UCLA’s Master of Quantitative Economics

Do your professional interests straddle the domains of economics/finance and data science? Are you searching for a graduate school program that will teach you data science and machine learning skills within the context of economics and finance data? Would you like to learn these skills at a school with global cachet? If so, then you should consider UCLA's Master of Quantitative Economics (MQE) program!

FEATURED GUESTS:

Name: Sam Borghese

Email: srborghese@g.ucla.edu

LinkedIn: https://www.linkedin.com/in/samborghese/

Name: Shany Mahalu-Atiya

Email: smahalu@econ.ucla.edu

LinkedIn: https://www.linkedin.com/in/shany-mahalu-atiya-msod-a47831a/

SUPPORT THE DATA CANTEEN (LIKE PBS, WE'RE LISTENER SUPPORTED!):

Donate: https://vetsindatascience.com/support-join

EPISODE LINKS:

UCLA's MQE: https://master.econ.ucla.edu/

UCLA's Veteran Resource Center (VRC): https://veterans.ucla.edu/

UCLA's VRC Director: Dr. Emily Ives (eives@saonet.ucla.edu)

UCLA's VRC Contact Us Page: https://veterans.ucla.edu/contactus

PODCAST INFO:

Host: Ted Hallum

Website: https://vetsindatascience.com/thedatacanteen

Apple Podcasts: https://podcasts.apple.com/us/podcast/the-data-canteen/id1551751086

YouTube: https://www.youtube.com/channel/UCaNx9aLFRy1h9P22hd8ZPyw

Stitcher: https://www.stitcher.com/show/the-data-canteen

CONTACT THE DATA CANTEEN:

Voicemail: https://www.speakpipe.com/datacanteen

VETERANS IN DATA SCIENCE AND MACHINE LEARNING:

Website: https://vetsindatascience.com/

Join the Community on LinkedIn: https://vetsindatascience.com/support-join

Mentorship Program: https://vetsindatascience.com/mentorship

 
OUTLINE:

00:00:07​ - Introduction

00:02:47 - Guest Introductions

00:05:14 - How UCLA's MQE was conceived

00:07:05 - What makes UCLA's MQE different

00:11:11] - Why UCLA's MQE is a good fit for veterans

00:15:24 - Career paths that MQE grads are best postured for

00:21:58 - Prerequisite requirements for UCLA's MQE

00:27:51 - A thorough introduction to UCLA's MQE curriculum

00:37:59 - Character traits common among successful MQE students at UCLA

00:42:30 - Character traits common among less successful MQE students at UCLA

00:46:25 - Typical career outcomes for UCLA's MQE grads

00:54:51 - How UCLA's MQE will be evolving over the near to mid-term

01:00:49 - Sam's tips on preparing to enter UCLA's MQE

01:04:09 - Info about UCLA's Veteran Resource Center

01:04:56 - Info about the MQE's 10-week applied project course

01:10:23 - The best ways to contact Sam and Shany

01:11:28 - Farewells

Transcript

DISCLAIMER: This is a direct, machine-generated transcript of the podcast audio and may not be grammatically correct.

[00:00:07] Ted Hallum: Welcome to the data. Canteen, a podcast focused on the care and feeding of data scientists and machine learning, engineers sharing the common bond of U S military service. I'm your host at Hallam today. I'm chatting with Shani and Sam two representatives from UCLA master of quantitative economics. If you're in the market for a veteran friendly and data science relate grad school program, then UCLAs MQE should be high on your list.

And this episode will touch on everything you need to decide. If the MQ is a good fit for you, we'll cover a lot of ground to include veteran specific initiatives that make the MQ a really good fit for those with military service, we'll also disect programs, prerequisites curriculum, delivery, modes, and career outcomes.

Hope you enjoy the rest of this conversation. And here we go.

Actually before we get rolling, there is a clarification needs to be made. I had to cut a section of this episode out because when I originally interviewed the representatives from UCLA as master of science in quantitative economics program, we talked all about how it was fantastic that this program was accessible globally.

And that was all true. At the time, there were radical changes that have been made to the program starting in 2020 as a result of COVID. And the representatives that I spoke to were, confidently under the impression that those changes.

Had reshaped that aspect of the program permanently, unfortunately in the intervening time, since we originally recorded, the leadership of that program has decided that for the coming 20, 22, 20 23 school year, those COVID accommodations will be rolled back and the program will return to being in-person their own campus in Califor.

Synchronous only. So there will be no remote asynchronous option, which is very unfortunate. I know for many of our veterans who are settled with families, they've been out of the military for a number of years. relocating to California is not realistic. Similarly for our folks who are still in the service who were stationed abroad in Germany or Japan, places like that.

you know, if you've got a passion for economics and data science, UCLA is master of science and quantitative economics program is quite possibly the best program for you. But, these geographic and temporal constraints may prevent it from, being a good one. And that's very unfortunate, but if you do have the latitude to be able to relocate and attend in person there in UCLA, you're passionate about economics and data science.

Do you want to bring those two things together? then I don't think you can do much better than UCLAs in QE program. So with that now let's get rolling.

Shawnee, and Sam, welcome to the data canteen. I can't wait to talk to you today about UCLAs master of quantitative economics program.

But first I'll just give you guys each a moment to introduce yourselves.

[00:02:51] Sam Borghese: Hi, my name is Sam Borges. Um, I'm a former alumni of the MQE program and they hired me to come in afterwards and design coursework to keep the program updated and teaching the students, you know, the newest techniques in data science, finance, um, to, um, financial machine learning, cloud computing, and even just taught the first blockchain course, um, in the, uh, in the economics department at UCLA.

Um, another thing I assist with is applied project. So connecting students, um, to companies to bring in real world projects, um, and have them use their, you know, use their skillset on something that's, uh, not a pre-packaged data set, but has all of the issues, um, of a real business problem, so forth. And I'll hand it over to.

[00:03:49] Shany Mahalu-Atiya: Thank you, Sam. Hi everyone. My name is Shania. Hallowine the director of corporate relations and professional development for the MQE program. I've been in higher education for over a decade and a fun fact about me. I'm a veteran of the IDF, the Israeli defense forces. I'm also on the team together with Sarah Blewett that, uh, lead the veteran initiative for the MQE program, growing our community and investing in, uh, partnerships like this one, uh, with the data, continue to, um, contribute and, and connect with veterans in data science and machine learning, economics and finance.

[00:04:35] Ted Hallum: Very cool. Now, uh, shiny. I'm so glad you mentioned that because one of the reasons why I wanted to highlight the NQE program, bring you guys on the show is because of the uncommon emphasis that you're putting on supporting the veteran community. It's just phenomenal. You know, as the founder of the veterans, a data science machine learning community, I certainly appreciate it.

I recognize what you're doing and it means a lot to me. Um, so I want to kind of start at the beginning. When I was looking into UCLA inquiry program, I realized that had been established all the way back in 2016, which that may not sound that long ago, in some respects, but when it comes to these quantitative degree programs, many of them are only a couple of years old.

So to go back to 2016 is actually, you know, you guys have a strong footing in the space. so I'd love to hear how the program was conceived. when they constructed the curriculum. Was it just all new from the ground up, or did you guys take some existing curriculum and kind of modify it for the new learning needs of students going into this era where data is the new oil is the, you know, common expression.

so let's, let's start there.

[00:05:44] Sam Borghese: So my understanding of, uh, the program being formed was to meet this need. You're talking about data is the new oil. Every company is a data company, as you said, uh, previously. Um, and so they wanted to make a program that allows students, uh, the skill set to actually go ahead and work with data, uh, work with, you know, these highly quantitative skills, uh, work with real-world data different than most academic master's programs, right?

Academic masters still, um, are relatively theoretic. And, um, so this program was formed in order to do that. And I will say from the beginning, Um, the program has changed vastly, right? It's been a trial and error process of, okay. You know, this course in microeconomics doesn't have enough real-world examples.

Uh, it needs to be revamped. Uh, this machine learning course, we need to split it into two for, you know, the, the needs of the different students and so forth. So it's really been an ever evolving, uh, ever evolving program, uh, taking in feedback from industry professionals, taking in feedback from these applied projects we're doing, uh, to find out the skillset that is in most demand by industry.

[00:07:05] Ted Hallum: that's awesome to hear the demand signal that you guys are being that you're in tune with, and that you're reacting to what you see the needs are out in industry and from your alumni. That's fantastic. now I know from having chatted with you guys, that there are some incredible differentiators that make UCLA MQ.

Different than the vast majority of other quantitative programs that are out there. so it, if you guys could relay some of those things we talked about earlier, I think that our audience would be super interested to hear about that.

[00:07:36] Sam Borghese: Yeah. So, uh, one advantage of being a top, the top ranked public university in the country is that we have access to distinguished speakers. So there's a whole distinguished speaker series that the MQE funds when I was a student there, we had Ben Bernanki, Eugene Fama, um, and the students as well as, you know, 150 or so, um, industry professionals invited, um, can all ask them questions openly.

Um, so that's really a unique experience, just one of many, um, that offers, uh, that's offered to the students at that.

[00:08:13] Shany Mahalu-Atiya: Um, few other things that I would like to mention is, uh, that set us apart is one is the flexibility of our program specifically to allow students to, um, not only choose the. Um, of the program in which they want to graduate, it can be done in nine to 18 months, depending on the student, uh, preference, but also beyond the three, uh, uh, foundational courses, they can, uh, pick and choose some of the other courses to make the 48 units that are required for graduation.

And that flexibility provides students a really freedom to choose their path, if it's data science or finance, and then really find the clarity that many of our students are seeking. And many individuals that are pursuing graduate education or are seeking specifically. Um, what do I want to do with a degree?

What would be my career? Post-graduation what is the right company culture for me? What are my values? Right? And so that's flexibility and adjusting as you go to really find your voice and find your path, I think is very powerful and provide the type of support that many students are seeking. And, uh, we have a dedicated career theme, which I think is a huge differentiator from other programs.

Of course, there, reason UCLA career center, the students. Throughout the entire institution can seek support from, but we have an MQE career team, um, that not only like myself connect externally with companies and generate opportunities, internships, and full-time opportunities for students, but also coach students in terms of resume writing cover letter mock interviews, really preparing them to the assessment, the, uh, technical assessment, a true seminar classes as well, uh, as part of the interview.

And that type of support is really instrumental in their success.

[00:10:14] Ted Hallum: Yeah, absolutely. Because getting people equipped with the theoretical underpinnings and then the pragmatic skills, that's a huge piece of it. But then, you know, keeping that, keeping a focus on the actual goal of everybody who gets one of these quantitative degree.

One of the biggest things that they're out to accomplishes to be competitive in the workforce. educational institutions have to have some infrastructure in place to best help students do that. And a lot of schools don't have that. And so these things that you just mentioned I think are absolutely critical and there are things that people should key in on.

if your ears didn't perk up and sh as Shawnee was talking about those things, they should have hit the rewind button, a few, clicks and listened to what she said again, because they're going to help you get a job, and that's going to be critical at the end of your educational experience. Now, I think for the next part, you know, I've done a number of these educational episodes on the data canteen now.

And I try to always stress to our veterans that a key thing in their, happiness and their experience as they go through a program like the MQE is to make sure they're going someplace where they're not just going to be tolerated, but they're going to be selling. Um, because that's the type of environment in which all of us humans thrive.

We thrive where we're celebrated, not where we're tolerated. so I'd love to turn it over to you guys to just tell our listeners for veterans specifically, why is UCLAs MQE a great fit. Yeah,

[00:11:45] Sam Borghese: well, this initiative has been moving so fast and, uh, sort of ballooned within our program, uh, because every one of the faculty knows that veterans are some of the best students, they're leaders in every class, in every project.

Um, they ask the best questions. Um, they're, they're really there to be a community member as well, help other students and engage, uh, with the faculty and, and, uh, their cohort. Um, so I've been, you know, impacted by veterans in my courses. Um, Shania has as well, so I've jumped on as well as most of the faculty.

Um, into, you know, uh, trying to get more veterans interested in the program.

[00:12:34] Shany Mahalu-Atiya: Yeah. And to add on that and thank you for that, Sam it's, first of all, it has been so much fun. Just connecting with the veteran community veterans and active, um, members, right? Active duty service members. It has been a celebration of the service of the skills of an incredible community of people that are so dedicated and then giving back and supporting individuals in rethinking or thinking about the next stage of their career and providing these opportunity to connect with our community.

We have a monthly lunch and learn that we're hosting and Ted, thank you so much again for being our guests. Uh, we have one coming up these Friday, um, March 11th. We also, uh, participate in the service academy career. Conference. I just came back from Florida about a week ago and met with so many individuals.

And I can tell you that the learning for me each with these initiatives is that, uh, in order for us to grow our veteran community, we need to be key players in the space. We need to connect with all the employers and all of them were there from PWC to the law, to Ernst and young and Amazon and Microsoft, right?

Huge name companies looking to specifically hire Amelie theory, um, veterans and active duty service members. And then the program should be definitely the pathway into all those companies. And then seeing the connection and starting to connect with the academy and the Naval academy. And I work at the heart of this conference, um, I think is really exciting.

And the program is suited to provide the support and we understand that the end goal. Is his career, his jobs fried, and therefore we're here. I was hired and we're building this big theme and we creating a big community of individuals that will specifically support the individuals in, in lending those great opportunities.

So back to your question, I think the answer is, um, why, um, why here and how will veterans be celebrated is really true. The appreciation of their skills specifically, and the resources that are being provided to allow for that success because you cannot do it alone. You, you, it takes a village to find the next opportunity.

[00:15:06] Ted Hallum: Absolutely. So I appreciate you guys humoring my questions about how the MQE got stuck. How it's evolved to be the program it is today. And I think we've well-established the relevance to veterans and the, all the extra stuff that you guys are doing to support and serve the veteran community there with the MQE program.

Um, so the next thing I'd like to talk about is I'm sort of uniquely positioned, with my role in veterans and data science and machine learning, to talk with newly transitioning service members who are getting out of the air force or the Marine Corps or the Navy or the army. And they've learned a little bit about data science or machine learning, but the array of educational options for them to upskill is a bit overwhelming.

So they hear about master science and data science programs, master of science and data analytics programs, master of science and business analytics programs. And then you guys are a master of science in quantitative economics. So. What I'd like to establish or help our listeners with is each of these different types of programs exist for a purpose.

They help students to get best prepared for different nuanced career paths. So when it comes to the MQE program, what career paths do you guys know that your students are better postured to tackle than students graduating from other types of degrees? Yeah.

[00:16:33] Sam Borghese: So being underneath the umbrella of UCLA economics program, we're able to leverage all the economics, faculty, um, and, and coursework.

So really what it creates is students who can, uh, move into almost any industry of. Um, so they can move into finance, they can move into consulting, they can move into data science. And we have two tracks, um, inside of our program as well, a data science track and a finance track. Um, and so students can, can fit, um, their learning into which industry they want to go into.

Um, so that's something that a lot of other programs don't have, um, where it can take two students in our program and to have a dream goal of being a quantitative analyst in New York. And another has a goal of, um, you know, working in economic policy and litigations, and they can both go into our program, um, and gain the skill set required for each of those.

So that's something I don't see in programs just primarily focused in business analytics or in data science. They don't get, um, you know, the, the economic, uh, background or finance background. Um, that our program really has

[00:17:56] Ted Hallum: access to. Awesome. So what I understood you to say is for most of our listeners who are tuned into this podcast, because they're interested in data science and machine learning, they're probably going to want to pick that data science specialization within the MQE, and then that's going to prepare them to do data science in the realm of finance and economics specifically.

I mean, they could, they could use it to hit pivot into other things, but probably, there would be best prepared for data science roles in that arena. Yeah. Um,

[00:18:27] Sam Borghese: so there's, there's other students too, uh, who we've interviewed and they go, I want to do anything but finance and they never touched finance the whole way through, but they're still given economic knowledge, you know, economic, economic background, whether that.

You know, uh, how, how businesses work, right? The economics of certain businesses. And, uh, it's a deeper level knowledge. I would say Shania works with a lot of students. She could speak more to that. Um, you know, the amount of students who go through and just focus specifically on data science and mission

[00:19:01] Shany Mahalu-Atiya: and learning.

Absolutely. And so it is, um, close to 40% of our students are looking, um, to get into data science specifically and not necessarily in finance or economics, but just anywhere and today, uh, as all of, you know, any company, any industry really seek that, the skills that the data scientists. Possess. And so, uh, from, um, hospitality to technology, to, um, the financial industry consulting, uh, the private and the public sector, we work with governmental agencies as well as, um, nonprofit organizations and six those skills.

So I'm overwhelmed with, uh, positions that really, you know, kind of, uh, come my way and they seek MQE students to submit an application. If the student has, um, the skills necessary again, to pass the assessment where the interview, they are. You know, most likely to get the offer from the company and a lot of companies, you know, the, the average, um, for roles that I'm bringing to the table, they request one year, two, maybe three years of work experience in the space.

And a lot of them, if not, all of them require a master's degree. And so it's really, uh, important to advance the education and to pursue, uh, the master's and seems that you see less. MQE you can finish within nine months and be ready to apply for those roles. I think this is really exciting that the timing invested in gaining the skills and developing oneself should not take more than nine months if the individuals is really ready to pursue these kinds of opportunities.

[00:20:51] Ted Hallum: Yeah. So the flexibility that you guys highlighted of this program, I think is phenomenal. I think a lot of veterans need that and want that. So I think that's key. And then also I'm so glad that you hit on that last bit Shani because you know, it is, is it possible to go out and learn a lot of these skills, if not all of these skills own your own, if you're a highly disciplined person.

Yes. I think you can do that, but you have to face the reality that we still live in a credentialed society. It is, even if you go out and master these skills, there are going to be places where we very difficult, if not impossible for you to land the job that you. Just because you don't have the credential that their HR department says you have to have.

So is that fair? I don't think so, but it is the world in which we live and we have to be realist. Right. and so if, if you don't possess a master's degree, I certainly think that it's something that, that we would all be remiss if we didn't tell you that you need to seriously consider doing that because it's just something that the industry generally requires.

So now, as we start to, I call it, peel back the layers on your program to suss out all those little bits that people will want to know as they're trying to make that hard decision as to what quantitative program is right for them. I think the first thing that everyone comes up against is what are the programs prerequisites.

And can I meet those prerequisite criteria? So what are the pre-recs here for UCLA incubator?

[00:22:26] Sam Borghese: So currently there's no prerequisites except for a undergraduate degree, but we take in all different types of students, right? There's engineering students who have come in there's math, students, stats, computer science, um, you know, stem oriented students are, are ones who typically come in.

Um, but there's, there's no prerequisites. And, uh, one thing to get all the students up to snuff with coding ability and math ability is we have a bootcamp over the summer, um, and intensive boot camp. All the students come in for six hours a day and just code. Yeah, pretty much learn how to program and learn that are required math skill.

So, uh, any student could come into the program, obviously they're going to be at an advantage if they come from a math background, um, from an ops background, but currently there's no prerequisites. Um, there's also no courses that can be accounted for, with undergraduate coursework. So if you took an econometrics, uh, even a graduate level econometrics course that they let you take an undergrad, uh, that cannot be counted towards the required courses.

So all the students have to go through the same required courses and then fulfill the same units. Um, there's no Coursera courses, um, you know, that, that they could do to, to be able to satisfy those, although highly recommended, right. It is a grind to come in and do the bootcamp. If you've never. Never coded at all.

I probably over half the students do that, if not more, but students that have some base level programming experience, um, or econometrics background, uh, are definitely at an advantage, uh, for understanding the concepts

[00:24:20] Ted Hallum: quicker. Awesome. Yeah. So you actually got to my next, my next question was going to be about own ramps and bridging gaps.

And it sounds like while it would be, you said it'd be a grind. Um, but Hey, let's be real. They decided some machine learning is a grind for most of us. Um, unless you're just coming from like a physics undergrad or, you know, something highly technical anyway, um, my next question was going to be for people who come from a non quantitative background, maybe they have a liberal arts degree.

Do you guys bridge that gap? And it sounds like the boot camps, while it may be intense, they can do that. So if you come from a completely non quantitative non stem background, you guys have on ramps in place to get people. Up to speed where they can manageably get through the inquiry program, which

[00:25:08] Sam Borghese: I think levels the playing field, or I should say lowers the barrier of entry for somebody to get into data science and machine learning.

New could have taken an undergraduate degree where you realize this isn't what I want to do. I want to get into, you know, creating models, right? Or, you know, providing tons of value to businesses, data, and you can go directly into a nine month master's and leave this program, uh, with the skillset and the certification to get.

[00:25:38] Ted Hallum: Absolutely. I find that so often with veterans, they have an undergraduate background in something different they've since learned about data science and machine learning, they know it's something that they want to do, but then they feel intimidated or they come into our, in the veterans and data science machine learning committee, and they say, is this even in the realm of the possible?

And we like tell them, absolutely. And then we like to point them towards programs like yours, that can help, equip them and get them to where they want to go. so we talked about, prerequisite courses sounds like that barrier is very low because you guys have these boot camps in place to help establish the skills that people need.

What about like GMAT GRE or those tests required? Are there ever any, if they are there ever any waivers to that

[00:26:23] Sam Borghese: the GRE was waived for COVID? I don't know if it's back in effect, so

[00:26:29] Shany Mahalu-Atiya: yeah, so, um, we, we do require the GRE and there are no way. Uh, it was waived during COVID because of some challenges with the testing centers that, um, but, um, last year, and this year for the recruitment cycle, the GRE was required.

And I also like to make a comment about quantitative skills in general, regardless of your bachelor degree in your major are being assessed throughout the admissions process. So the GRE will provide that, uh, insight in terms of the capability of the individual to try in our environment. And we want to make sure that the students will be successful and that's what we're looking at.

And then we also take a look at the GPA specifically to see the academic success and the, uh, likelihood of the individual to continue to be successful and to try to graduate school. So while we're not specifically looking for individuals with the. Prerequisite or skills or a specific class, a bachelor's degree.

We do assess the quantitative skills and the likelihood of the individual to be success in this environment.

[00:27:41] Ted Hallum: Sure, sure, absolutely. it would make perfect sense that you wouldn't want to admit a student and then kind of set them up for failure because they, they wouldn't be able to succeed. Yeah, for sure.

shifting gears to talk about the actual curriculum, I'm going to throw up a little banner at the bottom of the screen here, um, just to kind of help you all out as I ask these questions. So the first question is about essential technical foundations, um, and I've put, put them across the bottom of the screen here.

And so the question is what technologies are students. For specifically for data wrangling data, visualization, coding, version control, and modeling, to make sure that they are fully equipped and in the area of what they'll need to do technically once they get out into the workforce.

[00:28:27] Sam Borghese: Yeah. So the program, uh, the program teaches a variety of technologies for each of these, just to touch on a few. Uh, the program goes very deep into API APIs, which, uh, a lot of other programs don't even mention, you know, and APIs are how we're interacting right now. I'm sure. And, uh, interact with.

Throughout your daily life. So I'm using APIs for getting tons of data, cleaning it inside a Python. Uh, there's also a VBA taught for that Excel data. Visualization dashboards are taught in Tableau power BI, um, Excel dashboards as well. Um, also in Python using mat plot Lim, um, our GG plot so forth. So really when you go through and you look through, uh, jobs, you'll see probably a hundred different softwares close to no exaggeration, a hundred different softwares offered across jobs.

Uh, AWS, right? There's so many different required. Um, that one series of courses I designed was really just to bolt. The, the number of softwares that students have had exposure to and access to. So whether you're going into a company that uses Tableau or uses power BI or users are uses Python, um, you'll be able to say you have experience with that.

You have a deeper level knowledge with one, but as anybody who has worked across multiple BI softwares, um, they'll know that there's an easy transfer of skills. You get, you get very detailed with one, um, and you'll be able to transfer it to another one, much easier, excuse me, much easier. Um, CQL is also something we have an entire class on, uh, SQL and data management.

Um, so that goes right. Very deep into how to, how databases are even constructed. Um, how you would construct one from scratch with your own data. And then querying them, we even speak about more exotic forms of data, right. Uh, so working with like Neo four J uh, which is, uh, you know, a way to model data, um, similar to semi-structured, uh, maybe too detailed for anybody interested in data science, but, um, who doesn't have a background, but these are, these are all softwares that we work with, um, for coding data, wrangling data visualization, and then deeper into version control, get, and get hub is taught as well.

So in the applied projects, all the students work with get in, get hub, um, and inside of the course, um, apply data management for economists. Working with get hub is taught there and actually their homeworks are submitted through get hub so they can start building a repository, um, inside of GitHub, uh, Which is great for jobs as well.

A lot of, uh, data science jobs will actually ask for your get hub. So the fact the coursework integrates to already build that out for you. Um, you know, adds to the student's, uh, profile, right? When applying to jobs, um, in the same way, uh, the SQL course mandates you to get a certification. So you have to get assertive, a SQL certification, um, attached to your name, uh, to complete the course, right?

It's just another marker. Right? We live in a certification economy is so, um, it's just another marker that, you know, you're doing at the same time as you're recording. Um, so you not only have this masters and, uh, this course, but you have a certification, um, modeling all speak on, you know, a very exciting modeling technique.

Um, I just taught a few weeks ago, which is AWS's auto ML, uh, auto AI. So what you do is you take your data, you can take in a very large data set, you put it into AWS, which is a cloud computing platform, right? And, um, you say your output variable, and it will run over a hundred different machine learning models, compete them against one another, uh, find exact parameters that would be best.

It uses cross validation. So, you know, you're not over-fitting. Um, and then it'll spit out. And it'll say, this is the best way to predict, you know, loan default or whatever it may be, um, after training all these different algorithms, right. XG boost. And, and so, and I remember when this came out, I was a student in the programs three years ago and we were all worried, we'd go, oh, is our skill set going to be obsolete?

Right. They just built this AI, that's basically an AI data scientist.

[00:33:35] Ted Hallum: It'll run

[00:33:35] Sam Borghese: all the different models and compete. And I said, you know, eventually Python will be outdated. Eventually everything we learned will be outdated. So the key is to learn these new softwares and really get the skill of being able to take in the newest data science skills, um, understand how to apply them, um, and continue from there.

So, like I said, in the beginning, our program is constantly changing. Um, but. Those are just a few of the, uh, specific texts softwares we do in data modeling, coding, data visualization,

[00:34:14] Ted Hallum: et cetera, Sam, that was a phenomenal rundown. So three big takeaways that I took from what you said a couple of times, I heard you mentioned data management and I think that's huge because a lot of programs don't get into data, government governance and data management.

also what you said previously about, the relationships you built with companies to get real-world projects and messy data, and then integrating that with what you talked about with version control, where students are learning to actually do those. Projects in a realistic way where all their code and everything is being versioned.

And then having that in a GitHub portfolio that they could then show at the end of the program when it's, you know, the rubber meets the road and it's actually time to go out and start their job search. I think all of that is absolutely huge. now, as far as the curriculum structure, I know from going out the website and looking at the way everything is set up there, if I understood correctly, there's three required courses.

And then there is a list of approved courses that students can take to meet the remainder of the credit hour requirements. And so you might've mentioned it earlier. I can't remember it's a substantial 48 credit hour requirement. but given that there's only three required courses that gives a lot of flexibility from that Allah carte menu that people can pick courses from.

so obviously the flexibility is awesome. But then, you know, kind of the double-edged sword of that is how do you guys go about ensuring that if there's an employer and they hire five MQE graduates that each of them is going to meet, you know, the baseline ability to do data science and machine learning.

Does it come back to that specialization that you mentioned earlier? Or, you know, just how do you guys get that, that standardization among your graduates and a program like this?

[00:36:13] Sam Borghese: Yeah, so there's two pieces to it. One, the bootcamp and the required courses, um, are very difficult, right? They, they whipped students to a level of econometric, uh, background, data science background, and coding ability, uh, that is standard across the program.

Um, and it's sort of like the launch pad for all the other courses. Right. So that's, that's really the base level, um, knowledge that all students need, uh, specifically this four 30 course, uh, you know, students hate it when they're in it. Right. But by the time they get to the end of it, they're like, wow. I learned an insane amount of information, um, in a very short period of time.

And it becomes most of the, you know, most of the students, uh, favorite course by the end of it. Right. But when they're inside of it, they're, you know, busting 40 hours a week just on one single course. Um, it doesn't feel that way, but they get to the end and then all the students have these base level skills.

Um, it really does get down into yeah, the. Uh, the concentrations, right? So if a student has a concentration in data science, they're going to have to take a machine learning class, right. We offer several whether a student wants to go into the details and code up the gradient descent algorithm inside of, um, neural networks, or they just want, okay, here's some data run, some scikit-learn models, um, you know, visualize them.

They'll be able to, uh, fit to whether they want to go into consulting or a way more technical role, um, outside of the

[00:37:59] Ted Hallum: program. Perfect. Now, so we've talked about prerequisites and then we've done a good bit of dissecting the actual curriculum and what people could expect to get from the program in terms of skills. The next set of questions that I want to throw at you guys. Are in reference to character traits. So I know you see a lot of students go through the MQB program and there's probably some things over time that you've been able to take note of that students that exhibit certain traits or characteristics or proclivities tend to be the ones that do the best in the MQE program.

So I think as our students look in the mirror and make an honest assessment of themselves that knowing what those traits are, would be helpful.

[00:38:43] Sam Borghese: Yeah. I would say the largest trait that makes students succeed is persistence, um, and not giving up, right? Because with a lot of these projects, they're gonna run into errors, repeated errors.

They're going to have to be trying stuff that doesn't work over and over again. You know, the majority of the projects are not pre-packaged datasets, right? They are, um, difficult problems where they're going to encounter. Issues, um, where there may not even be an answer, right? That's it, that's an incredible struggle that students have to go through.

So, uh, students who don't get discouraged easily, um, are the ones who succeed in the program, right. Uh, students who, who get discouraged give up, um, those are ones that, that fail very, very quickly

[00:39:36] Shany Mahalu-Atiya: From a career standpoint, I can say that the traits that I'm seeing for them to the ones that land great opportunities outside of the classroom, uh, I mean the job market are, and that, you know, did the cation, um, And drive specifically to go out there and meet with people and make themselves known and a network.

And it tends on our sessions. We have career programs that are a part of the program. They show up, they turn on their videos. You know, there are the 10% that will turn on and show their faces that are scraped questions, you know? And so I've been in the field for over a decade and th the ones that succeed in graduate education and find great opportunities, they've done something different.

You know, they were the first one we kicked off the program, these fall in early September. And before the bootcamp I had the students already came to my office and he was in a suit and a tie and he was. Nice to meet you. I am going to find a great internship in investment banking. I was like, okay, I got to you, you know, and, and I have to tell you why he has an internship and it was maybe November that he signed the offer.

And so you need to have that drive and the dedication and willingness to do the extra work. And I think it's somewhat similar to what Sam said about, you know, not getting frustrated with the process because career justice, the data sets could be very frustrating. You know, the rejections of they're applying to so many opportunities and, and obviously they'll get more, no, you know, nos and no thank you.

But no, thank you then, um, offers, right? And so I keep telling them, this is a numbers game. All you need is one offer, right? And so keep going and don't take it personally. And the ones that are willing and have. Uh, characteristics, they're Uber successful and they land opportunities with huge brand name companies.

[00:41:43] Ted Hallum: It's awesome to hear your perspectives on that question, because having been in the military myself and now I'm in the veterans and data science machine learning communities, I interact with other veterans on a daily basis. I know that on average, across the breadth of service, military service members and veterans, two of the biggest traits that are most commonly exhibited are determination and resilience.

The ability to bounce back from failure, you know, when we do fail. And so having heard what you just said, it comes into focus, and I really can understand why you guys are doubling and tripling down on trying to, trying to serve the veteran population and why that's beneficial to your program to have veterans, enrolling because they bring those exact things that you mentioned to the table.

most of them. Now, the next question I'd like to throw at you is really kind of the inverse of the first question. And that is I'm sure, unfortunately, that you also see some students that come in that are a less than optimal fit and they struggle and maybe there's some common characteristics to those students.

So what would you say those are?

[00:42:52] Sam Borghese: Yeah, I would say the biggest characteristic of students who struggle is when they're not a team player, right. Which is actually the antithesis of what veterans and active duty service members have right there. They're forced to be a team, a team players. Uh, and why I say that is because a lot of this, a lot of this program is a group-based project-based and which really forces you to be in a real world scenario, where you're in a group with other people you have to delegate, you have to do your job, um, or else other people are going to be held.

Uh, they're not going to want to work with you on future projects. So then you're left alone or you're left, um, you know, with maybe the other students who, uh, their groups decided, you know, uh, they weren't a team player or they weren't a good partner. Um, and so I see those students struggle and then especially once they get to an assignment, they have to do all on their own and they realized, oh, they've been leaning on, uh, the other students who maybe have been driving a lot harder and picking up the slack of these other people.

And now end up with a better education in, in both ways. Uh, because one, they don't have to struggle on the individual assignments. Um, and two they've, uh, had to pick up the slack of even a third or fourth person.

[00:44:20] Shany Mahalu-Atiya: Yeah. And one more, uh, for me is communication. And I hear it from your lawyers all the time.

You can have the best data science skills and coding and machine learning. But if you're unable to communicate your findings in the boardroom with senior leaders and, and tell the story to people like me that are not, you know, from, uh, graduates of the program. And, but I make decisions based on data. So you need to explain to me, you know, what your data, your findings are saying, right?

And so if you don't have that ability, You will have very hard time advancing your career in that field becoming, you know, a direct or of the data science team at PayPal, for example. And so, because of that feedback, we recently had a session specifically with an industry leader and he's actually from PayPal and he's the director of data science.

There is an amazing guy and he, uh, facilitated a course on, uh, data, uh, storytelling and visualization, and specifically how to bridge that gap. And so I think students that lack that skill for really communicating their thoughts in a way that someone that has no background in that field is able to comprehend and then make the decision based on the feedback.

Uh, we would really struggle not only around here, but I think in the field in general,

[00:45:49] Ted Hallum: I love that you mentioned communication because that has come up in previous episodes of the data canteen they're focused on education. And I don't think it can come up too often because people do not trust things that they don't understand.

And so if you don't possess that communication ability to help decision makers, track along with your findings and understand them, then it doesn't matter how the quality of your findings, because they're not going to be listened to and trusted. and in which case, the decision makers, probably just going to go with their intuition rather than your data-driven insight.

And that's not, that's the exact opposite of what we want to happen. Right. now I, I know as we talked earlier, we said that all of this is great. People want to get skills. They want to understand the underlying. But one of the major things that people are looking for of a degree program is outcomes.

They want to have to be gainfully employed in a job. That's going to give them adequate salary and a high quality of life. So I'm going to throw some questions at you and understand that they're broad stroke. They're general. Think about your student population averages, just to give our listeners an idea of what an outcome for them broadly could be like if they chose to NQE program.

So I would imagine that, those who attend UCLA MQE, there are probably a couple of main geographic regions where people tend to settle out after they graduate. I'm sure a school like UCLA has students global all over the world, but where would you say are the top three regions where most of your students tend to go to work after they grow.

[00:47:33] Shany Mahalu-Atiya: I can answer the top two it's the United States and Asia Pacific.

[00:47:38] Ted Hallum: Okay. And as far as similar, as far as industry, what are the top two or three industries where your graduates tend to settle out?

[00:47:47] Shany Mahalu-Atiya: So finance consulting, data science and machine learning to function, but we can, you know, think about it as an industry and then, um, technology and the governmental agencies.

[00:48:02] Ted Hallum: Okay. And then those two were leading questions to get us to this one. And I've just thrown up here, three hypothetical students. Um, so zero years of experience, kind of a mid-career one to five years of experience and then mid to late career, which would be five years of experience in greater, as far as salaries, considering those main industries and the main geographic regions that you just mentioned, what would be a good general ballpark?

Salary that can be expected for each of these three hypothetical students.

[00:48:33] Shany Mahalu-Atiya: So from my experience, and I've been in this role for almost a year, but they've been in a business school for 12 or working with students. And I want to also mention that the majority of our students have zero to three years of work experience coming into the program.

Uh, not including some of our students that are more experienced with five and six years of work experience, uh, attending the program, being sponsored by their central bank. So we have many students attending from central bank, so Japan and Egypt, and, um, Uh, China and Mexico, and it's so exciting to have them in the class, but usually they go back to their banks to, um, you know, they return and they have, um, a role that these waiting for them upon graduation, and they will apply the knowledge backing their organization.

Right. So I don't work directly with them on finding opportunities because they are on a different path. But students, we, the zero to three years of work experience, which I work with directly, uh, it's very dangerous to just put a number, but it can be as low as I would say, 72 K. Think about it zero year as a fork experience with a master's degree, 72 to over a hundred.

And I see it every day. And so, uh, that would mean that someone with one to five years of poor experience, and especially at the, um, you know, end of the spectrum with a five-year olds would definitely should expect over a hundred K salary, which I think is incredible. And there are so many jobs and I want you to hear it.

There are so many jobs, it's not something that, you know, I graduated with a master's degree in 2008, the worst time to get out of graduate business school. And it was so hard. Every job had such huge competition and we couldn't really find jobs at the time. And so now with a master's in quantitative economics, the really, um, such a abundance of opportunity, and it's more about investing in developing those skills and feeling and being, um, Um, comfortable with all the programs that Sam mentioned and then being able to make such great impression work on communication past those interviews, and then that, you know, 120 K a job out of, especially for a veteran that come in with, you know, substantial experience that can be years of work experience, but just not just in, um, an organization, but, uh, in the army serving leadership skills on a team resilience, you know, we spoke about all of those, uh, traits, and I think it can be really beneficial to land the next opportunity.

[00:51:25] Ted Hallum: I love the way that you answered that question, because the idea behind it, the intent was to scope our listeners expectations, because, and there's, there's two sides to that coin. On, on the one hand, I want them to know the great salaries that are out there that they can strive for. But on the other hand, sometimes I come across really bizarre job postings for data science or data analyst positions where it's, you know, in some remote part of Oklahoma and the salary is only like $57,000.

And I definitely want to make sure that none of our listeners, especially when remote work is so common now, you heard it from Shawnee. The bottom is 72 K. If you're getting an offer lower than that, you need to pass because you can do better for yourself and for your.

[00:52:14] Shany Mahalu-Atiya: And by the way that 72 was rejected.

So the students and they lend them much higher salary. So that's the lowest that I want to be very transparent that I saw. And, uh, the students had higher expectations. So negotiating your offer and keep working for what you have set for yourself is very important. And our team is here to do just that support individuals, coach them partner, we, them on lending the right opportunity with the right salary and the right location.

Right? A lot of our students will relocate to Chicago and New York and boast on, but um, many companies now allow remote work. So if you want to stay where you are, um, keep that in mind as well. There are many opportunities that will allow for that. And I also want to mention that internships and specifically for veterans and active duty service members are also another way many companies, and I'm happy to be a resource.

Uh, to share my contact information. I'm happy to speak with anyone. I connected with so many companies at the service academy, uh, conference, uh, last month and they have another one coming up in may, in DC and many companies like Amazon again, and Microsoft and all of the ones that were there. They have a military pathway that sets you on a, on, um, um, trajectory for a senior leadership role within their organizations.

And they have amazing benefits and share shares in a great salary. Right. And they really look for, um, for military background. And so connecting with them, a lot of them have, um, veterans that are the ambassadors for those pathways. So I'm happy to put you in contact with all of them and data science. Uh, pathway within each and every one of those organizations.

And again, PWC, JP Morgan, Aronson, young, you name it. They were all there on there looking for each and every one of you that is listening today. So definitely look into that as well. SACC that's the name of the organization.

[00:54:27] Ted Hallum: So I'll go ahead and tell everyone who's listening. Shawnee is a tremendous resource.

She has offered to make herself available in this, in the show notes below you'll find her contact information. So if you're needing resources in this area, I, I challenge you. Please take her up on the offer. I know she'll be happy to help you. And she certainly has the right contacts and network, to help you get where you want to go.

Now, I know you guys mentioned earlier in the episode how the, how UCLA MQB has done an awesome. With evolving over time and staying in tune with the demand signal from industry and making sure that the course offerings are relevant and the people are getting equipped with the right skills. Now, as you look out forward on the horizon, say for the next three years, what things are you guys already planning to do to, to, to continue evolving the MQE for what you see coming down the pipe and industry?

[00:55:26] Sam Borghese: Yeah. So what I think is very novel in the program that we never touch on is, uh, the program director decided to hire me a student to come in and teach coursework and be connected with industry. That's really unique. I haven't seen that happen in any program. Right. So, uh, you know, 30 year old tenured faculty, um, that's mainly been teaching the same curriculum for a very long time.

Um, is standard right in most academic settings. Um, but they saw that I had built models and deployed them profitably on my own time. And they decided to hire me. And we actually just hired another student to come in and teach. Some of the courses I designed last year was I make new ones. And then we're looking to continue this actually, um, and higher and more students that are connected to industry and, um, sort of build that out since it's been working so well, um, to give all the students, you know, the software.

So that's, that's really, the biggest plan is, uh, build out these applied projects, which are direct connection to industry, right? They say, Hey, we need more people to solve this problem. We do not have the people to solve this problem. We think you got. Um, and then see what, you know, tools we need to solve those.

And then having more alumni as a faculty and really building a community in between the two. So it's not, oh, you just come in and here's a course, here's a course, here's a course. Uh, but everything's sort of interweaved with career goals, with alumni networking, um, and so forth. So that's, that's really a big plan.

We're gonna, we're going to start moving out is, um, hiring more alumni.

[00:57:23] Ted Hallum: Now I'm curious from what you just said and that focus on getting alumni in and being able to help students, um, to actually provide what's wanted by industry. I know a lot of companies now are working diligently to, to transition from that stage.

They were in kind of for like the last five to 10 years of, uh, data science, proofs of concepts. And sort of like glorified science projects to now, they want to get the models off the laptop and actually deployed in production. Will there be any courses to help students in that area to not just build models, but to actually take a model and get it where you mentioned APIs earlier, you know, get it working with an API and functioning in conjunction with a website or a software product, or what have you

[00:58:11] Sam Borghese: so interesting you say that the, uh, the assignment related to that AWS auto machine learning was actually to build the model and then hook it up to an API.

So that you could then send input information and then they would retrieve the output. So that was the final, uh, project of it. They had to write, you know, a paragraph about the business use case and shorter paragraph about something else. But, but that's what it was a fully deployed API, a rest API where you put in data for predicting whatever you want.

And then it outputting, uh, uh, what the machine learning output was, gender prediction, stock, market prediction, you know, there's a ton of it. So yeah, there's a thoughts we were actually meeting the other day about, um, more coursework and, um, building out those pipelines for deployment to a website, uh, or so forth.

Is it it's on the docket? I don't know where we're going to get there soon, but. Right now there's a lot of coursework surrounding a deployment, right? Like a quantitative finance course. Next quarter, uh, students have to deploy live, um, on a paper trading account, no money, uh, they're quantitative bot taking in economic fundamentals or sentiment data or whatever it is to actually, uh, you know, trade live.

And then they get extra credit based on the sharp ratio, you know, of their they're really, you know, fully deployed, um, uh, algorithm.

[00:59:47] Ted Hallum: Super exciting. In my opinion, I think that those skills that you're starting to teach students to take a model and then actually go the next step of getting it deployed and in motion out in the real world.

Absolutely huge. Uh, I look forward to hearing back in the future about how that continues to evolve and how that get incorporated into the curriculum. Um, now I know you mentioned earlier that even though the boot camps are in place and that people who are really willing to grind that that's sufficient to get them up to speed, but you said, you know, self PR self prep is a big deal and that you can make the transition into a program like the MQA much easier on yourself.

If you take some time beforehand to invest, do some Coursera courses, Maybe, I know there's a lot of different massively open, online course platforms out there now, but take some time there to refresh on statistics and linear algebra and maybe learn a little bit of programming and then you kind of make that logarithmic learning curve, not quite so steep.

Um, so I'd love to, to make your wisdom of that area available to our listeners. Uh, what self prep learning techniques and strategies have you found to be most effective, uh, among students to get admitted to the MQE?

[01:01:03] Sam Borghese: Um, so of course there's some base level coding. You need to understand for loops and while loops and data types.

And, and that's where, you know, the W3C schools, the Coursera to you, to me, that that's all great to get a base level knowledge, uh, what I would suggest highly. And I've always suggested this is do your own project. Try to answer a question or a problem that interests you and just figure out how. Right. Um, I remember we started in the program and we wanted to build a bot that forecasted basketball over and under and published it to Twitter.

And then, you know, you can sell so, uh, the results or whatever to gamblers, you know, and, um, that problem takes a lot of different skills to be able to go ahead and solve a lot of different data science skills, um, relating to over-fitting and under fitting. And it's like doing things like that. I learned, I learned so much more than, um, any pre-packaged course online.

So I tell students that all the time, when they go, how do I boost my Python experience? I go pick a problem. That's unique to you. And. Really find the hurdles and how to get over them. And you're going to, you're going to learn skills much deeper. Um, it's also going to go way better on a resume than, oh, I worked with a Kaggle data sets or predict heart, um, you know, heart disease, uh, which you can Google and find a hundred people who've done the same exact thing.

Um, rather you actually figure out something, you know, completely. So, uh, that's what I would say. And then to do that, right. Google is your resource, Google and stack overflow are your best resources. Um, in that case, pretty much every line of code, every line of code I write, I've probably had to look up at some point, you know, whether it'd be the documentation for the package or on stock and stack overflow.

So, um, yeah, picking projects and

[01:03:12] Ted Hallum: doing those, I agree. A hundred percent. There's no substitute for having. Unique novel problem and fighting, fighting through it on your own. aside from maybe having to teach the concept to someone you learn a lot from having to teach someone, but aside from that, probably fighting through a project on your own is one of the most effective ways to learn.

So great advice there. Now, as we get ready to wrap up, I can tell you, I've spoken with both of you in the past. in fact, a couple of times, and then I've spent a lot of time on the website becoming familiar with inquiry program, how it works, what it offers. but even with that, it would be hubris on my part to think that I've asked you every conceivably good question about the MQE. and I'm sure there's some things that our listeners would still need and want to know that I haven't thought to ask about.

So as one of my last questions is just, what have I not asked you regarding the MQE that I should have.

[01:04:09] Shany Mahalu-Atiya: That's a great question. I have, um, two things I'd like to mention. Number one is, uh, the question would be around, um, resources specifically for veterans at UCLA to answer questions like financing the degree and utilizing all the benefits that, uh, prospective students that are veterans head.

And yes, we have a veteran resource center at UCLA and Emily IVIS is the director of the center and she's available to answer questions and we can share her information as well. And so I think it is so exciting that there was a center within UCLA that support active duty members and veterans that want to pursue their advanced education.

And they will have. Uh, availabilities for meetings and, uh, information and all their questions will be unserved. So that's one. And the second one I sent, touched on it a little bit earlier, the applied project, and I think, uh, to do some justice to this amazing initiative within the program, I would like to speak a little more about it.

And so, um, as you know, when you attend the graduate education, uh, you learn a lot of theories and concepts, and of course you practice a lot of them in the classroom and in teams and you really start to apply your learning into, um, And take it one step farther. We are with SEM and our senior director of strategic initiatives and academic collaborations.

Uh, we, uh, look for companies that would be interested in coming in as clients, uh, to provide us with a business challenge. And it can be really anything about something within their organization that they would like to take a deeper dive with students that are students of the master of quantitative economics that learn all those tools and programs that have all of those incredible skills that can really, uh, provide a solution that they potentially didn't think about.

And so it might be any company from all the industries that we spoke about. Consulting technology could be a small startup company. They will come with a business challenge and Sam and, uh, our team will develop an assessment to specifically select the students into this consulting team. And so we build a consulting team that will work on a kind of a 10 week, um, course who solved the problem at the end, communicate their results.

So here we are allowing students to practice their communication skills, apply the knowledge, and bring really a great solution to a company. And it's such an amazing opportunity. It's such a hands-on, um, you know, opportunity to apply the learning, put it on the resume, um, speak about it in future interviews.

And some of those students. Being hired by those clients, the companies as interns is full-time, um, employees. So I think this is a huge advantage of our program. It's really, you think about leaving the program, you know, graduating, but never really applying the skills. It's one thing versus I already done this work with, you know, XYZ clients and here is why I can help your company as well.

And so I'm very, in some can share more, but I'm very, very, um, happy and excited that this is part of our education.

[01:07:46] Sam Borghese: I agree with Shani. Um, you look at online courses, right? There's tons of open coursework like you were talking about. And, uh, I mentioned to, you can learn a lot of the information in this program, just on your own with your own self-discipline.

Um, as we know, you can sort of maneuver those online courses and get the certifications without actually learning the material. Um, but what this program really offers beyond accountability and the certification of a UCLA that comes with it is UCLA. There's so many resources here, as far as what company you want to work with us, we're working with the largest privately owned company in Thailand, huge conglomerate.

Right now we have interests from Ingram micro. Um, who's hiring interns from the program as well, you know, a $50 billion revenue company. Um, just the fact of you being part of UCLA for networking as well, um, is invaluable. You know what I mean? It's beyond, um, just the knowledge you receive in the program.

It's really being a part of the UCLA. Um, that'll take you so much further than any other program.

[01:09:07] Ted Hallum: Oh boy, I'm glad I asked that question this time, because we learned about the contact information for the veterans benefits representative there, and we'll make sure that that's in the show notes below. we learned more about the nature of the project and the curriculum and how it's meant to be real-world and gets you comfortable dealing with uncomfortable situations and ambiguous data and those things that you're going to encounter in the real world, which is huge because there are a lot of academic programs out there that kind of have like more of what I guess I would call a straw man curriculum, where the data is curated.

The problems are curated. Everything is designed to make sure that you get a nice, neat solution, and then you can move on to the next module. And then those students graduate and they meet the real world and it's whiplash inducing because the real. It's not, it's not populated with straw man problems.

They are real problems that are very complicated and messy. And so you guys are equipping people for the real world, and I think that's huge. and then what you mentioned Sam about UCLA has reputation around the world with employers. and I would add, I remember in my research and preparation for this episode, I came across a link that talked about, there's a national ranking of UCLA alumni network.

I forget the actual number, but you guys have one of the top ranked alumni networks in the nation, as far as just how fellow graduates help one another throughout their career. So that's huge as well. Now I think the, I think we're down to the last thing and that is just your preferred contact information, how to each of you prefer to be contacted if a listener wants to write.

[01:10:45] Sam Borghese: Yeah, sure. You can reach out to me on LinkedIn Sanborn, gazey, V O R G H E S E. I'm. Sure the name will be in there and then a direct message on there is, uh, the best way to do that.

[01:10:58] Shany Mahalu-Atiya: And LinkedIn for me is really great. Uh, I'm Shamima Holloway, Tia on LinkedIn, or just email me directly.

in one ward at econ that UCLA, that EDU, I'm always happy to connect with individual and passionate about supporting people in fulfilling their aspirations. And if I can help one of you or more than one, uh, it will be very rewarding. So thank you for the opportunity. And it's been such a great conversation.

[01:11:28] Ted Hallum: Oh, thank you both for coming on the show, I'll make sure that both of your contact information is in the show notes. So if people were furiously scribbling it down, no need, you'll be able to go and copy and paste it from the show notes. I really appreciate everything that you're doing to support the veteran community.

That's interested in the QE program. thank you for coming on the show to give all of our listeners an introduction to it. And I know that it's constantly evolving both in terms of its technical content and in what you all are doing to support veterans. So in the future when major evolutions happen, I hope you'll let me know, and we can have you back on the show to give everyone an update.

[01:12:02] Shany Mahalu-Atiya: Sounds great. Thank

[01:12:04] Ted Hallum: Well, until next time. Thanks for coming on.

[01:12:08] Ted Hallum: Thank you for joining Shawnee salmon. After this conversation about UCLA master of quantitative economics program with that until the next episode I bid you clean data, low P values and Godspeed on your data journey.