As Rosie Martinez approached the end of her Doctorate of Science in Public Health, she realized her favorite part of her studies was data analysis. When she couldn’t land a data science job right away, she realized she needed to upskill and learn more modern technologies, so she enrolled at Galvanize’s Data Science Immersive bootcamp. Rosie tells us about the broad Galvanize curriculum, her project to get a computer to read messy handwriting, and how she landed a Data Science role at an adtech company! Plus read Rosie’s tips for other women entering the data science field.
What’s your background and how did it lead you to the Galvanize Data Science Immersive (DSI)?
I have a B.S. in biochemistry. During undergrad I wanted to be a physician, but shortly after graduation I realized that was not what I wanted to do. But I did want to stay in the health field so got my Master’s of Public Health at Columbia University and my Doctor of Science (Sc.D.) from Harvard, also in Public Health.
Near the end of my final degree, I was having the internal struggle of, “What am I doing with my life? Do I want to stay in academia?” As I was writing my dissertation and analyzing my data, I realized that I enjoyed the process of collecting data, understanding data, processing it, and presenting it in a way that an audience – no matter what level they're at – could understand it. That experience showed me that I wanted to go into a data science-type role. I first heard the term data science in the last year of my doctorate.
It sounds like you already had a lot of data analysis skills! Why did you need a data science bootcamp?
Throughout my education I took a lot of statistics classes which are applicable to data science, and I was basically a self-taught coder. I learned how to code in R, which is a statistics-based language. It is not as usable and versatile as Python, but it's good for statistical analyses.
When I started looking at data scientist jobs, they were asking for modeling skills like regression modeling, PCA (principal component analysis), and cluster modeling, as well as experience with unsupervised and deep learning methods, which I had no understanding of at the time. As I began interviewing, and not getting positions, I recognized that my technological education was a little bit outdated. I needed to update my resume to show that I could use these more modern and up to date techniques.
Why did you choose Galvanize Data Science Bootcamp?
I ended up at Galvanize after I moved across the country from New York City to Denver, CO. I looked at coding bootcamps online and chose a 3-month accelerated Python course at Galvanize called the Data Science Immersive to see what it was like. I liked the teaching style at Galvanize. It's fast-paced and I like learning in a quick environment. I applied, and I got in!
Did your previous data analysis experience help you with Galvanize’s application and interview process?
Galvanize has a two-pronged approach for admittance. You have to do a coding challenge in Python and SQL and then a statistical- and probability-based challenge. I definitely had to prepare for the application process because I didn’t know any Python. In terms of the statistical preparation, I could do that based on the knowledge I already had. I didn't find the process that difficult. I like those types of questions where I’m given a prompt and have to figure it out.
What were the other Galvanize students like?
I had 15 people in my cohort, including me. The age range of our cohort was 21 to over 60 years old which was cool. It allowed me to see a variety of different educational backgrounds and ages, and gave me different viewpoints from other people who maybe didn't have a degree or had 20 years of experience in another field. We had ten men and five women, which in data science is pretty good. As of right now, it's a male-driven field.
It was fun getting to know my classmates. We still hang out! We'll go to a bar together or go play board games together. We struggled through a class together, so it’s nice that we get to maintain those friendships on the other side.
What was the Galvanize learning experience like?
The typical week was a Monday to Friday, 9am to 5pm. There were four instructors. The learning process was very rigorous and so was the coursework, so you learn a lot in a short amount of time. It's a good baseline for everything in the modern data science world that you need to know. You'll touch a little bit on a lot of subjects, and if you find something particularly interesting, you can learn more outside the classroom. I had classmates who would go home and read more articles, listen to podcasts, or go to a meetup to learn more.
Every week began with an assessment based on what we learned the previous week. Most days we had two hour-long lectures and two daily assignments – one individual and one pair programming assignment. On Fridays we would work on a data set in groups of three or four, use what we had learned that week to analyze the data, then present it to the class. It was fun to play with data in a less directed way.
Every 4th week, each student had to create a capstone project based on what they had learned in the previous 3 weeks. At the final capstone showcase we presented our projects to alumni, job prospects, and the Galvanize instructors.
Did you have a favorite project that you worked on?
My favorite project was a handwriting image recognition system. I have messy handwriting and I was interested in seeing if a computer could take something that a human thinks is illegible and analyze it to return a word in text form. I fed a data set of handwritten words through a couple of neural networks, these were deep learning techniques, to see if the computer could figure out the word. With pretty good accuracy it was able to predict the handwritten words!
What sort of guidance did you get from the Galvanize career services?
The career services team gave us assignments to prepare our cover letters and resumes, and set up our LinkedIn profiles, then gave us feedback. During the program we were building our portfolios and employment resources, so that when the program finished, we were ready for job seeking. The Galvanize instructors also took us through interview-style questions, and coding challenges similar to what is required in a job interview. Luckily for me, these mock interviews helped bolster my actual interviews with my new employer!
Some people started applying for jobs during the program. The career services are also available to students up to six months after graduation, so they're there helping us every step of the way. Galvanize doesn’t guarantee a job, but they have a high job placement rate.
What was your data science job hunt like?
My job-hunting experience was unique. I graduated at the end of April and started my job in the second week of May. I am also now working as a part-time Galvanize instructor!
I was applying for jobs before and throughout the bootcamp because I wanted to have a job right when the bootcamp ended. During the program, I had a few phone interviews but I ended up landing a job that I had applied for before the bootcamp. They reached out to me while I was in the program and I went through their entire interview process during the first month and a half of bootcamp. I ended up getting a job offer from them a month before the program ended!
Congratulations! So where are you working now and what’s your role?
I work for a medium-sized adtech company in Denver that's about six years young. We focus on mobile advertisements. As a Data Scientist, I perform analyses for different departments within the company and also work to understand the business to continue to push us forward in the field in a way that is profitable and relevant. I'm working on a team with data and business intelligence analysts.
Are you using the technologies and skills you learned at Galvanize or have you had to learn a few new things?
I'm using a lot of the same technologies that I used at Galvanize. What I'm learning in my new position is more about the industry than about technologies. It's nice having a solid baseline from Galvanize. Now that I know Python and how to do all of these different analyses and models, I can focus on learning the business.
How has your Public Health background been useful in transitioning into data science?
In terms of taking what I got from my degrees, it's less about the field that my degrees were in and more about the skills I learned because of them. To me, data science is being able to present complicated, technical data in a digestible bit of information – and my education taught me how to do that.
How is your employer making sure that you and the other people in the data team are learning and growing in your jobs?
My boss and I have bi-weekly one-on-ones to talk about what we're doing at the business, and how I want to grow in my position. That's been helpful as far as the growth trajectory of my job and the business. In terms of extra learning, the company provides some tuition assistance for programs, assists me in going to conferences, or will fund an online class, which is great!
We also have two data-science-driven meetings a week to talk about what we're doing and get feedback from the rest of the data team. It's a very collaborative company, which I like. Sometimes you can get too focused on a problem and it's harder to step back and see another approach, so having those collaborative meetings helps open up that mindset.
How do you feel like you've grown as a Data Scientist so far in your job?
My definition of a Data Scientist is having the curiosity to solve problems and data analysis and communication skills. I definitely think I've gained those skills and I can now say with confidence that I'm a Data Scientist.
One of the biggest ways I've grown as a Data Scientist is being able to go into a job, know that I don't know everything, and ask questions to learn and grow in the role. Even though it's only been a few months, I've started to grow in this role.
What is the biggest challenge or roadblock on your journey to becoming a full-fledged Data Scientist?
In a way, it's going to be myself in terms of imposter syndrome and being more confident in my skills. A lot of it is has been becoming comfortable with being uncomfortable. Because that's super uncomfortable for a lot of people, but especially being in a field where everything is shifting constantly and there is always going to be something new or better. Being able to adapt and be okay with not knowing everything, knowing that what you know for now is good, and that you should always be learning is important. Getting over that hump of “I don't belong” and getting to “I do belong, and I can contribute” is the hardest to get over in data science for me.
Getting over imposter syndrome and being confident in being a Data Scientist, especially as a woman in science, was very important. If anyone is struggling with imposter syndrome, you have to remind yourself that they hired you for a reason, and even if you don't have every single skill they wanted, you are capable and know how to learn those skills.
What is it like being a woman entering the data science field?
The Galvanize campus is in a building with a lot of tech companies. Walking past the offices and seeing majority male teams, and only a handful of women, was interesting. As a minority in the field, trying to find my place has been interesting. In Denver, there are a lot of good meetups for women in data science and women in the tech field. Fostering a culture that women do belong here, and we can help each other is important. We need to grow towards being an inclusive field where anyone can contribute.
Do you think you could have landed a job as a Data Scientist without Galvanize?
I definitely think I needed Galvanize. If I wanted a job that was more academic-based or more public health-based, I could have. But because I wanted to break into the tech industry, having Galvanize on my resume, which is a well-known bootcamp in Denver, along with the skills I learned there, bolstered my ability to get a job.
Is there a big demand for Data Scientists in Denver?
I was in New York before I moved to Denver, and I was originally looking for jobs in the bigger coastal cities. Coming to Denver, I was pleasantly surprised by the market here. There is a need for Data Scientists and it's definitely growing. As companies grow to become bigger data-driven companies, they are realizing that they need to move toward data-driven business decisions, so there is more demand for Data Scientists.
How did you end up teaching with Galvanize?
Coming from an academic background, I did a lot of teaching and was a teacher's assistant and fellow. I told the lead instructor of the data science program that if they ever needed someone to teach an evening course to let me know. Some part-time instructors left recently and they asked me to jump in to teach an Intro to Data Science class. It sounded fun so I said yes!
What advice do you have for other people thinking about breaking into data science through a data science bootcamp?
I would say, go for it. There's always a lot of hesitancy and most people question whether they can afford it, want to make the switch, whether it’s worth it, and if they will be able to get a job. There are always going to be questions like that, no matter what field you want to switch into. Any bootcamp, online or in person, is going to cost money. It's about taking that leap and knowing that you're going to learn something.
For anyone who wants to go into data science, I recommend self-reflecting. Do you like to solve problems? Then try some resources online and see if this is what you want. Do you like data? Do you like talking and presenting to people? If you have 75% of those things, then go for it!
Whether or not it ends up being the right decision for you, you can at least say that you did it and be confident in your decision. For me, diving right in worked out and I know for most of my cohort it did too.