Join Lillian Pierson, P.E. for an in-depth discussion in this video Why are you passionate about data science?, part of Insights on Data Science: Lillian Pierson.
- I'm passionate about data science because of the power that data insights impart. When I was 17 years old, I did advanced chemistry research without having taken any classes in organic chemistry. We used NMR, NMR machines to get back information about the positions of atoms in DNA molecules, and based on that information, that data, I was able to do exchange reactions of hydrogen atoms on DNA molecules based on a trial and error approach using the data insights I got from this machine and then moving forward, I studied geographic information systems, and looking at location-based data, there's so much power in the data insights that are available to us based on location.
Later when I got into my career, they had me doing statistics to verify and to validate that they actually got tangible results from their projects and it's just always been to me, data has equaled power, and so I'm passionate about data science because of the power it imparts.
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