Former aerospace engineer Anterra discovered a love for data after teaching herself Python. Anterra shares why she pivoted from self-teaching to enrolling in the online Data Science bootcamp at Metis, and how diligent self motivation was key to building the foundation she needed to begin her data science journey. From hands-on project work to career services and confidence-building, Anterra explains how Metis gave her the experience she needed to land her first data scientist role at CarMax and answers our question: do you have to be a rocket scientist to get into a data science bootcamp?!
What inspired you to break into data science in 2020?
I have a background in physics and math, and after college I worked for a company that did small business innovation research for NASA. It began as an internship that turned into full-time employment as an Aerospace Engineer. It was exciting to take part in such a meaningful project! What I loved about the role was the innovation, data analysis, research, and writing, but I wasn’t as fulfilled by the mechanical engineering work. I stepped back from the role and began working as an optician, earned a yoga teaching certification, and taught myself Python. I fell in love with Python because it reminded me of complex math problems where there is a certain beauty in creating a solution. With Python, it went beyond an equation because I could watch it run and function. I discovered data science as a career option and realized that it was the perfect synthesis of my background in research and problem-solving and it was a toolset I could apply to anything. Working in data science, I wouldn’t have to pigeonhole myself.
There are so many data science bootcamps now — why did you choose Metis?
The curriculum at Metis really stood out. Originally, I was planning to self-teach in order to launch my data science career. Based on my own research, I laid out a curriculum for myself that I thought would be effective. When I discovered the Metis Data Science Bootcamp, it's curriculum included everything that I had identified plus so much more! I realized that the robust nature of their program, taught at an accelerated pace, by quality instructors would get me where I wanted to be faster. I would also learn far more tools than if I learned on my own.
What was the Metis application and interview process like for you?
The application process began with a letter of intent about why I wanted to pursue a career in data science. After that, it moved into a technical round that involved Python coding challenges. They weren't terribly hard, but they required a decent understanding of how to code with Python. Metis expects a certain level of proficiency so that when you begin the program, you can hit the ground running with data science methods, techniques, and projects. I also had a video interview with an alumnus who asked me probability and statistical math questions and wanted to see how I thought through projects out loud. During this call, I had to pitch my idea for a final project and we talked through the details of where I would get the data and what my approach would be.
You were an Aerospace Engineer – do you need to be a rocket scientist to get into Metis?
You don't need to be a rocket scientist to get in! Metis accepts people from all different backgrounds, and so many unique ways of thinking can translate to being an excellent data scientist. You should do some work beforehand to develop base level skills around Python and mathematics, especially probability and statistics. Metis has a free Admissions Prep course with learning modules to help you prepare, and there is a Beginner Python and Math for Data Science and an Introduction to Data Science prep courses, which help get you up to speed before the bootcamp begins.
Did you receive any scholarships from Metis?
Since Metis seeks to diversify the data science field, I received The Metis Scholarship, which is offered to women, members of underrepresented demographic groups, members of the LGBTQ community, and veterans or members of the U.S. military.
What was a typical day like during the remote Data Science bootcamp at Metis?
I spent the first hour of every day doing pair programming. It emulated a collaborative work environment, and helped prepare me for interviewing because I encountered new unseen problems, even in a remote setting. After that, we had lectures for a couple of hours. The teacher would either be running through a notebook with coding examples or slides with information. Then, we would have the afternoon to plan and work on our projects. Often, we would have standups or meet with instructors one-on-one to receive additional help.
Did the Metis teaching style match your learning style?
It absolutely did. A bootcamp is unique because it is extremely hands-on and project-based. Because it is fast paced, a bootcamp focuses on practical application. I learn best by doing, and that's what Metis embodies. Rather than passively absorbing information like in traditional higher education, you are putting your learning into practice immediately and applying your lessons to projects. The bootcamp model really works for someone like me who enjoys being hands-on and learning actively. That's why I thrived and became proficient quickly.
I also had an advantage learning remotely. I could keep open a split screen and watch the lecture on one side and on the other side, run through the code. For me, it helped to solidify those concepts.
Did you choose to specialize in a particular data science path during Metis?
There are so many paths within the data science field, and the fast pace of the curriculum introduced us to so many subjects. Natural language processing is extremely interesting to me and I applied it to a couple of my projects. Deep learning is another exciting subject that can be paired with natural language processing.
Since you were learning remotely, how did you interact with your peers?
Our cohort was close. Going through such an intense program together created bonds. We often studied and did projects during late night Zoom hangouts. I saw everyone in class during lectures, paired programming, and projects. It was a collaborative and supportive environment. Even now, my cohort meets regularly to catch up.
Was your cohort diverse in age, gender, and/or race?
Yes, across all of those metrics. It was nice to see how everyone's various backgrounds came through in their projects. Data science is so diverse that you can apply it to anything. People coming from so many diverse backgrounds take their passions, interests, educations, and former careers and combine them with data science.
What kinds of data science projects did you build at Metis?
When I graduated from Metis, I had a portfolio of data science and machine learning projects that I had created. The first was a group project that involved simple data analysis and data exploration. It was a great time to have a group project because I was so new to the world of data science that it helped to be able to collaborate at that stage and reinforce understanding. As the curriculum moved forward, we progressed through increasingly difficult levels of data science techniques and there was a project assigned around each learning module. We could pick our topics and apply different techniques for each project. One project was using linear regression, the next was a classification problem, unsupervised learning, and natural language processing.
What did you build for your passion project?
For the passion project, we could build anything that we wanted. I built an app that creates AI-generated yoga classes, which I chose deep learning paired with natural language processing to create. I used web scraping techniques to collect yoga classes created by real yoga teachers all over the world. Then I used natural language processing techniques to pre-process the data, which I fed to a bi-directional LSTM recurrent neural network which is ideal for sequence prediction and text generation. Once it was trained on that, I had it generate new yoga classes that could be either taken by students or used by yoga teachers as a reference for their own classes. I used the Unity game engine to render out an example of a front end to show what the custom-built class looks like. It's a passion project that I intend to continue to build on, and hopefully I will create a real app one day.
Since you learned remotely, did you present at a remote Demo Day?
Instead of the Demo Day, we recorded ourselves presenting our final project, which was then added to Metis’ Graduate Directory. The video presentation highlighted exactly what I had learned at the bootcamp and the work I had done. I was able to share this with potential employers and link to it on my LinkedIn profile.
How did Metis prepare you for the job hunt?
The support from the Metis careers team was exceedingly helpful. My career advisor walked me through what an interviewer is looking for, like how they want to learn how I think more than they want me to get the right answer to a technical question. This coaching shaped how I went into my interviews. During my interviews, I felt confident to ask questions, walk through my thought process out loud, and ask for guidance when I didn't know the answer.
I really liked the Round Robin Day that Metis hosted. We went through mock interviews in a high pressure environment that focused on SQL questions, background discussions, and data science methodology. Beyond the career coaching, the type of work that we did during the bootcamp and working with people through hard problems was great interview prep!
What data science roles did you feel qualified to apply for after graduating?
I felt qualified to apply for Data Scientist and Data Analyst roles.
What is your advice for making the most out of the Metis experience?
There is so much to take in, so don't let yourself be overwhelmed by the information. You will always be able to go back and deep dive into those lectures again. Focus on your own projects and what you can do well.
Congratulations on your new job as a Data Scientist at CarMax! How did you get the job?
I discovered the job posting for a Senior Data Scientist role at CarMax on LinkedIn, and I applied. After applying to the position, I sent a personal follow-up message directly to the recruiter. Even though I wasn't necessarily at senior level, CarMax decided to hire me as a data scientist to let me continue to build my skills with the expectation that I could train into that senior role they wanted to fill. My experience, portfolio, communication and enthusiasm spoke for itself.
What was the remote interview process like for you?
The interview process at CarMax was in several stages. There was an initial phone screening with a recruiter followed by a data analysis-based online technical challenge. Next, I had a phone interview with a Senior Data Scientist who spoke to me about my experience, background, future goals, and a deep dive into the classification project I built at Metis. They asked me why I made certain analytical decisions and my results. The final round was an all day series of video interviews that included more in-depth data science case studies and analytical challenges.
What are you working on at CarMax? Do you work with a specific team?
I have the pleasure of working within a strong network of teams that all have different specializations and exceptional leaders, including my direct manager who is a female data scientist. I work on a data science team within the marketing department, which places us within a broader analyst group in the company. My team is made up of talented, diverse engineers, and we focus on personalization and recommendations. We specialize in personalizing the user experience to help customers discover relevant vehicles.
It’s so cool that your manager is a woman! Do you have any advice for women and other underrepresented folks in tech who are transitioning into data science?
We are at a cultural moment right now that wants to prioritize creating equality. Hiring managers are very aware of inclusivity and representation. Now more than ever is the perfect moment to jump in and gain all the scope that you can, and leverage the fact that culturally, we are pushing in that direction. There is fertile ground that supports women and those from underrepresented groups to launch their data science careers now.
In your job at CarMax, are you using what you learned at Metis?
I am absolutely using what I learned at Metis. In the granular sense, I’m putting into practice all the languages, Python packages, and modeling approaches. The autonomy and collaboration of my role lets me focus on critical analysis and strategizing, which I feel Metis did a good job of nurturing. Metis wanted us to be thoughtful about how we were approaching problems, and I am definitely applying that mentality.
At CarMax, I get to present my work internally to various stakeholders (from technical to nontechnical people) with regular cadence. The ability to communicate effectively is one thing that I developed at Metis that I'm really proud of. We had presentations for all five of our Metis projects and we always received feedback on how to be a more effective communicator. I am now able to put that into practice at CarMax.
Do you have tips for other data science bootcamp grads who are on the job hunt now?
If you’re on the job hunt now, I highly recommend reaching out to recruiters and hiring managers. Being able to tie your cold application to an actual person who is expressing interest often goes that extra mile.
Before you joined CarMax, you were a data science teaching assistant (TA) at Metis. How did your teaching experience help you become a better data scientist?
Being a TA is an exceptionally rewarding experience. I believe you can learn by teaching and talking through your understanding of things. As a TA, I could step back and start thinking through the why behind the methods and techniques and share those concepts with students. I could explore ideas and solidify those concepts in my mind. When students were working on their projects, I helped them refine their ideas and determine their scope, giving them the support they needed to navigate their projects, which echoes the work of a more senior level data scientist, helping to guide strategy and vision of others’ projects.
What has been the biggest challenge throughout your career change?
From day one, Metis informed us about imposter syndrome. Being able to maintain belief in yourself and be an advocate for yourself was an ongoing challenge in the bootcamp and afterwards. My recommendation to those thinking about making a career change is to first be your own biggest fan. Speak to your strengths. When you come out of the other side of Metis, represent yourself as confidently as possible and it will set you apart.
Was Metis worth it for you?
Metis was definitely worth it. I considered teaching myself data science, but I would not be at the level I am now, have such a high earning potential, or land the job I did if I didn't graduate from Metis.
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