Online Data Science Degree at Johns Hopkins | Interview with James Spall

Online Data Science Degree at Johns Hopkins | Interview with James Spall

We met with Dr. James Spall to discuss the online data science program at Johns Hopkins University. Enjoy!

Dr. James Spall from Johns Hopkins University discusses the university’s online data science program with student Karina Macosko. The program is more rigorous and theoretical than other online programs, giving a degree from this program a longer “shelf-life” in the field of data science. The program focuses on teaching the basics of data science, allowing students to learn new skills even as software changes. Despite the program requiring prerequisite knowledge of multivariable calculus, Professor Spall is willing to work with students who have not completed the prerequisite, but want to pursue an online data science degree. He also mentions that free non-credit online programs can be an excellent option for individuals looking to learn a specific skill. Dr. Spall encourages anyone interesting in pursuing a degree to go for it!

Johns Hopkins online master’s in data science ranks #2 in the nation by our algorithm. We rank programs based on the influence of a school’s faculty and staff in the field of data science.

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Karina’s Interview with Professor James Spall



Interview Transcript

(Editor’s Note: The following transcript has been lightly edited to improve clarity.)

0:00:14.8How did your program get started?

Karina: Hi, my name is Karina Macosko from Academic Influence, and I am here with James Spall, and we are with another online program. He is from Johns Hopkins University. And so starting off, we just wanna hear how your program got started and what was the original idea for it?

James Spall: Okay. So thank you for the interview invitation here. So the data science program started about five or six years ago, and it was largely in response to demand in the marketplace. So Johns Hopkins University has a very large part-time division called Engineering for Professionals, which serves largely people who are working professionals and offering classes that can either an online modality or in the evenings or late afternoons that are convenient for people who are working. We had an applied math program already well established. It had been around for several decades, and that applied math program had quite a few courses related to data analysis and statistical analysis and so on.

However, the needs of the marketplace were suggesting something stronger related to data science, which is a little bit... People mix up statistics and data science very frequently. They are slightly different, have different emphasis, and the fields themselves are somewhat overlapping but somewhat different. So we needed to establish the data science program because we felt the market was driving us that way. So what we did was we sat down with a tiger team, so to speak, and organized this program in a way that tried to distinguish what we were offering from what was being offered in the general market. And in particular, what we wanted to do was to have a program that was much more rigorous and more theoretically focused than a lot of the other data science programs that were in existence, and to tell you the truth, still are in existence.

So the Hopkins program is more focused on the fundamentals, I guess, of the field. And so we merged aspects of statistical analysis with a lot of computer science and data handling issues, and put together a program that has been refined over the years. We’ve tweaked it here and there, but the basic program, the fundamentals of the program are essentially the same as they were when we established the program in 2016. So that’s...

0:02:39.9How do you think the field of data science will change?

Karina: Wow, that’s amazing. And it kind of sounds like your program has followed the general trends of a lot of programs, not just online programs. So a lot of data science started out as an applied math program and then grew into a field of its own. And so just looking forward, how do you think that will continue to change? Data science... We just talked to somebody who is doing cybersecurity one, and similarly, these programs have grown exponentially in the last couple of years, and they will likely continue to grow. So what do you see for the future of your program?

James Spall: Well, we see... So I guess my clichéd answer would be just a natural evolution. Okay. There is nothing really profound to answer it that way. The field itself is evolving as computational issues change and things like computer visualization and data handling issues change. We are going to maintain our focus on the fundamentals. Again, as I say, this is something we very... The leadership very strongly believes in our program as a way to set it aside from... Or set it apart, I should say, from many other programs that are in existence. As you know, data science is a field that is really, I would almost say flooded with programs of different types, but we wanna maintain more of an academic flavor to our program.

So in terms of how it evolves over the next few years, we’ll continue to tweak the course offerings, perhaps add some more course offerings. We are continuing our very strong online presence. It’s a strongly online program now, although you can pick up a few of the courses in a face-to-face modality, but the vast majority are available online. We’ll continue with that trend. Hopkins, the university, that is, has several data science programs. The School of Public Health offers one, the main campus, during the so-called Day Program, offers one. We offer one which is more for working professionals, that will continue. And so I don’t see any major change over the next, looking out, say, five years or so. Beyond five years, my vision is very, very fuzzy. [chuckle] So I don’t wanna speculate too far out especially in a field that, like data science, is fairly rapidly evolving.

0:05:03.5Could data science evolve into new fields entirely?

Karina: Right, yeah. And I wanna go off of what you said with data science being crowded with a lot of other fields. Data science itself evolved from another field, which we have already talked about, and it seems like it is being incorporated into every other field. So this is kind of, not just for an online degree, but data science in general. Do you think it’ll start to bud off sort of new fields within it? For instance, biology with data science applied, do you think that will ever become just a stand-alone field with another program, or how do you see that evolving?

James Spall: Again, my vision, I don’t wanna make any claims that are way outside of my area of expertise. Okay? There certainly are... Even today, there are many, many tentacles out into various applied areas. That’s the role of data science, is fundamentally is in applied areas. Though, as I said, our program tends to focus more on the academic and theoretical principles of it, but the actual ultimate usage of it is in the real world. And the real world obviously involves things like medicine, cyber, etcetera, etcetera, etcetera. So there are already vast amounts of connections with real world problems, whether those spawn off specific academic programs that are very very narrowly focused. I am sure there are...

And I know, there are many already, and there will continue to be more, and the need will grow because for example, within the realm of genetics and something, there is just a tremendous demand for proper data handling and proper data inference type issues that go with a field like that. So fundamentally though statistics remains at its core, we have set up a program where the statistical methodology and the basic underlying equations and formulas and such that are related to basic statistics, remain there. So data science, you have a lot of the data handling issues and you have to be concerned about the pre-processing type issues, you have the statistical part that sits in the middle where you take all the data, you merge it with methodology and formulas.

And out of that comes other data, post-processed data, I guess you might say, or processed data that have to be interpreted and such, and again, that’s where we got data science as well as again, further statistical methodology can come up as well. And that as far as I can see, will remain at its core. I am not sure I am fully answering your question.

0:07:39.1What is different about your program?

Karina: No, no, that was great. Yeah, of course nobody can really predict what’s gonna happen and I am sure nobody would have expected data science to be at the height it is 10 years ago, the height it is today. And so kind of switching gears a little bit, let us talk specifically about your program. I know we touched on this and how you guys really focused on the academic rigor, and so just kind of tell us what do you think is different about your program versus a lot of the ones that are offered? And also, what do you think is different about the kind of students that would really thrive in using your program compared to others?

James Spall: Okay, so really... Again, our program was designed and continues to be one that is more focused on the fundamental academic principles. So as part of that, we do have a higher level of prerequisites coming into the program. So we do expect more of our students coming saying the world... In their terms of their background, both on the computational science side as well on the mathematic side. So to get very specific, we require all of our students to come in with at least a multivariate calculus, which is typically a third semester of calculus for people. And then there are other prerequisites as well. And so in terms of that, that’s gonna change... I mean, that’s not gonna change, that’s gonna be solid as far as we go forward. And in terms of how it compares to other programs, a lot of the other programs are more focused, I would say, on the user end, maybe how you actually, out in the field actually use software.

There are many, many software tools that are available for doing aspects of data science, so some of the programs are more focused on the use of those software tools, which of course can go in and out of fashion as time evolves. We are not so focused on that, although certainly students coming to the program and getting through the program will do software-based experiments, and the things, possibly even including a capstone project, so that’s gonna stay the same. But because of our emphasis on more of the rigor and the theoretical principles, we like to think of the shelf life of a degree from us is a little bit longer than it would be for a lot of other programs, because software comes and goes as we all know and what’s in fashion, but the basic mathematical foundations and such, and even computational algorithmic fundamentals, whether they’re from the math side, or whether they are from the computational, or computer science side, those stay longer, those have a longer shelf life, and we are gonna use that as part of our founding principles and part of our guiding principles going forward.

0:10:29.5What kind of student is a good fit for your program?

Karina: Wow, yeah, and I think that’s a really great point that a lot of people don’t consider is, if you are going to get a data science degree right now, the technologies you are using might be completely different in 10 years, but you are still gonna be in the workforce, so having that foundation is really key. And so apart from just the pre-reqs that a student comes in with, who do you think is really the target for this online degree? Is it people straight out of college who are just looking to further their education, or is it somebody who’s been in the workforce for 10 years and now they’re coming in, they wanna apply data science to whatever their profession, or they wanna go into a degree in data science?

James Spall: Well, we draw from all of the above, I guess I would say.

Karina: Okay.

James Spall: But mainly we draw from people who have been working for a little while. So let us say our core demographic are people who have been out in the workforce for several years, and I would define several here and quite broadly, ranging from maybe 2 or 3 up to 20, even beyond that, some of them are going... Are maybe making a transition in their career, they wanna change the path a little bit, some might be just building on what they have been doing all along. So our core demographic are people generally working professionals, who wanna build their academic skills in this world of data science, and again, we think that that’s gonna stay, that’s what we see going forward in terms of our demographics. Our demographic now, I expect that’s gonna be our demographic going forward now.

Now again, that contrast us with maybe the so-called day program at the campus, because Johns Hopkins, as I mentioned, one of the other data science programs they have is hosted at the... What’s called the Homewood Campus, which is sort of the main academic campus of the University up in Baltimore. And they have a very good data science program as well, attracting, the kind of you attract more students on a traditional academic path of coming out with a bachelor’s degree and going directly into a graduate degree program, so that’s a master’s program as well, for the most part, and it draws people... I guess you might say a little on the younger, usually a little younger and these are people fresh out of school. Now, I should say we do get some people like that as well, it is not... As I said, we draw from all of the above when I started answering your question, but our main demographic is people who have been working for several years.

0:12:57.6What advice would you give someone considering a data science degree?

Karina: Well, that is just fantastic because we have really seen throughout the people we have talked to from online degrees how fantastic these programs are because also for a lot of people, especially in data science, data science really was not even a field you could go into when some of these people were going to school. So given the people fresh out of school, obviously, a lot of universities have incorporated in data science field, but if you have been in the workforce for 20 years and you really wanna go get a data science degree, that was not always an option when you first started working. So I just think it is fantastic to have online programs like these to really make it accessible for more people. And so just finishing out, is there anything that you would like to say to people who are maybe considering going to get an online degree specifically in data science or any encouragement you would like to give them?

James Spall: I guess my encouragement would be to go for it, [chuckle] I guess, but on the other hand, you obviously wanna get into a program that is right for you, and it is just data science in particular, and I would say this applies to many other programs as well. The needs are widely varied and whether you want a program that is more directly and short-term applied is one thing, whether you wanna focus more on academic principles and rigor is another thing. And I say in most cases, sooner is better than later. If you have an opportunity, if you are a working professional, and have an opportunity to go for it through some program, maybe your employer is sponsoring, whether they maybe subsidize some of your tuition costs or whatever, take advantage of that.

I should mention, of course, that you can get courses through organizations like Coursera and such that are actually pretty good. They’re well done, they’re very professionally created and curated and such, they’re nice programs, sometimes non-credit programs, and they can be appropriate as well. If you wanna learn some particular software products, and be ready to put that into immediate direct use, those are good programs. But again, a program that such as ours has a little longer shelf life and if you are willing to make the investment in terms of studying hard and coming in with the right prereqs, I would encourage you to give us a look and we’ll be happy to accommodate you.

Also I will say one other thing too. In terms of the prereqs that we have, as I said earlier, they’re a little higher than maybe many other programs have. If you think you do not have the prereqs to succeed, we will take a good look at your academic background, and if there is something missing, we can generally provide some of that background material within Johns Hopkins to build that up to cover that missing link basically to allow you to succeed in our program. So if you are missing, for example, a particular... A calculus course or something like that, we can provide that for you, so if we think you are otherwise strong academically. So I guess in a nutshell, that’s what I would say going forward. Okay. Got it.

0:16:16.1Sign Off

Karina: Yeah. That was fantastic. So don’t be scared off by the prereqs because you guys are there to help out, and if you think that this is something you wanna do, just go for it. And I think that is great advice to people out there who are maybe on the edge of going or considering it. So thank you so much for talking with me today. It was really interesting hearing about your program. And yeah, it is a new world that we are moving into of online learning and I think schools like yours have really done a great job incorporating these into their curriculum. So thank you so much.

James Spall: Thank you very much for asking, they’re very insightful questions. I enjoyed this a lot. Okay, yeah.