Join Lillian Pierson, P.E. for an in-depth discussion in this video What are the best resources for people interested in a data science career?, part of Insights on Data Science: Lillian Pierson.
- There are some great, great resources on the internet for data science. As far as data science communities, I like Data Science Central and KD Nuggets and also R Bloggers, but for people that want to get experience with data science projects, Kaggle is the place to go. As far as me and women in technology, I have found the most amazing community of Instagrammers that work in technology and are not stifled. They are fully themselves and being women and being women succeeding in tech, and I find that we really bolster each other in our confidence just by interacting with one another, and if you'd like to connect with me on Instagram and that community, my handle is @BigDataGal, and you can find most of the other ones through me.
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