Join Lillian Pierson, P.E. for an in-depth discussion in this video How did you make your way into data science?, part of Insights on Data Science: Lillian Pierson.
- I started off as a teenager doing advanced research in chemistry, and we were deuterating RNA and DNA molecules, and I had to work based on the data insights I would get from a NMR machine, because I didn't have the working knowledge. I didn't have the advanced background in chemistry. And I've always since then been cognizant of the power of data insights. Later in school I got into geographic information systems. And then as an environmental engineer, most of my first tasks were using statistics to validate results.
And as I moved further in my career, I just gravitated more towards the modeling and the analysis type of tasks and less interested in doing design work. And so it was a natural transition from there. I find that all throughout my career I've been doing tasks that fall into the purview of data science, but it wasn't until I discovered that that's actually a field in and of itself that I really got passionate and interested in doing data science as a career.
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