Join Lillian Pierson, P.E. for an in-depth discussion in this video Do you have any project management advice for analytics professionals?, part of Insights on Data Science: Lillian Pierson.
- The project management advice I have for analytics professionals is to always evaluate your solution alternatives before making a start on a project. Mostly with data science projects and analytics projects, you're going to get someone's going to come to you with a problem to solve. And the first inclination is always to just start solving it, but without a thorough examination of what issues could come up or what's the most efficient way to solve it or how this could affect future projects downstream, you could put yourself in a position of wishing you had done a different approach and perhaps having to redo your project, so it's always, always, always important to sit down and analyze the problem and the potential solutions before choosing an approach.
I got my project management approach from the engineering field, because in engineering you have to do alternatives analysis. It's part of any good technical report that you explored what alternative technologies are available, what are the costs of those, and support your suggestions, your recommendations.
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