Join Lillian Pierson, P.E. for an in-depth discussion in this video What challenges should people be prepared to encounter?, part of Insights on Data Science: Lillian Pierson.
- The thing is you have to have the mindset to work in data science so it can be sometimes tedious, and you need a lot of commitment and perseverance. A lot of times in project development there will be changes, or perhaps some component of your system will break. And then you've got other components built on top of it. And you'll have to go back through in either case and make changes that effect portions of the work that are built on top of it as well so you'll have to go make changes all throughout the project and it can get kind of tedious and it takes a lot of perseverance and also a mind for details.
You know, if you're deciding whether or not you want to pursue a career in data science it's a good idea to keep in mind that it's highly quantitative, you have to have an analytical mind and love doing that type of work. And also you need to have a strong sense of perseverance that you're going to stick through something and get through it no matter what. So what I did personally was, well, two things. I did volunteer work for a group called Standby Task Force. And so I worked on data science implementation projects for humanitarian efforts and humanitarian response.
And that got me some extra experience outside of work. And then also I built little hobby projects and I put them on my website. And I also started writing articles to kind of get the word out about different things that were happening in data science. And so I basically just made my career pursuits my hobby. And I worked after work. They could see in my performance that I was definitely headed in the right direction, and then what I did was I just made sure to stay two steps ahead.
And so when we're in meetings I'd say, "Well you know, you "have these alternatives open to you. We could solve this "problem this way, this way, this way. And here's what "we need to do that." And so they saw that I was really dedicated to doing well in my job. And so when I told them if I get this training, I'm going to be able to get these results for you, they were more than happy to make the small investment.
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