workforces. Plus, she discusses the industry itself, covering recent changes in the field and areas of need, and clearing up a few common misconceptions.
- Practical ways to acquire data science skills and experience
- Which courses should you take to become a data scientist?
- What challenges should people be prepared to encounter?
- Best practices for landing a job in data science
- Common misconceptions
- What key personality traits are common among successful data scientists?
- How has the industry changed in recent years?
- Practical advice for minorities and women pursuing a career in data science