August Siu

About

For August Siu, a high school mathematics and computer science teacher, the field of data science offered the perfect intersection of curiosity, challenge, and real-world impact. Initially inspired by classroom experiences and a growing interest in tools like R and Python, her passion deepened as she witnessed the rapid rise of generative artificial intelligence (AI) and large language models (LLMs).

Drawn to Boston University’s reputation, supportive bootcamps, and flexible online format, August embraced the online master’s in data science (OMDS) degree program to expand her expertise — namely in deep learning, medical imaging, and AI automation. Now, with a capstone project focused on Alzheimer’s diagnosis, August exemplifies how the OMDS program empowers educators and professionals alike to bridge theory and application in a fast-evolving data landscape.

What initially sparked your interest in data science, and how has that interest evolved over time?

I am a mathematics and computer science (CS) teacher; I love analyzing information and asking my students to research and find linear and exponential equations based on points in a scatter plot. When I learned more about data science, I realized that it was an actual concept scientists use in the real world. I was originally introduced to data science by learning a bit about R and have always wanted to learn Python. So, with time — especially also living in the Silicon Valley and with the world's fast changes in GenAI and large language models (LLMs) — I realized that I have to take a bite of it!

What were you/are you doing professionally or academically before joining the CDS Online Master's program?

I was and am still a high school math and computer science teacher.

Why did you choose the CDS Online Master’s program specifically, and how did it stand out from other programs?

I went to Providence College for my undergraduate program and lived on the West Coast in San Jose, but my family and friends have always been in the Massachusetts, Connecticut, and New York area. When I discovered the Boston University (BU) OMDS program with the online platform and its cost, along with bootcamp classes before the term started, I felt that it was the perfect school. BU has always been a renowned school, and I have always wanted to attend a university like it — so I was excited (and nervous) to apply when I got the chance.

What skills or knowledge are you most eager to gain from this program, and how do you plan to apply them?

At this point, I would love to learn about deep learning, facial recognition, and imaging. I am writing my capstone research on Alzheimer's disease (AD) and have focused a lot on concrete numbers from CSV files with data such as beta and amyloid protein. However, I don't have any background when it comes to looking at an image, translating that to coordinates, and then analyzing what that target goal of the image entails. I would also love to learn about automated AI and how AIs can operate independently.

Have there been any “aha” moments or unexpected insights during your time in the program so far?

I think in every class, there have been many such moments. From learning about the different models and ethics to how our capstone has been laid out, every step of the way has been more or less an “aha” moment.

I would have to say that the most recent one is using all the models to identify not only the data but also which features would be most connected to the target in the forms of various modeling. For example, in week two of my capstone course, it was nice to see that, while running through elastic net regression, my handwriting dataset showed that all features have some sort of correlation to the target of AD diagnosis — but in the biomarkers dataset, the tau specifically played a bigger role than any other proteins.

What has been your favorite course in the program so far, and what made it stand out to you?

I have a few favorite classes in the curriculum:

I love Dr. Snyder's classes and the Module 3 course, Machine Learning Fundamentals — where we focus on the housing dataset, go through all the various models, and compare/work with teams to help support us. In general, Dr. Snyder does a great job of capturing the audience when he teaches!
The capstone classes (Mod A, B, and C) allow us so much autonomy to work on the assignments and learn coding independently while still regularly covering skills on certain concepts in the homework. I think Dr. Von Korff designs his assignments and lessons to truly teach concrete skills every week while broadening our minds on how to think like a data scientist.
Another course that stands out to me is Data Management at Scale (Mod 4) with Dr. Seferlis. I have to say, this course was not intuitive, but I think it was by far the most important in the real world. Dr. Seferlis working for Microsoft and looking at what is actually important in the industry helps, too. When I communicate with real data scientists and people in the tech world, working on data, SQL, Azure, AWS, pipelines, triggering, and everything we learned from Dr. Seferlis is what a data scientist actually does outside of academics, so this course was important to learn!
Lastly, Responsible and Ethical Data Science and AI (Mod 5) is a personal favorite because I am generally interested in ethics, law, and policies. As a previous political science major who attended an undergraduate school where we focused on philosophy, theology, and ethics, I love Dr. Villegas' questions, debates, and the overall content of the course. Although it entailed less coding and more policymaking, I would consider it one of my favorite classes so far.