Makenzie Wells has quite the impressive background, made up of math, engineering, and a diverse set of transferable skills. While working in a startup, she moved from a role in customer success to a position in business development, trying to find new ways of leveraging data to improve her company’s product. To learn how to do this, she enrolled in the Practicum Data Science program, which opened the door to her new profession. Now, she's leading technical operations at an EdTech company and plans to start a nonprofit for children with poor internet access. Here's how Practicum supported and enabled her career transition.
The missing piece
Makenzie's no stranger to wearing many hats to get the job done. Having degrees in both math and engineering, she originally managed the global customer success team at a healthcare startup before later switching to the business development side. Startups rarely limit you to one role, so Makenzie found herself seeking out new ways to use company data. "The idea was to use the data to find if there were things about a particular user that we could use to help them determine good patterns of behavior," she explains.
With her tech background and experience writing small programs, Makenzie knew she could use data science (DS) methods for this task. However, she didn't understand how to apply DS in practice. "I had a sense of data science. I knew how to program in R, but I didn't know the theory behind algorithmic building and how that worked."
Makenzie started looking for places to learn more and spotted a Practicum ad in one of the media channels from Women Who Code. It was important that she have access to experts who knew the material, so Practicum's community of tutors and peers, as well as their 1-on-1 meetings and live sessions, set it apart from many other online programs. Also, knowing in advance that learning would require about 20 hours a week was helpful, as Makenzie had to plan around her day job. All things considered, she decided to give Practicum a shot.
Building upon previous experience
The Data Science program consisted of several two- to three week-long sprints. These included theory, practical tasks, and project assignments. "It went really well," Makenzie summarizes, though she admits she had some struggles as the topics got more theoretical. She started having to apply the theory to practice, with fewer and fewer examples as the lessons grew more complex. Fortunately, the assignments were always checked by code reviewers. She could also leverage various internal and external coding resources to accomplish the tasks. "By using online resources, like Stack Overflow, I was able to connect those dots," she mentions, "[and] I found the reviewer's commentary very helpful."
The programming languages Makenzie learned in college also served her well when she needed to understand code syntax. Once she started working after college, she never wrote code herself or fixed bugs. Still, she was reading code often and could understand what a particular line of code was supposed to do.
Makenzie's math and engineering knowledge sped up her learning as well. She already knew the math behind logarithmic and linear items, so those concepts were easier to grasp. She completed the first sprints on weekends using her baseline understanding of material, and her math background helped her get through the later ones. She didn't need to go back and look up what an algorithm was or how to work with statistics, she explains. "I think I've definitely had an [advantage] in that way, especially in the earlier lessons. And I think I helped five or six people on the side over Slack with things that they were encountering too."
Her advice for newbies is to be ready for growth and not be afraid of doing something unfamiliar. "Having a growth mindset is really the primary thing that you need in order to make any sort of conversion. And being ready for the unknown — you're not going to know a whole lot in the beginning, but there are so many resources."
Skills in action
Soon, however, things got harder for Makenzie to juggle. In the middle of the program, she landed a new job. She joined the team during their busiest season of the year and there were a lot of new things to learn. After deciding to take an academic hiatus for a month, Makenzie was able to pick up where she left off at no additional cost.
Getting the job was very much a coincidence. She had been casually looking for a while and noticed a posting in one of the professional online communities she was a member of. The company was already in the final stages with another candidate, but Makenzie did her best to show them her skills and potential, including brushing up on Ruby at night for the interviews.
And it worked: she got hired as a Success Engineer at Remind! The company is building a communication app for parents, teachers, and students to stay in touch with what’s happening in the classroom. When she started, she was fixing bugs and doing data cleanups, but now, a year later, she's leading technical operations and serving as a "bridge" between the business and technical parts of the team. If a question or a problem emerges when her company is setting up or training a client, it gets redirected to her department. "Either it's something support can help them with, or it's something that a sustaining engineer needs to go in and clean up, correct, or solve the root cause for. Sometimes, a program manager needs to think about a project and gather some data on how this would be beneficial to the larger customer base," she explains.
Hard skills are not the only thing she learned at Practicum, though. For instance, she was able to explain to her managers how different data can be leveraged in their product and what they should be looking for. "At this point, it's helping those folks set the base layer of what they should be thinking about now, so that we have the right features to use later when we want a project to predict or analyze something."
Charting a new road
Unlike many tech newbies, Makenzie has a well thought-out plan for her future. In the next few years, she wants to move to doing more engineering tasks and become an Engineering Manager or Head of Technology. Her diverse background will help her find novel approaches to solving various problems.
"The skills I've acquired over the course of my life and career all fit into a toolbox that's pretty unique to other places. When faced with a problem, I can tap into each of those skill sets, so that I can see it more cross functionally or from a higher level. It also puts me in a good position—or at least I think it does—as a leader and a manager to help other folks understand how to pull those skills from their background and see how they can apply them to the here and now."
Long term, though, Makenzie would like to focus on providing opportunities for other people. She wants to set up a nonprofit in her small hometown in East Tennessee, which she says still struggles with internet access. "I want to go back there and set up a resource forum for the kids to showcase what you can do with technology and engineering. [I want to show that] the farms, the factories, the gas stations, and the salons that you see everyday are great opportunities for employment, and there are so many other options that you may be interested in but don’t have access to. These options may even help improve the operations in this small town. [I’d like] to open that door."
Has Makenzie's story inspired you to pursue a career in data science? Start learning for free with us!