Burnt out from teaching high school math and science for four years, Joanne Jordan decided to search for a new career direction. After discovering the potential of Machine Learning at a startup, she enrolled in Lambda School’s online Data Science Track. Joanne tells us how the vibrant online community kept her motivated to keep studying, how she built her capstone project analyzing health and environmental hazards in NYC, and how Lambda School invited her back to teach the next cohort of students while she figures out the next steps in her data science career!
What is your background and experience before enrolling in Lambda School’s Data Science Track?
Before taking the bootcamp, I had studied Applied Physics in undergrad and then got into teaching. I taught at a charter school in Brooklyn and over four years, I taught nine different courses in math and science ranging from algebra to calculus, as well as statistics, chemistry, physics, and computer science. I loved teaching, but it was a lot of work and I got burnt out, so I took a step back and did freelance tutoring while I figured out what I wanted to do next.
I took a role as a temporary content writer for a startup SAT prep company called PrepScholar. They used Machine Learning to analyze the questions from different standardized tests and used those models to analyze student responses. They then create an individualized curriculum for each student to maximize their time and gain the most amount of points on their next SAT test. I was impressed with how powerful Machine Learning and data science can be, so I started looking into coding bootcamps as well as graduate school programs.
What stood out to you about Lambda School’s online Data Science program?
I kind of stumbled upon the program and I think I got really lucky in choosing it. Lambda School stood out to me because it was a remote bootcamp, but taught live. I need human interaction so the live classroom aspect was really appealing to me. Because it was online, I didn’t have to stay in one place – while I was studying I moved from New York, back home to Los Angeles.
I also liked their income share agreement because the school is incentivized by my success – I didn’t have to pay until I found a job, so their fate was tied to my ability to get a job after I graduated.
What were the Lambda School application and interview processes like?
Before I was admitted, I had to do Lambda's free two-week data science precourse covering the basics on Python, statistics, and other concepts, then pass a couple of test assignments to be considered for the program. Then, there was a 30-minute interview with a school representative asking about my background and why I wanted to do the bootcamp. There was a question in the application about my background in math and coding, but it wasn’t a requirement for admission.
What was your cohort like?
There were around 25 of us in the cohort, which was a perfect size. We had a male majority (but not overwhelmingly), there was a good amount of racial and ethnic diversity, and huge diversity in background experience. Because of the intro course, there were people coming in with no programming experience. Some had studied math, some came from programming, and some didn’t have experience in either but because they passed the precourse, they were able to keep up throughout the program. It was a really big mix and Lambda did a great job of attending to the different needs of the students. In all of the assignments, there are basics and then more in-depth concepts, as well as stretch goals for those who had come in with more knowledge.
What was the remote classroom experience like and how was the material taught?
I was in the first data science cohort, covering topics in Artificial Intelligence, Machine Learning, and Data Science. Every morning we had a mini coding challenge to get us into the mindset and then there was a two-hour lecture on the topic of the day. The instructor shared their screen live through Zoom and the students simultaneously chatted together on Slack. The instructor could see our questions and we also were able to help each other. After a lunch break, we received an assignment and were encouraged to ask questions on Slack or make our own Zoom conference to pair program with another student.
Each of us was also on a team of about 6 to 8 students and after everything was done, we’d connect to discuss how our days went, review anything we got stuck on, and help each other out to close the day. Our teams were really tight, so I never felt like it was remote – I have friends from the program! We’d fill out a form giving feedback about the lecture and the material, which the instructor would then use the next day to address any problem issues or clarify any questions.
Did you have a favorite project that you worked on while you were studying with Lambda?
We had two main projects: a two-week personal project and a four-week group project. The group project was a simulation of a Lambda client interaction, so they gave us an objective and we had to go find the supporting data. It was fun to work as a team and provide a completed deliverable. For the personal project, we could choose anything we wanted. We had to create a proposal and discuss the feasibility with the TAs. I’m a teacher, so naturally I’m interested in education and public health, and the related sociological elements. I decided to do my project through Small Area Analysis, where you analyze the data of small communities within a larger area. I looked at New York City’s neighborhoods and compared health and environmental hazard data alongside education attainment data. It was a lot of fun to combine my skills to do something that I was interested in and passionate about.
How did your applied physics and education background help you during the data science bootcamp?
Having a strong math foundation definitely helped me understand some of the concepts, and my experience working with people and knowing when to ask for help was one of the main factors of success in the bootcamp. It was really helpful to be part of a team where I could go and ask someone when I was stuck on something. Having a collaborative environment was similar to teaching since I was both a general education and special education teacher in an inclusive classroom environment. I would have to collaborate with the other teacher in the room and would ask senior teachers for help if I needed help figuring out something or solving a problem with a student. Those were skills I was able to bring into the data science bootcamp to help me succeed.
How were you able to stay focused while studying remotely? Do you have any advice for others doing a remote program?
I think that participating in the community was really important. It was really integral for me to learn with others because I had found I wasn’t able to teach myself the concepts alone. I took advantage of the live, interactive lectures, and knowing that at the end of the day I was going to be accountable to our instructors, the TA, and the team, kept me focused on completing the assignment. I think the community feel of Lambda School is fantastic – there are certain parts that you’re required to participate in, but the more you take advantage of it, the more you’ll succeed.
Congrats on your role as a Teacher Assistant at Lambda School! How did you land the position?
Lambda School is a really great environment and I’m really enjoying being back as a Teacher Assistant (TA). Initially, they asked me to be a TA for a part-time evening program while I was still a student in the bootcamp because I was performing well. After I graduated, I was working on some projects and trying to figure out what I wanted to do next when the head data science instructor asked me if I wanted to come back and be a TA for the second Data Science cohort. I was still connected to the community through career coaching, so returning to the learning side sounded fun!
What does your role as a Lambda School Teacher Assistant involve?
As a TA, I attend the lectures with the students and monitor Slack throughout the day to see if they have any questions. I also hold office hours in the afternoon through an open Zoom link and students can pop in any time and ask questions. I’m also assigned to a team, so I lead the meetings at the end of the day, and then grade and give feedback on their assignments.
Having been a teacher at the high school level, I can see similarities in the way I work with and manage a group of students with different skill levels. Some students get frustrated with the material and some are very advanced and work through things quickly. I try to note the high performing students and give them challenges to raise their skill level.
How does Lambda School prepare students for job hunting?
Career coaching occurs once a week starting about a third of the way through the program. Career coaches give lectures on basics like writing resumes and cover letters, setting up LinkedIn, organizing portfolios, and networking. Students are assigned to a specific career coach who is connected to the business partnerships and they also are assigned to a team with an instructor whom they can ask for more technical questions and advice, or to read over a final submission for a job interview. Lambda School also has a program called Lambda Next, an opt-in, full-time program (and free if you’re a student) which has assignments geared towards helping you build your portfolio and network, to help you figure out your next steps.
What are your next steps? Are you planning on staying on as a Lambda School TA or will you do something different?
The TA position is intended to be temporary for Lambda grads so I’m looking at data science roles in fields related to education and public health or healthcare as my ideal next step. I’m also considering coming back to Lambda School as an instructor some day. They really value instructors who have industry experience, so I want to go out and get some real world experience and come back to Lambda better equipped to help the next group of Data Science bootcampers.