Join Lillian Pierson, P.E. for an in-depth discussion in this video What advice would you give to women within tech, specifically?, part of Insights on Data Science: Lillian Pierson.
- As far as women in technology, I can only speak from my experience and that is that I was raised in a family in a subculture that told me that women can't do arithmetic. And I was raised to be a school teacher or a nurse and so, that stereotype really limited me up until my 20's. I had a teacher, she was a Ph.D in Chemistry and she taught me chemistry in high school.
Just her presence and the fact that she could what she did showed me that I believe I could do what I was going to do. It gave me hope and encouragement. Just because I'm a woman doesn't mean I can't do something incredibly technical. So in my career, like I said, I did a lot of research and educated myself and then I would take that knowledge into my meetings and I would mention all these different potential solutions. And some of my managers liked that but then a lot of times my colleagues didn't like that because it looked like to them that I was showing off but what I was trying to do was to keep moving forward in my career and not to stalemate.
I think that just going into everyday with updated knowledge and a passion to really do the very best you can with the projects you have helps to garner the support of employers in getting more training for you. But I also think sometimes workplaces may or may not be willing to help and you can't let that hold you back. Like I said, what I did was I went and worked after work because if I ever depended on my employers or really anyone else to give me something, I would have always been at their disposal.
I never would have gotten anywhere. To me, it's like everything in life is about deciding what you want to do and then do it.
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