Getting a Masters in Business Analytics

Being a business school professor, I often get asked for advice about applying/getting into graduate school, most often about getting a Masters in Business Administration (an MBA).

More often than not, these queries come from 22-24 year olds who have recently finished their undergrad degree, or are just about to. Many of them want to get a masters before they enter the workforce or soon thereafter. Sometimes this is out of necessity (e.g., they couldn’t find a job with their undergrad major) or they have some technical degree (e.g., engineering) but want to work in a more business-oriented or management role.  My advice to them is often disappointing to hear. Students straight out of undergrad, without significant work experience, are at a disadvantage for traditional MBA programs. The most competitive students at top MBA programs not only have great academic backgrounds, but also have 4-5 years of progressively demanding work experience, sometimes at great firms or startups.

In recent years, some business schools have responded to this pent-up demand for management education by developing pre-experience generalist management masters degrees (e.g., a Masters in Management Studies) or specialized masters in a specific management area (Masters in Information Systems). Some schools, like Carnegie Mellon’s Heinz College have built highly successful programs like MISM that specifically cater to this high-talent/low-experience quadrant. Runaway successes like the MISM program in the specialized master’s category however are rare, and have historically been located outside the top business schools—e.g., the MISM was originally Carnegie Mellon’s Public Policy School—or at less prestigious business schools.

This is all beginning to change. Some of the world’s leading business schools are now coming out with their own specialized masters degrees, and for bright and enterprising recent grads, these degrees might just be the perfect option. One genre of specialized masters is especially hot today—the Masters in Business Analytics.

Why a Masters in Business Analytics?

The field of “data science” seems like it appeared out of nowhere, but now everyone wants to “get into data science.” The data science buzz is everywhere, see this recent Forbes article.

What is data science? It is basically advanced statistics applied to (usually) corporate data in order to improve firm performance in one way or an other—e.g., human resources management, strategy, advertising, product development, customer support, etc. Great data scientists have both the technical chops but also good business sense—and know how to squeeze out actionable insights from messy company data. The competition for great data scientists still appears to be quite fierce and even top tier companies struggle to find data science talent.

The Degree

The Masters in Business Analytics (or other similar Data Science + Business Management) degree is an interesting option for students considering this career path.  It combines the core skills from a traditional MBA with a specialization in data analytics.

Coursework in a Master of Business Analytics degree would include: learning about software such as R, Matlab or Stata, Optimization and Management Science, Probability and Statistics, Data Mining and Machine Learning. Students are often required to also take, in addition to these technical ones, courses in leadership, economics, finance,  or marketing. Most programs also have a “hands on” project where students apply their skills to a real-life data science problem.

Since this is a new category for most business schools, there isn’t much data available about how hard it is to get in, nor what the placements are. But a few schools have published class profiles. UCSD’s Rady School, for instance, has a MS in Business Analytics with an enrollment of 45 and an average GMAT of 715. Just to put that in context, that GMAT score is as high as venerable full-time MBA programs such as Columbia U (717), MIT-Sloan (716) and Berkeley Haas (715). What is even more striking is that UCSD Rady is a fairly new (though its faculty are leading lights in their disciplines) and it was able to attract such high quality talent for a new program.  Similarly, Carnegie Mellon’s MISM program’s Business Intelligence and and Data Analytics track  has a median GMAT of 710. 

Of course, getting a degree is a very personal choice. A given program might not be the right fit for everyone. If you have interest in “big data” and want a technical+management job, the other MBA might be something to explore. As is the case with any big/expensive move, I would trust programs that make available data about the incoming class, placements, and the faculty that teach these courses.

Below, I’ve gathered some links to some programs across a range of universities. I’ve probably missed some, but the list below should help you get started on your search.

(PS, I am not being paid to advertise any of these programs.)

Some programs to start your search.

Arizona State WP Carey – MS in Business Analytics
Babson Olin – MS in Business Analytics
Carnegie Mellon (Heinz) – MISM Business Intelligence and Data Analytics
Case Western Weatherhead – MS in Management-Business Analytics
College of William and Mary – Master of Business Analytics
Duke Fuqua – Master of Quantitative Management
Emory Goizueta – MS in Business Analytics
Georgia Tech Scheller – MS in Analytics
Maryland Smith – MS in Business Analytics
Michigan State Eli Broad – MS in Business Analytics
MIT Sloan – Masters of Business Analytics
Notre Dame Mendoza – MS in Business Analytics
NYU Stern – Master of Science in Business Analytics
Purdue Krannert – MS in Business Analytics and Information Management
Southern Methodist University – MS in Business Analytics
Stanford MS&E – Masters in Management Science and Engineering
Syracuse Whitman – MS in Business Analytics
Temple U Fox – MS in Business Analytics
U Cincinnati Lindner – MS in Business Analytics
U Colorado Boulder – MS in Business Analytics
U Conn – MS in Business Analytics and Project Management
U Iowa Tippie – MS in Business Analytics
U Massachusetts Isenberg – MS in Business & Analytics
U Minnesota Carlson – MS in Business Analytics
U Rochester Simon – MS in Business Analytics
U Tennessee Hallam – MS in Business Analytics
U Utah Eccles – MS in Business Analytics
UC Davis – Masters of Science in Business Analytics
UC Irvine Merage – MS in Business Analytics
UC San Diego Rady – MS in Business Analytics
UCLA Anderson – MS in Business Analytics
USC Marshall – MS in Business Analytics
UT Austin McCombs – MS in Business Analytics
UT Dallas – MS in Business Analytics
Washington University Olin – MS in Customer Analytics

PhD Student Stars

After I defended my PhD dissertation in March of 2010, I decided to send my friend Chris (who is a star Informatics professor now) an e-mail summarizing what I had learned during that experience. As I read this email 7 years later, there is little I would change about the advice I would give to a new PhD student. Indeed, I give very similar advice to my own students, some of whom are now professors at great universities themselves.

Here is the text of the original email:


So, now with a PhD (well, enough signatures to get me a PhD) in hand. I thought I should perhaps write some of my thoughts down about what I learned throughout the process. Primarily, I learned that “research” is much like any other job, perhaps even akin to making “widgets” in a factory. There is a process. Although I haven’t figured the entire process all out yet, particularly the publishing part, which is now going to be the primary interface between me and the production of widgets, I think I have come up with an outline for a theory.


Before I started graduate school, even before I started my MS (I think), I read the website below. It gave me the best advice in terms of a general framework about how I should think about acting/behaving during my time in graduate school. I believe it was what helped me get admitted, finish, and find a job.

I would recommend any graduate student read it and take it to heart. When I started graduate school for my master’s degree, I tried to model myself after these suggestions. Though others might argue otherwise, I think, for the most part I worked an average of around 5-6 hours of real work per day, for at most 6 days a week – putting peak times aside. I mostly worked at school. I think most faculty knew my name and I personally asked almost all faculty to come to my presentations.

I expect to work significantly harder during my faculty job. Raising the average real hours worked a day to 7 or at most 8.

Some observations about “poorly” performing students:

  • The students who do the worst in graduate school are not present on campus and in the office on a regular basis. This conforms with the visibility hypothesis. Being on campus is important. First, you work. Second, you can talk with other students to resolve your problems. Talk to faculty and be a part of the intellectual life of the place. That means attending talks, giving talks. Even the “mindless” chitchat often contains important pieces of knowledge, gossip, tips and tricks, linkage into important networks that will provide guidance and encouragement during your PhD and beyond.
  • Students who perform poorly often reinvent the wheel. They do not take good advice from others – both explicit advice and what would I consider “implicit” advice (e.g. modeling yourself after the best of the cohorts above you.)
    • This includes writing papers. The structure of research papers is quite standard. This includes how to write introductions, results sections, etc. However, it also consists of due diligence on statistical procedures, etc. I have learned this through trial and error. But I often look at other good papers that try to do “similar things” (broadly defined) to see what types of other tests, etc. I should do before I wrap up my paper.
    • It also includes presentations. Particularly glaring is the absence of students at other people’s presentations. I am often surprised by this since academic output consists of two tangible products: papers and presentations. Just as writing good papers requires reading good papers, giving good presentations requires going to good presentations. And much like how writing good papers requires the ability to take and give productive criticism, so does presentation.

  • Read. I am often just in AWE of students’ lack of knowledge in their own field of study. I have encountered many students who are totally unaware of the basic – that is core – papers or ideas in their field. Not that I am the most well read person in the world or even the program, but I do work quite hard to keep abreast of recent literature (less so these days), the news, and the classics (putting a lot of time into this right now.) Reading and digesting the literature puts ideas, especially theoretical ideas, in context. Reading is important, as is remembering what you read. We all make mistakes. I might cite Smith’s 1975 paper, while it might be Smithe’s 1975 or 76 paper. But my “hunch” is that even when we make mistakses these are good heuristics for remembering papers, linking names to concepts (Granovetter -> Weak Ties) to era’s (1970’s) and linking these with each other into a “network” of sorts of concepts, authors, and eras. Knowing these basic things, will give individuals a good lay-of-the-land with respect to where the holes are in the research, where the interesting problems are, and where your own research can fit in. It also goes back to “re-creating the wheel.” A good knowledge of the current and past literature will give you, in addition to a better theoretical lens with which to view your research, ideas about data, about survey instruments, about methods, and about framing research as well. – A good quote about the importance of reading can be found here:

    “My first rule was given to me by TH White, author of The Sword in the Stone and other Arthurian fantasies and was: Read. Read everything you can lay hands on. I always advise people who want to write a fantasy or science fiction or romance to stop reading everything in those genres and start reading everything else from Bunyan to Byatt.” – Michael Moorcock


  • Do not wait for feedback to do work. I often notice students with a paralysis of sorts when it comes to doing anything that they have not gotten explicit directions from their advisors or approval from them for some reason. Keep playing with your data and your ideas. Feedback is slow, people are busy, and even when you do get feedback – remember that no one knows your data and the methods you used to analyze it better than you. Keep plugging away. I kind of have a heuristic about “regression analysis.” Once I get a “main effect” to be significant – I try (though, I increasingly notice that I often fail on some dimension) to do all I can to make it disappear (in theoretically justified ways of course). If I do get it to stay, then I am more confident. If it disappears, then you have to start searching for theory again (especially if you didn’t include the variable that made the effect disappear for a theoretically justified reason.)
  • Don’t listen to all the advice you get from your advisors. They are busy and they are human. Take all the comments in, make appropriate changes, and argue back when you have you. You will have to do it for the rest of your life with reviewers anyway. “Critiques” are not always correct.
  • Don’t TA too much. I see some students overload with TAs even in their 8th or 9th year (yes!). I think a manageable number of TA’s per semester is three if you got your research organized and you are in your 2nd or third year. If your research is a mess, keep it to 2 TA’s a semester. Here is a simple formula. Assuming that an average student can TA three classes per semester (not all unique) – that is 6 total classes a year earning $28,800 per annum without significantly extending their time in the PhD program. Now assume that any additional TA above this 6 TAs per year will increase the length of time you stay in the PhD program by 3 months (that’s just 1/4 of a year) and that your opportunity cost of staying in the PhD program is 80,000 (an above average salary for a master’s student). That decision to TA just that one extra course will cost you 20,000-4800 = 15,200. That is probably a low end of the estimate. Increasing the number of extra months that you might stay because of an extra TA by another month will increase this to over 20k lost. Bump up the salary… and you see the point. TAing, even if it just adds a “few” months will really hurt your pocket book. The next point is related more to time in the PhD program.
  • Little rules, big rules. Making sure you don’t break the little rules will help you make your deadlines on time. Finish your classes, your first paper, and your second paper ON TIME. That is almost like the first commandment of the PhD at Heinz. The little rules at Heinz are quite simple. These are the major milestones of the PhD here and are almost sacred. Doing this will provide you with enough structure in the formative periods of your PhD that will take you along through your proposal and defense. The more important thing coming out of finishing your FP and SP on time is that this will give you the “meta skills” to get you organized for your proposal and your dissertation. Finally, as a secondary note regarding the FP/SP deadlines is that there is an organizational memory. Everyone knows who didn’t finish their papers on time. Faculty have long memories as well. They are more lenient (with risky topics, etc.) when people make sure they obey the little rules. So, if you follow the little rules, you can break the big ones. Breaking the big rules is where the fun is.
  • Again, time. The academic job market penalizes “long” PhDs. This is a qualitative observation. Though there may be a handful of PhD’s who finished after 9 years who ended up with jobs in academia – the fact of the matter is that it is really looked down upon. Six year might be the peak of the neutral point at which it is OK not to have finished your PhD by this time, after that your prospects of landing a good academic job decline quite dramatically, and it snowballs to almost nil by 8th or 9th year.
  • Be nice to other students. Word spreads about “assholes” (this is a technical term – see Van Maanen 1978). We all have made faux pas’ in our lives. Probably tons of them. But consistent “assholeary” is bad. Be trustworthy and others will trust you and even let others know they trust you too.
  • Everybody here is pretty smart. It is not just you. It is hard work that creates the gradient on which good graduate students vary. Hard work is demonstrated by being on schedule, writing, reading, and working every week day for at least a few hours on your research (on average.) I am often surprised at how easy this is, and how some people just do not get it.
  • Know when to quit. Get real advice. Don’t stick it out longer than you have to because of your ego.