Join Lillian Pierson, P.E.—a leading expert in the field of big data and data science—as she shares observations and tips to help you embark on a career as a data scientist, regardless of your starting point.
- I'm Lillian Pierson. I'm a data scientist and an engineer. I'm the owner of Data-Mania. And my story is that I'm an environmental engineer who became a self-taught data scientist. I taught myself to code in Python and R, and I learned different data science methodologies. I'm going to share with you a little bit about my journey, some of the obstacles I overcame, and insights about the field. My story is that I started in environmental engineering and then I taught myself to code and I moved into data science without having to go back to school.
In the course of that progression, I did a lot of interacting with people online and I was tremendously inspired by a number of data scientists whose skills still blow me away today. And also I feel I inspired other people. So one of my bigger passions today is to engage with and interact with people who want to do data science but don't know what to do next, and just encourage them not to be worried about having to go back and get a formal four-year education, but that they can start where they're at just by perhaps learning online or picking up some books, but really just making it a decision and being dedicated.
So today I'm really excited to get to share with you a little bit about my journey and the things I overcame and also some important details you'll need to know about the field.
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