Insights on Data Science: Lillian Pierson

Insights on Data Science: Lillian Pierson

Data science is a rapidly expanding field offering a wealth of possibilities for viewing the world around us through a more accurate lens. But for many of those whose imagination is sparked by big data—but who have already started pursuing a career in another field—the dream of becoming a data scientist can feel far-fetched. Lillian Pierson, P.E.—a leading expert in the field of big data and data science—aims to prove that notion wrong. In this course, she shares observations and tips to help you embark on a career in this exciting field, regardless of your starting point.

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.

Topics include:

  • 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


  • 英文名称:Insights on Data Science: Lillian Pierson
  • 时长:23分51秒
  • 字幕:英语


  1. Welcome
  2. What is your educational and professional background?
  3. How did you make your way into data science?
  4. Why are you passionate about data science?
  5. What's the most practical way to acquire the skills and experience needed to become a data scientist
  6. What challenges should people be prepared to encounter?
  7. What educational backgrounds lend themselves well to becoming a data scientist?
  8. What best practices do you recommend for landing a great job in data science?
  9. Do you have any project management advice for analytics professionals?
  10. What advice do you have about diversifying the workforce in tech?
  11. What advice would you give to women within tech, specifically?
  12. What is the biggest obstacle you've overcome along the way?
  13. What are the best resources for people interested in a data science career?
  14. What is happening in data science globally?
  15. What's so exciting/emerging about the field?
  16. How has the industry changed in recent years?
  17. What do you think are the greatest areas of need in data science?
  18. What are some common misconceptions in the field?