Lavender Zhang was a student at Flatiron School’s part-time Data Science bootcamp and ready to build her Capstone Project. Empty shelves at her local grocery store at the start of the pandemic inspired Lavender to build a dashboard that could predict food crisis levels and infrastructure needs. Lavender walks us through her final project, explaining where Flatiron School instructors helped her navigate challenges, and how she’s been able to discuss topics like geospatial visualization in interviews! Plus, hear Lavender’s timely advice for aspiring data scientists and the need for data skills in 2022.
What motivated you to enroll in Flatiron School’s part-time Data Science bootcamp?
After completing my master’s at New York University (NYU) in Urban Infrastructure Engineering, I began working in the energy industry at Bright Power as an Energy Analyst. I wanted to advance my skills in data science and did some research about what opportunities were out there. A colleague recommended that I look into Flatiron School’s Data Science bootcamp because she had done the Software Engineering bootcamp. I spoke with Flatiron School’s admissions team, and they provided an excellent overview of the program, including the career service support and the immersive curriculum. I felt confident in their bootcamp and decided to enroll at Flatiron School while I kept working full-time at Bright Power.
What is the difference between learning data science at a bootcamp like Flatiron School instead of at university?
During my time at NYU, I was interested in coding so I took master degree-level coding classes through the department. Each week, the classes would dive deep into the intricacies of data science and coding, but it was difficult for me because I did not have a strong coding background. After finishing this class, I had a general understanding of the data science world, but I did not have the fundamentals I would have needed to pursue a career in it. A data science bootcamp like Flatiron School provided me with more confidence and a solid foundation in data science so I could actually pursue a data science career. On top of that, bootcamps are beginner-friendly and attract people from various backgrounds and industries. Even coming from a STEM field, I felt I could learn the basics and work towards more advanced techniques, like machine learning and deep learning.
What was the application and interview process like at Flatiron School?
The process was quick and not very complicated. The admissions team provided a thorough overview of what I could expect from the bootcamp. It consists mainly of two parts:
What did the Part-Time Data Science bootcamp curriculum cover?
Flatiron School structures its Data Science bootcamp into five modules:
In addition to the capstone project, students must complete either an individual or small group project at the end of each module to move forward in the program. In the ten months that I was enrolled in the part-time bootcamp, I built five projects that were helpful when creating my portfolio.
How did you balance the online bootcamp while working full-time?
I had the benefit of completing my bootcamp both online and during a pandemic, which meant I could allocate more time to work because I had to stay home. Instructors provide workshops and lessons in the early evening, so I was able to focus on my job during the day and then easily hop into my online bootcamp classroom in the evenings. You can also set up one-on-one meetings with instructors if you’re having trouble with the assignments or projects. My instructor, in particular, was very flexible and located in the same time zone as I was, which made it easy to get in touch with her. She also has a similar background as me, so she understood the challenges I was having.
So Lavender, what did you build for your capstone project?
At the beginning of the pandemic, we experienced empty grocery store shelves so that made me wonder about food security issues and the factors that drive it. Through my research, I found that infrastructure was a factor in global food security issues. I decided to create a two-part, capstone project so I could:
Which technologies and programming languages did you use to build this project?
I used Python, Jupyter Notebook, and Streamlit to build this project. Streamlit, which I used to create the dashboard, is a relatively new Python framework so it wasn’t covered in the Flatiron School curriculum. My instructor, Amber, helped me look for various resources to create this project, including introducing me to Streamlit.
What was your biggest challenge while building your global food insecurity project?
The data processing was tricky, particularly matching all the regions and ensuring the geolocations were on the exact coordinates and using the same projections. I have to thank my mom for assistance on this part of my project, as she has a background in cartography. She helped me think through the best coordinates to use.
I started the project by only looking at geophysical factors, such as climate, landscape, and weather, and not thinking about social activities. At the beginning of my project, the model wasn’t predicting very strongly. I started thinking about other factors and looked upon more research papers from a professor from my undergraduate studies who was an expert in food security. She referenced further research that used social activities and infrastructure level as a factor. That led me to look at all the data sources to make my dashboard more comprehensive and robust.
Do you anticipate using this project for any future interviews?
Yes! This is a project I spent quite a bit of time on, and I have continued researching further, including geospatial visualization and food security. I have already spoken about this project in a few of my interviews and feel more confident talking about it because I built it from scratch.
Are you using everything you learned at Flatiron School on the job as an Energy Analyst II at Bright Power?
Yes! Recently, I have worked on projects at my current job that focus on statistical analysis on energy efficiency and performance. I utilized the statistical knowledge I received through my Data Science bootcamp to complete this project. Another project that I also recently took on is a pilot project that models energy usage with machine learning. I was able to think outside the framework we were using in order to add some interesting pieces to the project based on what I learned at bootcamp.
What’s your advice for incoming Data Science bootcamp students?
Utilize the time that you are in the bootcamp and make the most out of it! Prepare in advance for your studies and always dive deeper into the content. For example, in my job interviews and technical assessment, the interviewer was looking for my understanding of data structures and building scripts that save space and time. I wish I had taken more time to dive deeper into this during bootcamp.
Would you recommend Flatiron School to other aspiring data scientists?
I would recommend it! Continuing to learn is key to advancing your career. Many industries right now are utilizing data for optimization and creating better predictive modeling for energy generation, forecasting weather, and more. It’s an excellent time to build your skills through a data science bootcamp.
If you’re already working in data science, a bootcamp can be a good opportunity to refresh your knowledge or learn new technologies. It also allows you to meet new people who have new ideas from different industries.
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