Suchaya Rojanasuwan had the drive and the passion to work in data science, but wasn’t sure how to make the career change. Suchaya shares how the Data Science Immersive bootcamp at Galvanize taught her the necessary data science languages, soft skills, and career support to land her first job as an Associate Data Science Engineer at Gap! Plus, Suchaya explains how she’s using her newfound data knowledge for social good by contributing to a directory of Black-owned businesses around the country!
What were you doing before you attended Galvanize?
I went to school for Economics and I found my econometrics class really interesting. I enjoyed using data to forecast economic patterns. I interned with SDSU Research Foundation where I worked as a Data Management Analyst. I enjoyed my role, helping to expedite data entry and data verification, and it became my career goal to work in the field. After I graduated from college, I decided to use online classes and part-time courses to pursue my passion to become a data professional. Prior to Galvanize, I co-founded a small business with my family and worked there for about a year. After that I worked as a Project Coordinator at an engineering construction company. While working at both jobs, I was able to facilitate and improve my work using data analysis.
There are so many data science bootcamps now — why did you choose Galvanize?
I was taking online classes and I wanted to be fully immersed in the career scene. I have a friend who attended one of the bootcamps at Galvanize, and he said it was the best time of his life. That was my first impression of Galvanize, and when I researched it more, I felt that Galvanize was well prepared to help their students get into the data science field. Galvanize also had financial options and opportunities that appealed to me. Their scholarships were especially attractive.
Did you receive a scholarship to help cover the tuition?
Yes, I earned the Galvanize Scholarship, which is a full-tuition scholarship from Galvanize! To be considered for the scholarship, there was an application with five questions. It also required that I make a short video where I had to teach a concept. Since I'm a native Thai speaker, I taught how to compliment people in Thai language. I thought it would be the best way to show off my personality!
What was the Galvanize application and interview process like for you? Was it hard to get in?
After I scheduled my interview, there was a Python and statistics test. Even though I’ve worked with data, the test was hard for me because I wasn't familiar with Python. Taking Galvanize’s data science prep course helped prepare me to pass the interview coding challenge. I recommend taking the prep course before the interview! My background in economics helped me to pass the statistics section.
What was a typical day like learning data science at Galvanize?
We met from 8am to 5pm everyday. We would have two lectures per day, and after each lecture there was a review exercise. On Fridays, we worked in groups on case studies. From 10am to 4pm, we worked on our project and then had to present it when time ran out. It was intense but a great experience.
Did Galvanize’s teaching style match your learning style?
Galvanize incorporated hands-on learning, which was very helpful for me. We were expected to figure out problems through our own research first, but if we could not, the instructors were there to explain it further. Galvanize focuses on teaching us to be self-sufficient, and they taught us how to learn on our own.
What did Galvanize’s data science curriculum cover?
Galvanize taught a variety of data science concepts such as statistics, big data, machine learning, and different type of models, including supervised, unsupervised, and reinforcement learning. My favorite one would be Natural Language Processing (NLP). Python was the main language we used, and Galvanize gave us an introduction to SQL as well as a dynamic SQL. We were also introduced to big data tools and platforms like Spark and AWS.
What kinds of projects did you work on at Galvanize?
My favorite project was Fake or Fact, a Sephora fake review detector that I built. I am an active user of Sephora and wanted to be able to detect when makeup brands hired people to write positive reviews for them. I started by looking at Makeup Alley, another review website with a great reputation. I treated that as a trustworthy data source. I did natural language processing to choose what words were used more. Honest reviews had a tendency to provide both pros and cons. On Sephora, when too many positive adjectives are used, it seems suspicious, so I created a model to detect the potential fake reviews. I trained my models on the Makeup Alley dataset, which I considered the “authentic” dataset. Then I classified the test data, which contains the Sephora reviews, as similar or different to the training set. If the data point from Sephora is similar to the training set, then it would be classified as authentic. If it was different from the training set, it would be classified as fake. I set up my models so that the threshold for 'fakeness' can be adjusted per business use case. The models I used were called One Class SVM and Anomaly Detection with MLP auto encoder. This project was great to highlight in my job interviews!
How did Galvanize prepare you for the job hunt?
During the course, we had a career service lecture every week to teach us how to brand ourselves. We had several whiteboard sessions during the program where we would solve either Python or SQL challenges and our partner would act as an interviewer. Towards the end of our course, we also had mock technical interviews with our instructors and mock behavioral interviews with our career services manager.
After graduation, we had weekly career service meetings where we would share tips and the status of our job hunt process with our cohort and the career services team. Besides these weekly meetings, I know I can always reach out to Galvanize to receive help with my cover letter and resume.
Since you were on the job hunt during a pandemic, what job search strategies worked best for you?
Applying to as many places as I could! Some people feel that it's quality over quantity when choosing where to apply, but I didn't follow that theory. I also networked to get referrals from friends. I'm extremely active on LinkedIn and made sure to share my projects there.
What advice do you have for recent data science grads looking for a job now?
Don't be afraid of job descriptions or degree requirements. Just apply! You don't have anything to lose.
Congrats on your new job at Gap! What was the interview process like?
I sent a cold application to Gap; I found out that Gap was hiring from LinkedIn and followed the link to their website where I applied. I felt that applying through Gap’s website instead of through LinkedIn helped my visibility. Then I had a Python and SQL technical challenge. I was prepared for the Python questions by Galvanize. In my cohort, Galvanize only had one day of lecture on SQL, so I recommend that new grads brush up on the language to be better prepared for future roles in data science. Galvanize did a great job in their introduction to SQL, but I would have needed to prepare more in order to apply it in the interview challenge.
Since you did the Galvanize bootcamp in-person, did you feel prepared to work remotely in your first data science job?
Yes! Working remotely comes naturally to me, and onboarding remotely wasn't a problem for me at all! Gap had three days of onboarding where we learned about the company and signed up for benefits. My team had a technical session to familiarize me with the tools and platforms we are using. We also discussed our team's main goals for the next few months.
What does an Associate Data Science Engineer’s day look like?
I'm working on a data engineering team that’s creating the back end. We make the data available and optimize it for the other teams within the company. So far, I use everything that I learned at Galvanize. On my team, we have a daily standup in the morning. I tell my team what I will be working on that day. If something comes up, we have a chat room that we communicate in. If I am stuck, I can reach out to my team so we can work on it together.
Looking back, is this career as a data science engineer what you expected?
My initial goal was to become a data scientist, but during my capstone project, I became interested in data engineering. Data engineers focus on extracting, maintaining, and optimizing data whereas data scientists analyze data and create machine learning models from it. At the beginning of my journey, the roles I considered were more on the analytical side like data scientist and data analyst. However, I found my new interest in data engineering at Galvanize. For my capstone project, my instructors encouraged me to create my own dataset rather than using datasets that are available online. I spent 80% of my time webscraping the data from a website and optimizing the dataset for my machine learning models. I was really happy that Galvanize covered skills that can be used in many types of data-related roles! So while my current career isn’t exactly what I had expected, I am happy with it!
How are you using your new data science skills for social good?
My good friends were creating a website for Black-owned and COVID-affected local businesses called Uplift Locally, but they only had 30 establishments included on their platform. My friends obtained a dataset that includes thousands of restaurant names and their locations. However, they needed more crucial information, like directions, websites, addresses, and photos for the website. They could not figure out a way to obtain all that information since they were doing everything manually. And that's when I jumped in and helped them generate the data they needed. The new data included photos, directions, websites, and addresses to each restaurant. We were able to put up thousands of restaurants all over the country on the Uplift website!
What has been the biggest challenge in your journey to becoming a data science engineer?
Prior to joining Galvanize, I felt that my goal to become a data scientist was a long reach. Now, I realize what an incredible opportunity the immersive presented and how much I learned. After I attended Galvanize, I felt ready for the job market, even though I had some self doubt when I wouldn't receive interviews. Rejection is difficult for me. Many times, I wouldn't be selected for a job position because of my lack of past experience in the role. Having the career service meetings with the Galvanize team gave me the support I needed to try harder and push myself. I had to accept that there was nothing that I could change about the situation. The companies that didn't give me a chance were not the right fit for me.
What’s your advice for anyone who is currently considering a data science bootcamp?
Go for it! Don't overthink it. Being fully immersed in the environment Galvanize creates for students accelerates the learning process and facilitates your preparedness for the job market. The more you think about it, the more likely you will decide against it and lose out on the incredible opportunity to jumpstart your new career in data science.
Was Galvanize worth it for you, Suchaya?
Definitely! Going to Galvanize was a great experience that taught me data science skills and soft skills that I use at my job every day.
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