Join Lillian Pierson, P.E. for an in-depth discussion in this video What advice do you have about diversifying the workforce in tech?, part of Insights on Data Science: Lillian Pierson.
- As far as diversity in tech and in data science, I see two areas of great need. One is to have more women in tech and the other is to have more Americans working in the field. So, we have a lot of tech jobs in the United States and we don't have a lot of American citizens to fill them. Americans need jobs. I think it's really important that the high school systems and the junior high systems that we start really pushing this, like get skilled in math and science, you can do this.
And start encouraging people, 'cause it's more encouragement I think. The whole idea that I got like math is too hard, women don't do math. So, as a young person, I actually believed that about myself and people believe this about themselves, so they don't pursue something, and it limits their whole life experience. And I just think that it's a matter of changing the messaging and then people actually stepping out on a limb and saying I'm going to do this, I'm going to make the extra effort. And, as a result, they get these careers that are just absolutely amazing.
I talked to someone from Pakistan the other day and he said that everyone there becomes a doctor or an engineer, because that's what they're told to do and that's the only thing that's really validated. And I don't think that's good and neither did he, but I think that the culture is responsible for a lot, the choices people make when they're young and they're deciding what they want to study. But the employers, by the time you get a job, it's already too late, you made your decision. And people, when they're 18, they're not thinking about, they're not really thinking about jobs usually.
They're just trying to do what's right in front of them. I have a daughter and my plan with her is to encourage her and to teach her to program by the time she's five, so she's just fluent that that's part of what she does. And I want to also teach her, as part of her curriculum in primary school, digital marketing and these things that are going to help her earn for herself when she gets older, because I think it's important. Her father and I are both programmers.
We both know how to program, we both did heavy STUN work, and I see that it's given both of us so much freedom and flexibility for our lives to be able to work in these capacities, and I want her to have that.
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