Lisa was a middle school math teacher for 20 years, but was ready for a change. After completing the data science bootcamp at Flatiron School, Lisa is weeks away from starting her new career as data analyst II with a highly-respected Fortune 250 company.
Read this interview with Lisa — who calls herself a “midlife career-changer” — about how she made the switch.
What made you decide to attend a data science bootcamp?
I knew that I wanted to change careers, but I also knew that I needed to find something that I was excited about, rather than simply running away from my former career. Data science intrigued me with its blend of math and creativity, but I thought that it would require me to get (another) Master’s degree, which I did not want to spend the time or money on. I also discounted my ability to successfully move into data science because I had zero experience with coding and my computer science knowledge was out of date (to say the least!).
My transition started one Saturday morning when I saw an ad for Flatiron’s Data Science program. I clicked on the link and within the hour, started the free bootcamp lessons. Although I didn’t make it too far before it became too difficult for me, it clarified a few things for me — this content excites me. I want to learn more. And I have the ability to learn how to do this.
Within weeks, I had a plan — continue teaching for another year and a half while learning Python, SQL, and some machine learning concepts on the side before starting a full-time data science bootcamp program. It was a risk leaving a successful career and guaranteed income for the unknown, so I wanted to make sure that I was as prepared as possible.
How did you choose Flatiron School?
I researched different bootcamps and talked to people that had completed various programs, and Flatiron School stood out because the program curriculum — and the admissions process — was more rigorous than many others.
Flatiron School builds labs that require that you’re an active participant in your learning. Does this mean that it’s difficult at times? Absolutely. Does this mean that you have to seek out other resources to clarify concepts or get extra practice? Yes. But you get out what you put in.
Why Data Science?
Data science is a field that is constantly changing and if you want to be a part of it, you have to become comfortable with struggle and with figuring things out. I love the fact that I get to question data for a living. This is a career that is not only in-demand, but also has so many areas that you can specialize in and never-ending room for growth.
If all you want is to get a “completed” stamp on a data science program, there are easier ways to do it. But if you want the opportunity to really understand the material and create independent projects that prove to prospective employers that you can do it, Flatiron School may be a great fit for you.
Name one thing you wish you knew before starting Flatiron School.
I’m SO grateful that I spent as much time preparing as I did. I went from no experience with coding to being intermediate-ish with Python. I became familiar with SQL, and I did some of those free and cheap “pre-chewed” self-paced data science programs to gain some familiarity with machine learning concepts. All of that meant that I wasn’t overwhelmed at the beginning of my data science bootcamp, and I was able to dig deeper into concepts since I already had a cursory understanding.
I wish that I had believed in myself more from the beginning. As a mid-life, career-changing female with a non-technical background, I was dealing with major imposter syndrome. My instructor — shout out to Claude! — had to spend more time convincing me that I could do this than he did actually teaching me concepts.
I won’t pretend that it was easy, but with consistent effort and engagement, it was never too much. I cannot emphasize the importance of engagement enough. To paraphrase what I always told my students, “Data science is not a spectator sport.” If you want to understand it, you have to be active in the program, attending sessions and asking questions.
What advice would you give someone else who wants to change careers?
- Make sure, whatever discipline you choose, that you love it. That passion will make the effort of learning it worthwhile.
- Don’t be intimidated. Nobody knows everything in this industry — even though some of the loudest voices may want you to think that!
- Learn how to seek out information. Google is as important as Python.
- Focus on the learning, not on clicking the “finished” box on each lesson. No potential employer will care that you checked lessons off your list; they care about what you know and what you can do.
- When you approach job-hunting, look at what technologies keep coming up in job descriptions that you’re interested in and dedicate some of your time towards learning those. I spent some time after graduation (and while applying for jobs) working on Tableau, getting better at SQL, and learning some cloud computing concepts.
- Be committed to becoming a lifelong learner.
Finally, enjoy the process!