Join Lillian Pierson, P.E. for an in-depth discussion in this video What do you think are the greatest areas of need in data science?, part of Insights on Data Science: Lillian Pierson.
- I definitely think in data science there needs to be a professional organization that people can go and they can qualify for different positions within the spectrum of data science and data engineering. Right now, and since I've been in the field, there's been a lot of uncertainty or fuzziness and controversy about the titles people are using, where, say, companies are hiring for a data engineer, but they put the position title on their job description data scientist.
Or one person is a software engineer doing data science, building a platform, and another person is perhaps generating data visualizations and communicating, but their company is calling them both data scientists. But the magnitude of difficulty between both of their jobs is so different that there's a little bit of hostility. Or not hostility, but a little bit of animosity, which I think is fair. And I think that we just need a professional organization to say, "Okay, you have qualified.
"You have a stamp. "And you have a responsibility, "and this is where you're at." And so that there's clarity in the industry. But it's a new industry, and that's why we don't have it. Someone will have to step forward and take the effort to do 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