Join Lillian Pierson, P.E. for an in-depth discussion in this video What are some common misconceptions in the field?, part of Insights on Data Science: Lillian Pierson.
- There are a few misconceptions in this field. One of them is a lot of companies are building applications to try and it seems like replace the data scientist, thinking we can build an application that will do what a data scientist does. And I think that that's not possible, because it's not just coding. A data scientist also has subject matter expertise and the mathematical and statistical know-how to understand and interpret the insights.
So if you build a computer application that generates the insights for someone, that doesn't mean that they're going to understand the implications of what that data is telling them or how to use that information to affect a business. Another common misconception I have heard about is something called a citizen data scientist, and I'm pretty sure that term is referring to saying that everyone is a data scientist because everyone is interacting and creating data and using data.
And while that's true, data science involves application of analytical methods and subject matter expertise, knowing how to apply data insights to a business. And so not every citizen is a data scientist.
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