Join Lillian Pierson, P.E. for an in-depth discussion in this video What's so exciting/emerging about the field?, part of Insights on Data Science: Lillian Pierson.
- I'm really excited Internet of Things analytics, and the whole smart house, smart city, smart lives thing. Since I've gotten into the field there's been continued visibility. There's been continued coverage by the media. More and more organizations have been implementing big data projects. And so, that's making an impact, not just for the large organizations like Facebook and Google, but now down to the smaller mom and pop type businesses.
They're getting the benefits of data science in big data technologies. One exciting emergence I've been seeing over the last few years is with IoT, Internet of Things, and Internet of Things analytics. And that's really exciting for me, just from the perspective of the energy saving possibilities, and the possibilities of a more energy efficient society based on automated machine functioning. A lot of times I see people hiring for, say a data scientist, and then in the position description they describe the tasks of a data engineer.
So, they don't actually know what they're hiring for. They're mislabeling the job. That causes a lot of confusion in the industry. Then also people don't know what to pursue when they're learning, because you see this job, and it says data scientist, and then it's got all of these data engineering tasks. So, what I recommend people do when they're getting into the field is to figure out what your passion is and what you want to do, and then pursue that.
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