James Kantor

About

James Kantor’s data science journey originated from a professional challenge: designing a machine learning (ML) course. From there, it quickly transformed into a passionate pursuit of deeper expertise. Already established in the field as a certification lead for machine learning and generative artificial intelligence (GenAI), he recognized that the rise of GenAI in 2023 demanded more than surface-level knowledge.

Thus, James turned to the online master’s in data science (OMDS) degree program at Boston University (BU) for its flexible format as well as academic rigor and reputation — especially valued by someone based in Boston. With the support of BU’s faculty and learning staff, he is confidently advancing his career toward becoming a thoughtful, impactful, and well-rounded data scientist.

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

Several years ago, in a previous position, I was charged with creating a machine learning course for new Spark users. Until that point, most of my IT career was in data capture and analytics. After experimenting with predictive modeling and seeing its power, I found myself with the same excitement that I remembered from the beginning of my career. At that point, I decided to continue this new journey, so I got involved in some of the well-known data science resources like Kaggle, Medium, etc.

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

I was already in my current position in certification for ML and generative AI at Databricks when I started considering getting more serious about my skills. I think it was when GenAI took off around the world in 2023 that it became clear I needed to strengthen my fundamentals in the technology. I had just presented our new certification on GenAI at our conference and met some great practitioners and experts. My conversations really convinced me that I wanted to go forward in data science. What was immediately clear is that a certificate or an online course wasn't going to be good enough; I had to go all in to get to the fundamentals and ensure a strong foundation, so I knew a master's program was what I needed.

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

The main driver for me, without a doubt, was Boston University's reputation for solid academics. It isn't just what you read online. Living in the Boston area, there are plenty of graduates who will attest to the quality education they got at BU. So, I was totally comfortable with that.

Next, of course, is that I wanted to get my education and still keep my current position. I knew I needed an online program. It seemed like a stroke of luck when I read about the program. Finally, I read the curriculum, and I was sold by the program's design, scope, and structure.

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

I am definitely eager to acquire the foundational skills in data science: mathematics, statistics, and experimental design, plus practical experience building models and experiments. And it's been working quite well so far.

I expected a challenging environment, as this is a master’s program at a major U.S. university. At the same time, the learning support staff are always available, and I feel I have made major progress so far, both with their guidance and on my own.

I plan to level up my current career in data science with my skills. I am already applying what I've learned in my current role and will use the full skill set to orient my career more toward data science when I graduate. For me, it makes sense because we're moving toward a data-centric world, and IT and software development are adjusting to it. You can see new roles in data science opening up every day as the field keeps growing in importance.

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

There are way too many to list. We have had industry guest speakers give us a full scope of GenAI and data science for today and the future. We've also had challenging coding and science projects that yielded real-world and production-ready results. So, it's been a great spectrum.

Additionally, I am quite impressed with how the scope of the program is changing as we continue. I can see how the department is serious about making us into competent and realistic data scientists. For example, we're being led through formal logic and ethics to empower us to make responsible decisions as data scientists. Also, our course on causality is giving us the deep knowledge to analyze problems in the real world and build solid experiments to investigate them. I don't think I expected such depth and rigor, but I sure am enjoying that.

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

I've already mentioned Professor Villegas’ ethics course and Professor Von Korff's causality class this semester. Otherwise, I actually can't choose a single favorite from the curriculum.

Prof. Considine's Mathematical Foundations of Data Science (Module 1) was rigorous but well-paced, and even with my non-specialized background, I was able to grasp all the content.
Similarly, Prof. Snyder's Machine Learning Fundamentals (Mod 3) was the kind of iterative approach to building performant models that I think we all needed to build ML modeling muscles. The practice and software development approach was great.
Talking about practical — we built pretty impressive cloud pipelines and data structures in Prof. Seferlis’ Data Management at Scale (Mod 4).
Through it all, Prof. Von Korff's AI for Leaders (Mod B) is a multi-semester, soup-to-nuts curriculum of all types of topics from data analysis and visualization to statistics and science fundamentals.

Each of these courses has helped lead us to create a worthy portfolio of work for our future careers while also delivering the fundamental skills to do rigorous and ethical science.