Show Summary
The NYU Stern Masters of Business Analytics and AI (MSBAI) program is a one-year specialized degree program that trains students to make data-driven decisions. Dr. Anindya Ghose, Heinz Riehl Chair Professor of Technology and Marketing at New York University’s Leonard Stern School of Business and Director of the Masters of Business Analytics and AI Program at Stern discusses everything applicants need to know. He shares the core aim of the program which is to impart cross-functional skills that can be applied across industries. Additionally, Dr. Ghose highlights the career path for graduates of the program.
Show Notes
Welcome to the 597th episode of Admissions Straight Talk. Thanks for tuning in. The challenge at the heart of admissions is showing that you both fit in at your target schools and stand out in the applicant pool. Accepted’s free download, Fitting in and Standing Out: The Paradox at the Heart of Admissions will show you how to do both. Master this paradox, and you will be well on your way to acceptance.
Our guest today is Dr. Anindya Ghose, Heinz Riehl Chair Professor of Technology and Marketing at New York University’s Leonard Stern School of Business and Director of the Masters of Business Analytics and AI Program at Stern. Normally, I would give a brief bio, but Dr. Ghose’s list of achievements, titles, degrees, and books is so long. Let’s just say he’s here today to discuss Stern’s Masters of Business Analytics and AI Program.
Dr. Ghose, welcome to Admissions Straight Talk. [1:40]
Thank you for having me, Linda.
Can you give us an overview of the Masters of Business Analytics and AI Program at Stern? [1:46]
Sure. So this is a one-year, specialized degree program in which our graduates are trained to take data-driven decisions. As you might know, over the last, more than a decade ago or so, companies started moving towards making decisions based on data as opposed to simple intuition and gut. And I think there was this opportunity that no other business school had a degree in this space, and we thought, “Why don’t we be the first?” And turns out that the market really handsomely rewarded our graduates. It’s been almost 12 years now.
Why was AI added to the title? [2:34]
Right. So amongst many hats that I have, I spent a lot of time in the industry helping companies with their data-driven decision making. A lot of them have data and they don’t know quite what to do with it. And the last four or five years I’ve been working with them and it turns out that all of these companies have been hearing about AI, but they didn’t really know what AI is. So it was this mysterious, mythical thing. But I had a background in AI for almost 20 years. It’s just that at that time we were the nerdy geeky folks, and today we are cooler and happening because AI is everywhere.
And so I think when I talked to a lot of these companies, my colleagues or ex-colleagues, they were like, “Look, we need people, graduates with degrees or at least skill sets in AI that we don’t have.” And I had been anticipating this over the last four or five years, and I’d been already making changes in the curriculum, like putting in new courses in AI, revamping existing courses. And as the market really started talking about AI, I approached our deans and said, “Look, I think we are ready. We should rebrand or rename the program.” And it took a little while for the discussions to actually come to fruition, but eventually our deans agreed and said, “I think it’s the right time.” So that was the genesis. I saw the market demand, I saw a huge feedback from recruiters saying that we need skills in AI. And the curriculum was already geared towards an AI degree, and so I thought this is the right time.
I mean, I am not a techie at all, but isn’t AI just a very advanced use of data? [4:14]
In some ways, it is. And so we have this framework based on which I put together this curriculum in the MSBAI program. Data is just one part of it, but data engineering is sort of the lowermost level. And then there’s analytics, there’s modeling, there are various dimensions of AI like generative AI, and there are various dimensions of machine learning. The way I design this curriculum is we have this stack layer-based approach where we start with such broad-based skill sets first and then narrow it down like a pyramid. And so data, you’re right, the data is a part of it, but it’s one thing. There are many other components in an AI degree program that are required.
What are some of those other components? [5:09]
So for example, most of the data sets that we know they exist are raw and dirty. They’re not processed, they’re not curated, they have a lot of problems. And so data engineering is the first step where unless you clean the garbage, you end up with sort of what we call garbage in, garbage out, right?
And so we basically train our students before they unleash the AI algorithm, we train them to clean the data. And that’s not a very kind of an exciting part of the job, but it’s an incredibly important part of the job. And then once you do that, then we train them on how to build different models like predictive models or causal models or prescriptive models. Then we teach them how to tell the story, which is, what is a question you’re trying to answer? Why is it important? Who can use it? And finally, we teach them what we call responsible AI, which is you should always keep in mind that ethics and fairness and equality and equity is important. And so AI can be magical, but you’ve got to be very cognizant of the limitations of AI, which is you have to devise the algorithms to make sure they’re fair, they do not end up in discrimination, the algorithms are trained using ethical means. And so those are the other components beyond data.
That was a great answer. For a non-techie, I appreciate it. Who is this program intended for? [6:33]
We actually draw candidates and applicants from across the board. We almost never, even from the beginning, we never restricted only to engineers. Typically, we see that people with some level of quantitative or computational background have maybe slight edge coming in, but by no means is that sort of a deal breaker. So if you don’t have one, there are other means with which you can compensate for that. So a lot of people we work with in the program, they may not have a professional degree in engineering or quantitative sciences, but during their work experience, they’ve actually acquired on-the-job skills that are more quantitative or computationally intensive. And so sometimes a lack of a prior degree in these skills can be compensated by actual work experience.
Can you go over the structure of the program? I know this is a program for working professionals, right? [7:42]
Yes. Until last year, that’s what we asked for. You should have at least three to five years of experience. But now going forward from this year, because of this massive demand of AI, we’ve started to relax it now. We no longer are going to impose that three to five years of prior experience.
There are in-residence portions of the programs and more portions of the program, correct? [8:07]
That’s right. So it’s not a full-time program. The way it works is we have five modules. Each module lasts for about a week. It’s during those modules, these students meet with the faculty in person physically and mostly in New York City with one global module. But then in between modules, everything is happening remotely. Some courses require you to check in at least once between modules, some don’t, but the students have to, obviously, they have to work in groups. So amongst themselves, they are constantly almost on a weekly basis, they’re logging in and discussing and finishing assignments and pre-module, and post-module work.
During the remote portions of the program, how much time per week do students typically spend completing the program? [8:53]
I would say on average, it’s between 25 and 30 hours a week, which makes it possible for them to continue their full-time job. Let’s say they have 40 hours or 50 hours of full-time job. And then on top of that, they can add another 20, 25 hours a week. A few weeks tend to be higher, but the 20, 25 is typically the average.
I know you mentioned that most of the in-person sessions are in New York City, but you also mentioned that there’s a global component to it. Where do you typically go? [9:21]
So we have one global module, we used to have more before, but we have one global module in Abu Dhabi in the United Arab Emirates. And there’s a couple of reasons why I chose that. One is NYU has a full-fledged campus in Abu Dhabi. It’s relatively brand new and it’s really pretty awesome, pretty flashy and all that. But also the other important reason is the Gulf states are extremely progressive when it comes to technology adoption and thinking about the vision. So if you look at Qatar or Saudi Arabia, UAE, when it comes to AI, they’re actually very progressive and in some ways a lot more than maybe Europe or Africa. And so when it came to the choice, because I wanted to give our students an exposure into maybe opportunities globally, I thought, “Look, NYU has an infrastructure on top of that. This is a very progressive nation when it comes to blockchain technology, AI, so let’s make the best of it.”
How many students are in the MSBAi? And do they ever mix or meet with the other graduate or undergraduate students at Stern? [10:33]
So we have approximately 40 as of this year. When it comes to mixing with others, there’s no formal requirement, but I see them when I’m connected with them on LinkedIn or Facebook, I see them informally mixing with MBAs. That does happen. They’re about the same age group and then the MBAs, there’s sort of a synergistic mutual relationship. The MBAs benefit from understanding the specialized knowledge that the MSBAI, and and the MSBAs can benefit from having networking opportunities with companies which traditionally may not have recruited MSBA people, but now when they talk to the MBAs, they’re like, “Oh yeah, so you guys also need people like us.” And so there’s this nice, kind of a symbiotic relationship.
I once had this discussion with one of my kids. Isn’t the MBA a more recognized degree and somewhat more flexible? What is the benefit of getting a seemingly narrower degree? [11:29]
So I think the shifts started to happen, like I said, around the time when we put this program, because even with the C-suite, the CEOs and CMOs with whom I often work very closely, they started noticing the shift from this intuition-based decision making into data-based decision making. So it became people no longer simply rely on their gut primarily. And so I think as they started to see the shift, they also realized that there are some benefits to doing it this way, using more scientifically approved methods. And that momentum then picked up as big data became popular, and then business analytics became popular. And now with AI, because of the very rapid progress in competition complexity of both hardware and software, I think as an organization, if you’re not making data-driven decisions, you’re just leaving a lot of money on the table. So I think it’s just a matter of time.
What are the academic requirements to gain admission to this program? And what are the nice-to-haves? [12:47]
I would say maybe more than academic requirements, we traditionally have looked for some relevant work experience in broadly, let’s say sort of again, this space of analytics or analysts or maybe even programming. So I think we typically look for people with background in analytics or programming because if you have that, it sort of helps to hit the ground running, right?
I assume some knowledge of statistics is pretty important. [13:30]
Yeah. So sometimes you have backgrounds in statistics or economics or even computer programming that can help. But I think over time we’ve also, I’ve revised the curriculum in a way where if you don’t have that, it’s not going to be a big impediment for you to make progress. And quite often, frankly we see, I often tell them on day one that, “Guys, this is not rocket science. Data science is not rocket science. If you are willing to work hard, if you’re willing to burn the midnight oil and put in those hours, you will excel. There’s nothing that’s going to stop you.”
Do you ever tell people, “Look, I’m going to admit you, but I advise you to take this in this class before you start the program”? [14:12]
Sometimes people ask me, and I’ve said that. Our program starts in May and people start to get admitted by December of the previous year, so they’ve got about six months. And if they asked me for advice on, “How should I best use my time?” I often say, “Why don’t you take online today? Education is totally democratized now. So you can get a certification program, like Harvard has programming in Python. Top universities now have these certification programs, so consider that.” And many of them do.
What kind of experience do you like to see? You mentioned computer programming but what about healthcare data analytics, military data analytics, insurance data analytics? There are all kinds of data analytics. You don’t really care what industry it is as long as they have some relevant experience? [14:55]
It’s funny you mentioned all three. We do get people from all three. We do get military veterans, we get healthcare professionals, we get insurance professionals. So what I tell them on day one again is, “Look, you may have picked up some skills in your domain, but what we are going to teach you are cross-functional skills where, based on those first principles, now that you’ve learned, you can apply them in any data set.” So literally, we are agnostic to the industry because the way the curriculum is structured is we are going to give you this portfolio of hammers and nails and knives and all sort of thing, and you can now then sculpt any data set the way you want it to.
I noticed that neither the GRE nor the GMAT is required, but may be requested of applicants. Who is likely to be requested to take the test? And does the test score play any role in the awarding of scholarships, specifically the Dean’s Scholarship or the Yellow Ribbon Program for US veterans? [16:00]
I would say, first of all, we do not require GMAT and GRE as part of admissions. I think standardized tests are usually helpful for the admissions team to assess if the candidate is academically ready. This is a very rigorous program. You have to put in 25, 30 hours every week beyond your actual work hours. But what we have done is we’ve realized that there are other data points that can be used to assess someone’s academic capability. So like undergrad degrees, graduate degrees, any certification, any coursework. So we also, like I said, because it’s a professional degree program, we look for your prior professional experience. And if you have a couple of years of relevant experience, that does help.
And then I think you asked about scholarships. Typically, when you’re awarding merit scholarships, we look at the whole application pretty holistically and we would then decide to award scholarships to those people who are, we think, are going to be a strong addition to the overall cohort based on their professional background and experiences.
There’s an essay that’s a required part of the application. What are you looking for in the essay portion of the application? [17:27]
We are really trying to get to know the applicant and learn more about their background, professional goals, and really the reasons for why they want to pursue the MSBA program because we do get a very diverse set of people. And it’s also international, we get people from other parts of the world as well. So I think we are trying to keep in or maintain or enhance a level of diversity in the cohort. I think we are looking for people who are truly excited to invest and learn in this program. We want them to build relationships with not just faculty, but also their peers, and basically contribute positively to this program. And last but not the least, if you can demonstrate in the essay that you truly have a passion for data, maybe you did a project using publicly available open source data, that’s like the icing on the cake.
What can applicants expect if they’re lucky enough to be invited to interview? [18:24]
I personally don’t do the interviews, but in our admissions team, led by Neha Singhal, she does it. What they have told me is that the interviews tend to be like a two-way conversation, where they’re trying to get to know more about the applicant, and the applicant is trying to get to know more of the program like, really is it the right step? But sometimes there are a handful of situations where people have not really thought through what they want to do with this degree program. And I think it’s appropriate for the admissions team to counsel them and say, “Look, if you haven’t thought through, maybe you want to think about it more, maybe come back next year.” So I think they treat the interview more like a formal job interview. So they’re going to be asking questions about your background, accomplishments and goals, and then we will want to assess how the MSBAI curriculum actually fits with those goals. And it’s usually about 30 minutes or so.
Thinking of goals, what are the typical positions that graduates of this program go into? Do they have access to the Career Center at Stern? [19:31]
Great question. So because this is not a full-time program, they don’t have formal access to the career center, but we often send them, like if the career center sends event information about events, like Jamie Dimon from JP Morgan comes and does a fire chat with one of the faculty. He is obviously a world-renowned figure, so we would inform our MSBA students about that sort of fireside chat. So we keep them informed, but there’s no formal relationship with the career center.
Do graduates of the program typically just advance in their current positions or do they change companies and roles? [20:13]
Great question again. We find both. So sometimes the folks who are slightly senior, generally I would say they’re using this degree program to advance in their existing organization, like climb the ladder, so to speak. Younger people, oftentimes because age is on their side, they often try to experiment. So they might have spent a couple of years in retailing and realize, “Okay, I want to move to banking or move to consulting.” So I do see that cross-industry migration. Typically, it’s on the younger students’ side.
What do you think of applicants using AI to write their application? [20:56]
Personally, I’m all in favor of using AI tools as long as you disclose it. And so that’s something we have mandated also in the program curriculum that I have not stopped students. And I’ve basically suggested to faculty, faculty have their own mind, so I can’t impose it, but I basically told them, “Look, the real world, these guys have access to AI tools. This is only going to get more and more ubiquitous. So why make this artificial distinction between tools that you can use in the real world but not use in the program?” What I’ve told our faculty is, “You should revise your coursework so that they can actually use the tool and then you can assess them in a way that makes sense.” So I think in the application too, look, if you’ve used it and if you’re asked, just be honest. And we are not going to hold it against you.
I interviewed Dr. Robert Salomon, who’s from NYU Stern Abu Dhabi. We were discussing AI and he said that he has a question he likes to ask applicants, “Here’s a response to a question I asked AI. Tell me what’s wrong with it.” [22:00]
Yeah, that’s smart. I like that.
I thought it was pretty smart too. And I don’t think it was a take-home exam either. I thought it was pretty clever.
You just came out with a new book. Can you tell us a little bit about it? [22:29]
Yeah, thank you for asking. So first of all, there is a direct connection between the book and the program. And if you ask me how, it turns out that the curriculum that I designed for the program is actually based on the framework that is there in the book. And my co-author and I, Ravi, he also teaches in the program. We started writing the book four years ago, and both he and I have spent a lot of time consulting with companies. So I think collectively we spend, maybe we work with almost 100 companies at this point, and that framework is so well tested now across dozens of companies, so I felt very comfortable and confident changing the curriculum based on the framework. And so that’s a connection of the book with the program.
Now, the book is called Thrive: Maximizing, Well-Being in the Age of AI, and we wrote this book because we were really tired of the negative narrative around AI, and we thought it is really prudent upon us to balance the narrative and tell the other side of the story. And nobody was doing that because good news doesn’t catch attention; bad news sells.
So we’re like, “Okay, so what? We are seekers of the truth, so we are going to tell what the true story is.” So that’s the background of the book.
Can you give us an example? [24:03]
Absolutely. We talk about how AI can improve your physical health, mental health, and I can give you examples in dating, relationships, work.
I’m past dating, let’s start with health. [24:15]
I have two favorite stories. One is based in the US. With an increasing use of granular location data, first responders, 911 medical professionals when they can improve their response time by one minute in a densely congested city like New York City, they have been able to save 10,200 lives, people who are just going through heart attack or a brain stroke. That’s an example of AI was used on location data to predict, are you on the 19th floor of this building or the second floor? 17 floors, one minute makes a difference in your life.
Wow, that’s amazing. [25:00]
Yeah, that’s one. And the other one very quickly is out of Hungary, Budapest, there are these cancer detection clinics for women in Budapest. And turns out that last year, 63 women who actually had cancer, the human radiologists could not detect their cancer, but the AI algorithm that was unleashed on the data, was able to detect it. So 63 people owe their lives to AI.
That is very impressive. Is your book about the benefits of AI exclusively, or is it also about some of the risks and how to handle them? [25:29]
We are very clear right at the beginning that, look, we are not dismissing the risk, but there’s like 10,000 books that have talked about the risk. And so we are cognizant of the risk, but we want to dedicate the book to the positive impact, the positive stories about AI.
Thank you. What question would you have liked me to ask you that I haven’t asked? What would you like to answer or discuss that I haven’t raised? [25:28]
Well, I think we just talked about it. I would’ve asked you to ask me about what motivated me to change the curriculum. And I think simultaneously writing on the book and the real world experience and the connection between the framework in the book and the curriculum is what really motivated me.
Relevant Links:
- Masters of Business Analytics and AI Program
- Applying to Graduate Engineering Programs: What You Need to Know
- Application Advice from Accepted Data Science Clients
- Seeking a Graduate Degree in Artificial Intelligence? (accepted.com)
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