Join Lillian Pierson, P.E. for an in-depth discussion in this video What's the most practical way to acquire the skills and experience needed to become a data scientist, part of Insights on Data Science: Lillian Pierson.
- The best path to take to get into data science depends a lot on your background, but if you have a quantitative background, it shouldn't be too hard to make that transition. You can just take some online courses and then so long as you have some coding skills in object oriented languages like Python or R, once you learn the data science methodologies, you can start working on implementing those in your professional life or even in little hobby projects you create after work.
That was how I made my transition was I got my employer, I got them to get me training that helped me perform my job and then I just started implementing that in work and that was an efficient way of learning.
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