Join Lillian Pierson, P.E. for an in-depth discussion in this video What educational backgrounds lend themselves well to becoming a data scientist?, part of Insights on Data Science: Lillian Pierson.
- Most data science position descriptions, they're hoping they can get statisticians or computer scientists, and that makes total sense, but also, engineers make great data scientists, and it's not too far of a leap. Some engineers don't have the coding experience or perhaps the statistical know-how that they need to do data science, but it's not too much further out. Even people that have backgrounds in finance and anyone that's got a heavy focus in quantitative analysis as part of their main course of study, or main tasks at work, they're going to be a step ahead.
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