MS-DSPP Admissions & Academics Webinar

Hi all welcome to this afternoon's webinar hosted by the McCourt School of public policy. I am Lindsey. Munson me associate director of admissions here at the court and we are joined with our faculty directors of our Master of Science and Data Science Republic. Polisy program might Bailey, an Eric jumper so during today's webinar. I'm gonna ask Mike and Eric to go over the curriculum of the data science program that will take some questions and then I will finish up with some application information so deadlines.

Location requirements and then will also finish up the questions so you can post your questions into the chat box and will try to answer them along the way so with that I will hand it over to Mike and Eric will talk about program. It's great, yeah, so thank you so much. Lindsey and thank you. Everyone for showing up virtually here for this so yes, my name is Mike Bailey. I'm not.

You get the director of the data science podcast program So what I want to do is just lay out like kind of a philosophy of what we do, and give you a big picture and turned over to Eric and talk a little more granular. He'll also kind of flesh out his view that philosophy, but also a little more granular about that. Some of what exactly we do so. The big picture is a couple things that I think you know general but that specific about Georgetown. So one is it data science is just on fire right data science is.

Yeah, huge job market opportunities huge growth in Hindi industry. Huge growth and in organizations across the whole spectrum of the economy and beyond of one and one more data scientists.

So this is one background condition and then what the Georgetown view of this order datasets policy view of this is and this is consistent with what a number of other people have talked about is like data science in isolation is pretty rough to really pull off and be able to really contribute to things what really makes data science work is to be able to link to substantive and domain knowledge right so that's I think such an essence of what we're doing it.

Data science for public policy, and both those 2 sides of that phrase are really, really important. So if someone comes in and just use this as purely like kind of a computer science or programming problem. That's fine. There's certainly programs and folks who do those kind of things. But honestly that's where they can kind of outsource to kind of factories that will just run a particular algorithm or something and it kind of almost gets on the interesting if that's like the essence of it, but what?

Really and where there's so much attention, and activity and action and employment prospects and so forth. If someone more on what the McKinsey the Consulting Group McKinsey. They call kind of translators and the translators are able to they need they need technical knowledge and experience, they need to know what they're talking about the same time, they need to have domain expertise and they need to understand the problem need to care about the problems we have to work data. Science is not easy. You have to work really, really hard seem to care about the problem. You don't understand the problem.

And then also you need to think about.

Like the organization in the management of it, so these things do not. It's you know, I so wish. It was the case that we could just say Hey man wouldn't it be great. If we had data that would tell us whether this program is effective and then snap our fingers and people would give us the data and they run a little program on it, and spit them back. Some visualizations or something about what's going on. That's the furthest thing from the truth in terms of what happens? What really happens is people are starting to say, Hey, we really want to improve our delivery of Education, or delivery of better inspection regime.

For safety and food safety or something and then there's a whole management in the whole process of like you have to understand the problem and what the incentives are proves doing what and how they're doing, and how the data generated what weird things about the data on how to bring that data and bring it matches. Others and then to think about how causal causal? How much does this policy cause what we're seeing or how much is just Association so anyway, so the thing I want to leave you with as as you're thinking about that.

At least the big picture and I'll talk a couple more specifics, but the big picture is just that. We really are both sides of the expression data center public policy and so it's are very much goal is to provide an educational experience and inside the classroom and outside the classroom that gets you both the data science in the public policy and then so that's one big thing and then let me just give you a kind of 3 concentric circles so bad as you think about what George how can add value to?

So the specific thing and what Eric is going to talk about in a moment is the curriculum and so I'll let him talk about that, so there's a curriculum that really. I think has been designed and fine tuned to give you the kind of skills were talking about.

So that's within the data science for public policy program itself. Then there's some of them a quart school. McCord school but one of the nice things about our program. I think is your embedded were a program that's probably between 20 and 30 students per year and then the court schools get about 200 suits for years. So we're going to embed you and you're going to be taking classes with and doing activities in and outside of class with a broader. McCourt population, which is about 200 a year students. So now there's just more people more stuff there's.

You know the student groups can be more specialized and focused on the kind of things that you're interested in whether it's a you know, India, or Latin America or the environment or Department of Homeland Security. You know you name it. There's going to be we have a large enough group there at Mcchord School and the fact that resources are going on there and then the next circle out is Georgetown University to Great University and so there's resources activities and Displacement and reputation and so forth that there's even more you can draw on.

And then finally the biggest circle that this very relevant for your life is Washington DC itself. And so there's an interesting place to live. It's a fun place to live, but the business of Washington in public policy, and so there's there's a huge interest in the data science end of it. And so there's employment opportunities. There's talk opportunities. It's just kind of a culture of where you should be able to match your interests with people doing like exactly what you're interested in doing.

And so like you think of those kind of concentric circles you're going to spend most of your time, especially in the first year and the 1st circle doing classes in the curriculum and so forth, but we very much value and encourage you to take advantage of the whole spectrum of all these other rings as I would call it opportune tease.

So I'm happy to come and eager to get your questions. But I think the way we made it out is done for the social structure of the program. He's going to talk a little more detail about the curriculum and related items. Yeah, yeah, so let's get my name is Eric Dunford and so I had both teaching some of our data science course sequence and also help directed program with Money magazine mention and so just as my kind of hit on it, and whenever it maintains here of course, is to kind of make you both are good.

Good thoughtful public policy consumer about research critical that research in person. That's able to kind of approaching these kind of problems were really good theoretical angle and equally equal step. We want to make you a good data science practitioner to make your work reproducible to make it transparent and in fall, all the good kind of research that kind of research methods and approaches that you kind of need.

Any kind of collaborative data science, setting and so we, we talk about joining these 2 things in a lot of ways. They're not divided spheres of kind of thinking in the sense that you're going to go out to your public policy class or anybody for data science class and they're going to be truly exclusive kind of material and kind of come back together. But rather we're going to be thinking. About is OK. What exactly is public policy type data right like? What is the kind of data that you would have to encounter the kind of algorithms in the kind of approach that you have to be able to implement.

Think about what's the best way to kind of crack this nut. We've given whatever public policy domain that you're in and then two we really want to think about is you know after learning these public policy related topics. We're going to do so very much so under the guise that this is a public policy related kind of data set or this is public policy kind of related application to this machine learning methods. And so we very much have these 2 elements baked into the curriculum and so they very much speak to one another as you're kind of becoming better at the public policy domain in data science domain.

So specifically our curriculum is organized in such a way that in your first year. We're going to steal a heavy dose of both the statistics and the data sequence just kind of happy love for any kind of some summer internship experience and on Top of that it will also be delving right into the foundations of all the important aspects of public policy? Which of course includes things like Macro Macro and Micro Economics and public policy process to the data science sequence in particular, we kind of have it laid out.

And three quarter sequence is you don't actually think about is 4 core sequences insist that we kind of laid down the foundational framework that will compliment all later coursework will take both kind of re emphasizes the basic elements of programming? How to determine if you lation. We supposed to think about that and also a lot of the mathematical foundations that you kind of need to that underpin a lot of statistical machine learning models and a lot of processes that we use to optimize.

And I on it, and so on, and so forth and so in the first foundations class will get a lot of the kind of the groundwork and then the tools that you'll need to kind of instantly delve into your second semester apply. It applied applied statistical learning will you'll learn a whole array of supervised machine learning techniques specifically within the realm of the public policy and also be applying those with natural real life. IDC clients as you're kind of actually you're actually learning that during that course material.

And so it's really quite nice about that right is that you're gaining this is very substantive is this applied knowledge of how to actually implement these tools are not just kind of you know learning OK. This is a clustering algorithm and kind of storing it away. But you're immediately kind of Reapplying. This real kind of active real world, setting as you're learning as a part of that class, So what we try to do is make it in these classes very much that we're not divorcing you know, we're not removing. You too much just into an academic domain, but really thinking about the fact that.

No, you'll be an applied you will be working in applied setting rather quickly after your second semester and summer. We have a lot of our students. That kind of jetted off in and did various various internships. Internships techniques for their immediately applying the skills that they've learned in the course of their first year and then on Top of that with a big science in public policy sequence would also be taking accelerated quantitative course sequence right, which will teach you everything.

I'm from running multivariate models running more generalized linear models running to think about causality and what's ways that we can kind of make causal assessments, both using ideal kind of experimental data and then also using observation. Ull data that's not necessarily perfect for our needs so you kind of come out of this, this whole first year as a well shaped individual back will just think of data just purely in terms of algorithm that can instantly solved the problem, but also think of it under good causal links that would be really quite useful.

And I sure as you're actually moving forward and kind of actually finding something interior and then your second year we have a kind of move into the final course sequence of our data offerings in since you'll be taking a data visualization class to be able to really kind of make a lot of your results in your findings transparents to decision makers on another consumers of information that might not have the technical credentials or background that you do and so very much kind of emphasize the communication elements of this.

I'm in ways that you can kind of convey your finding Sue to various groups and decision makers and then I lost you taking a massive data course, which will both you can both think of it in terms of either databases or also any kind of cloud computing resources that you would necessarily need because very often in any kind of organizational multiplied setting really what we're dealing with is not some simple kind of database that can actually be stored on in memory on your computer.

Rather, larger database that the organization itself is building up as a lot of security features baked in to kind of make sure that the there's no breach in that there's ethical use of the data and so you know it's imperative that you have your ability to kind of communicate and attacking to whatever framework that they're using AT whatever site you end up at and then ultimately the final part of this is the curriculum is very much in your hands in the sense in terms of electives and so it kind of matters on.

You know, we have assumed that of course, try tool up and take some some more different data science courses. So we think about like natural language processing, you get really deeply into that or taking very specific courses like time series or geospatial analysis or courses of those domains and if that's that's what's of interest. You in your public policy kind of area really kind of uses. This kind of data at this gives you an offering to kind of go and get more skills in that domain.

Or if you have certain kind of public policy areas or that you really want to kind of explore further. The electrodes of course could be an opportunity to kind of like to kind of expand and kind of learn more about actual substantive knowledge on there and for yourself back to Main and so it's really quite nice about our curriculum now is that we have the heaviest load that first semester. We really kind of really getting getting immersed in the environment and really kind of.

Nice looking after data data techniques and then we kind of taper off to a 333 year remaining successors and this is really quite nice 'cause. It gives you an opportunity. Both take on part-time internships over in the DC area to take further exploring further coursework or it kind of use that extra kind of credit or that extra class opening kind of really engaged and try to get yourself kind of out there.

What is really Byron gated community here in BC either in terms of how ponds or in terms of DC meetups or in terms of just policy talks there in there, you know, we really kind of pain to have you freed up to actually be able to engage with that community and actually be a goodnight kind of data consumer and policy consumer here, while you're in DC?

He had a great day and I'll just add one brief addendum to what Eric said is I really want to note and this is different from some other programs that were at the core of what we offer is offered by our full time faculty so the stats 2 course sequence in the 3 three first three data science courses are offered by full time faculty and then the other courses in public management and economics.

Also process also offered by core factors you really get this is not kind of an add on.

Hang on this side this is very much you know poor enterprise was going on the same time, we do, and I think that's why the advantages improve from Georgetown, Washington DC. We do leverage and make use of the fact that DC has so much more on and so our ethics classes taught by someone who's working with Google and their kind of ethics area. We have another one of our elected as a policy issue policy issues for big data. That's also somewhat from Google are natural language processing.

Collective is offered by someone from Facebook, so I think that's a kind of a nice you're going to get you know the score like academic people who are committed and connected to Georgetown and connected to the program for the long term, but you're also going to be have access to the people. The culture, the way they act right for some people in the technology space as well so that you know, we're pretty proud of that balance.

So these questions, so should we

Um, I'll take, will just kind of go back and forth. So I hate seeing question that can they also be special so there's a question? How do the economics and policy courses compared to the MVP and PC courses respectively. So the happy talk individually with you later. Offline also, if you want more details but so the MVP stats classes. You get are they take 3. Semesters to do the statistics material that we do in 2 semesters, but it's basically quite quite similar at.

Essentially toward the MTP doesn't stats MVP does not have a data science curriculum that we offer they have collected but they're not the level of what we do.

And then but some of the courses the policy process and the management and the economics courses are actually in the MVP program and like I said, I think that's a nice feature that then you get. We're not just isolated in data science only work connecting to a broader policy group of people comparing to PhD courses.

You know there's such a range of pencil a PhD program. And so forth, but I I think I also teach the PC program for the government government for Georgetown and the stats that we do here is very comfortable to government Department stats. Looks like an economics Department is going to be much more mathematical unless applied by a large so were applied. We're getting you into using data using are getting going with it and then were different from computer science kind of place.

There you know much more about algorithmic efficiency and and really focused on some other things that we don't do 'cause. We're really applied you're going to do some very, very serious data science here using you know big computers and big complicated things. But we're focused on the applied end of the world and you know the computer science, obviously gets gets into the same kind of a different competing type level.

The alternative drugs and feel free to weigh in others. Lindsey and Eric if you have another question about prerequisites. So we do not I mean, we do have prerequisites that the philosophy. You actually Lindsey can give me the very specifics. But my philosophy. So Eric and I read all the applications and what we're looking for is you have to show an interest in an aptitude in technical material. But there's not a specific way you have to show that so do you are you know a Nikon major and you took a calculus based E con?

Micro theory courses in a sequence on that and you are good at it. We're pretty confident that that and then you apply to our program that you're kind of have the wherewithal to figure out.

But that you know you're going to be able to handle the technical material. We have people who do with taking programming classes with people taking statistics classes mathematic classes or some engineers. There's really lots of different ways to do it. We're just looking for again. It's not just the attitude. Some good grades in technical classes but you know like this is like something that's really interesting to you and part of that is seeing you took it in college and then obviously part of that is just showing otherwise you can show interest.

I'm just move so now there's a question from Crystal Lynn. How much of the core data science classes would you say are foundational?

Data structures and algorithms courses elerick weigh in on this to the.

Uh I mean, so the first course is more right, yeah, other ones are applied, yeah, and so in our very first coordinated science sequence where we're really going to be doing is trying to get underneath the hood of So so actually they anticipate Ronalds question down below right so we kind of think about this is we're going to we're going to try to teach you both are in Python simultaneously and then a sack sequence will focus in primarily use are in the data science sequence will use Python.

And make you a good active user updated manipulation and using both of those languages to you know to their 2 as they were designed right and so in our in the first quarter data science sequence class right. We're going to think a lot about the fundamental programming in Python. Fundamental datatypes how what's the best way to necessarily kindof loop through or manipulate different data structures.

And he did not raise feeding nested lists or become limitless or other forms of data structures that we will generally kind of run into it's just kind of a base data features and then we're going to kind of build off on those is OK. What's the kind of the toolkit. That's going to merge around it and how did those relate back to those based data types that we need so we're thinking about using like things like the Scipy Stack, with tools like NUM PY and pandas and how we kind of can build on everything that we understood on the lower end of actual.

Python programming from there and then and then from that kind of sequence then we're going to kind of be constantly iterating back and forth between OK? How do we manipulate data on using these kind of very high level. APIs and also how we manipulate the data. If we had to take it back down to say something like nested list to solve our problem because we know that not in as you'll learn is that not everything is as efficient right when you do it just using standard packages and often.

Oftentimes, your problems unique right there's many gigabytes data science problems have been encountered before, but everyday sense. Problem is kind of unique and sometimes you have to build your own kind of custom toolkit to kind of deal with whatever data data issue. You're kind of running into and so will our immune to have those kinds of to be able to think back in a way kind of approach. These problems from a variety of angles. So we're not just track with one tool kit. You can understand the larger framework of how everything works and then from that, your later sequences.

You're you're obese primarily focused on machine learning algorithms. And I'm thinking about like how are these algorithms put together how they implemented? How do we assess them? How do we interpret them right so we can actually say features out of them 'cause once again estate become more flexible right. It becomes harder dependent story. This actually drag. The effect and so a lot of those those features will be thinking about that. In both supervised supervised settings. And so we're going to cover a lot of ground, but of course, as Mike said before.

We're going to spend less of our time thinking about. Hey, what's the best way you know what's the best way to make this time, efficient or space efficient. You know, once the absolute wave maximize performance on on hard drive or any of the other kind of core problems that's more of a computer science problem right so we're going to Bury think about. We're going to we're going to have a complete approach about thinking about all the computer science angles of things from an applied public policy data angle.

Yeah, that's great and one thing I added so you know for a number of you like NUM. PY and pandas in structured and unstructured some of these terms might be unfamiliar to you and that's fine and I do want to emphasize that what we do is we bring in people from a diverse set of backgrounds and so we start from zero and so like in the summer before you enroll. There's a summer online program where you get kind of get rolling on.

R and Python And of course, you know when you show up like I I for example, teach stats class. We're going to start from 0 that's about 1/3 of the classes had some are before and maybe half of those folks are pretty darn good in our but our experience has been that.

You know if you really, really good at all. Or maybe your Python isn't quite up. You know, so like we're going to kind of get to from zero to 60. But we're starting from 0, so if you hear things that aren't familiar to you. That's fine. That's the point of the program is for us to be able to.

OK so am I on the Ronald another question stats undergrad, yeah, so we so riled questions was what, if you have a lot of experience in your academic record doesn't maybe reflect all that when we read the applications were very holistic about it. And so obviously it helps you can guide us through, Hey, maybe this looks weak and here's.

You know how I run since Anna when I received of course, appreciate that navigation process by application process is pretty cool listing.

Hum.

I'm going back to how much do we do with massive data Institute. Another centers are able to do research swords thesis so that is great question so the centers are at this point kind of a locus for research assistance of jobs, which by the way we very much view as part of the learning experience are not mandatory but you can get paid and then you're seeing these things in practice and so we have folks working at the Center for retirement initiative. We have folks working at the Center for energy security emerging.

Emergency City Center for secured emerging threats. We have people working at summer health centers. We have here.

George time we will have people look at massive Dayton students well right now, primarily on a one off you know the relations. Individuals findings from research assistant jobs.

And the thesis thesis we don't require a thesis for the program. But we can support independent research that is essentially a thesis and that's something we're happy to talk about. We also kind of have found that it's in the as you're working on a project, either within class or beyond classes. You may be able to get a thesis like experience say for example, at your job and then were very supportive of that as well.

On my C question from crystal about parts of program to focus on machine learning. And yes, absolutely and basically particularly data science 2IN data science, 3 at the locus of that and it.

For me its other classes but definitely going to show up there is the heart of that, so very much for those of you kind of knew a little bit to data science space there's an interesting.

Division were covering both sides of this so the one hand machine learning is very much typically about prediction. So we're going to really be able to take some data and you want to predict you know like in the in the private sector. We want to predict clicks or purchasing is more affinity or some.

We want to predict you know successful completion of high schoolers or a We want to predict if there's a terrorist act or something you know, so we're going to have like different things that we're trying to predict that's going to have that same spirit of what's really very hot in the private site.

But that's contrast, it, too, and we're going to a big part of our curriculum philosophy. Here is there's also cause. It will cause we're trying to does X cause? Why does X cause? Why is a very different question than what is why going to be in the future you know why being in some variables and so forth and so we're going to do. Both sides of that, but the machine learning is primarily that exclusively on that prediction side.

So I see a question from Sarah Qualitative research, but not so much in our which is still the kids with program, yeah, so that's a great question. I think that kind of comes back to the themes of who we are. You really even if you've been doing qualitative primarily before we definitely have students who fit that profile.

Somewhere there has to be evidence in its fruit for you and for us of an aptitude and interest in technical material. But there is no expectation or requirement that you have to have all things if you don't know are that's fine if you've been working with people who were working in state up undergraduate research assistants are working in state's fairly advanced material in undergraduate curriculum so forth. That's fine. That's great. It's nice starting point we're going to do different things. So I would definitely not.

Google yourself out you know this is of interest to you and you're the fit. You know if you think this is the kind of thing you want to do I mean, you can show us. Some technical skills and some people even do. They maybe they do. An online course. Or they do a summer course or something if they really haven't developed that to date. You know, there's lots of different ways to do it, but we're definitely first backgrounds things that we offer he ran recording so we also offer a?

I'm looking at material as you're coming into the program that will help you kind of get on the same page and start thinking about this stuff before you also have to think about statistics and public policy and all all the other domains that you have to think about that per semester. And so we very much have materials that kind of plug-in students that are definitely freshened immune to this material or even students then maybe have run inside a Python or R before but could really use refresher because it's been years since they've applied it right and so we

We do this weekend course jointly with Analytics. So we're kind of here together as a kind of prep preparatory material that we would have for you all as soon as you walk in the door will kind of kind of refresh could it be able to order it had some exposure to materialize when you're walking and so that's something that we've offered it to great effect for our students names coming in the program. Especially this year. Yeah, like so I would say 2/3 of my students in this the current first year.

Did not have substantial are at either 0 or minimal experience in our and we have our midterm coming up tomorrow and their cruising there. You know it's a long-term process to become really, really, really you know proficient at all but there are able to do the analysis and visualization and so forth already that's that's expected see another question of work experience. You know how much should I deal applicant have you know we're pretty flexible in that again. We have a diverse set of people coming in?

Um with some people come straight from undergraduate.

And then probably most people have been working at at least a little and there, you know, I mean, we have.

There's so many different instants abroad point here is that we really have a diversity of folks that come in and let me just tell you a couple examples. I can think of off the Top of my head and there's one student who was working in industry for a grocery store chain in its business business person was doing perfectly well there. But just kind of realized his passion was. He loved the kind of the data side, but wasn't really doing that serious data working his his job that he had, and he?

You know, he's going to do it, you wanted policy. He actually had this up along and he was doing stuff on his own already kind of anticipating what he did here, but you know, there's a person working in industry and really a field that was pretty far afield fair number of people are have been in jobs in Washington, DC or elsewhere across the world of kind of a Policy Research Institute, a research assistant program coordinator or something some kind of policy space that's pretty common and.

You know, and then other people definitely had people who work in an engineering firm or working in government already so it's really we're pretty open again. We want to see that you have aptitude and interest in the field and then we want to support you once you're here.

Um so begin working at RA at the beginning of the first semester is there waiting period required so the question is can you be a research assistant right away or is there a waiting period you can be a research assistant right away there's a small number of cases where we kind of match make on the front end. That's pretty small but we do some of that and then more commonly as people show up or before they show up. They gotta ask us about opportunities or they have access to the McCord resources at the indicate that those options exist.

So I mean, 1, you're pretty busy that first semester, so we generally we really, really, really love the working if you're ever going to work less the first semester is the time to work less so I mean that's one thing to keep in mind, but another thing to keep in mind is you're going to be very high demand. So the skill set that you have often coming in, but certainly skills that you that you develop is something that that we're seeing a lot of faculty amount of research centers are interested in that skill set special that I'm here at the second year.

We have more requests and were able to satisfy the students. We had forgot to research assistantships.

Um so Victoria asks other opportunities for mentorship with the program and a mentor. We don't have a formal network program. But we do were small so that's one of the things I like smallish and so we have a party at my house and started the year and 2nd years first years come to that and we tried to have opportunities for the second used to be have connections to the first years we've not formalized that mean we could actually have it hasn't been.

You know the feedback we have from students as they feel I feel.

What they're telling us is they feel like they have a community so they have the right off the bat they have the DSPFLODSPP students that senior taking classes together. There's an actual quickly as you do together some seminars. We do together and some events and then between that and then this broader community community at the port.

People can find specific affinity that they have they want to do before policy and practice or we want to do environmental group and so forth so people are pretty connected and I feel pretty good, that that's one of the nice things.

So James has a question about the start from zero aspect of programming, but will you be bored? Yeah, if you had a couple of years experience? It's great question so 2 things on Maps so one is you can test out for classes so.

We have yet to have someone test out of classes. You just 2 years in so no one's done it yet, but we're very, very open to that, like were were 39 credit program. That's less than others, and we want it so that we can be efficient cost and with regard to cost in time. We have just 39 credits and so, if there is a course that you can test out of actually someone testing out of economy so the economics class, the first level stats class.

For sure, and this one is data science all those courses. You'd be open to testing out of those, and then you would just have that much more latitude for taking electives. I will say one of the things we just had a student who she was visiting in my class yesterday as a perspective student that she is an undergraduate econ major.

Who has now working in an economic consulting?

Organization doing kind of this kind of stuff, so in but.

Yeah, she said it in my class and then after the class. She she did note that we kind of do things a little differently than you may have had it done where you were so the stats to focus on causality and some of you are. Just you know that's that's bus is not in our class. That's that's fast as a class where we learn all in order to do statistical techniques, so again we will go with.

What what you need what you can do but a number of people have even had statistics before in general have benefited from the way we can steer but we're not going to force you to do it if you already have that feedback.

Um.

Yeah, so across a question about Carlson veteran and has a question about reaching out to someone for assistance wish. We had the beginning, but there's a veterans assistance office here. Georgetown would be happy to make that connection email me and organs and we can make it a connection that we've done this summer was working with that's helping them this is for undergraduate undergraduate applications.

Oh beyond Georgetown and Beyond and then we have that support and are on TV.

Uh some court scholars and other scholarships allowed has a question about Scotched. So one so there is something called a port scholar program that's a very competitive program that's across the entire report school public policy. I think there's there's 5 per year and it is fully funded with this type. So it's one of the one Frank McCourt gave us 100 million dollars in 2013 roughly 1/4 of that gift.

Well, it's kind of allocated towards really funding exceptional students. So what you do for that. To to you don't apply for that? What happens is you apply and then we pick out people that we think of potential candidates for the McCourt Scholarship and then we kind of will initiate that process and let me know if you're in the pool from that, we have for example, one imports valor in the program this year.

Are beyond that there's some scholarships to the admissions process in the admissions process we allocate scholarships. So we have a budget for scholarships. It is not unlimited, but we do definitely provide scholarships their merit based scholarships, but that's something that definitely you shouldn't like.

You can talk more with us about it, but also is there, thinking about this and costs and so forth. You know keep that part of your definitions welcome me instructions.

For you.

How many students Allison as how many students accepted the program every year did miss the middle class has been around 20 and each year, little last first year little more, the second year and so we're probably anticipating somewhere between 20 and 30 students who enroll not sure on the acceptance that yeah. I don't know the exact number on that, but that's kind of like the Romans problem ballpark.

Ah.

Hi.

Zay asks about PhD programs an insistence that full time offers right away. So the PC program that's an interesting thing just doesn't aside. I haven't even told Derek about this. But we have someone who's come to us. He wants to fund a PhD student and probably a graduating student would be good mix for that. But it's just an aside that I think that there would be opportunities in certain PhD programs or even be emerging from the DSP program could position you well.

Other policy political science and data science pH DS for economics probably less so but if you really were oriented towards that we could talk about how you might want to tailor your program here.

Uhm and then so full time job offers so we're just finishing up our second year so we have yet to have students graduate, but again one thing that I'm seeing is on the internship and a non. Just seeing the job on the job opportunities that were seen which were feeling very good about that. I mean, the market. We could actually take about the job opportunities for people this degree. But since we're young degree. We do not have a specific track record on that.

Ronald bass about Bayesian statistics that's an elective sequence that you can take that? Which of course offerings for that so that's a good example of OK? Here's the course sequence of stuff that we've learned and I want to learn some more statistics. Whether that's Bayesian statistics for time series. Geospatial statistics and we of course, offerings for that. So if that's something you're interested in we have material and offerings for that.

So should we do this other thing to the next 3 questions kind of quickly and then because I really want to turn it over to Lindsey. She has a specific information about the admissions process itself, so early action is we will talk about that second great question an opportunity to work with you last night or another great question so they're not formal but I would say like that's a great example, so the Center for.

I was getting run Center for security emerging threat there. Actually, another downtown location, but their downtown location. They've got a lot of hiring that they're doing, and I'd be happy to talk offline about what they're doing, but they are very much in the data science space into the Law Center. The Law Center and one hand is not like an obvious place for data science right like laws just kind of a different field typically as far afield from that, but they have something the center of variety of centers. I'm going to get the name wrong, but it's part of the Georgetown Technology Initiative.

Um in loss centers leading there so they have a lot of people doing privacy. A lot of people doing AI. Polisy Impala see that's related and so it's kind of on the edge of synergistic with what we're doing. There's a lot going on there and there's places where I could well. Imagine they would really value. Someone really those are policy questions mean obviously legal questions, too, but their policy questions and their policy questions where.

If you don't have some technical chops of some technical understanding of things you just might not really be able to weigh in the way he should, and so that's those have options for exist for sure.

Yeah, one last question, then turnover Lindsey and then at least keep asking questions and we may have to do these ads are them offline as well. But if you want to take courses outside.

Uhm you do not have to apply separately.

And then you can take courses outside but it's not like your access to the DSPP and report. You have full access to them before classes like this is your program where you you know, sometimes classes fill up and so forth. The DSP classes by the way all those core classes you're in you don't have to worry in Iota.

Right.

McCord elected and so forth, sometimes it might be a really famous professor something with a limited number of seeds. There's just a process as it would be for any report student in order to get those electives, but basically you're going to get those courses. The courses outside. It's kind of a little bit on a case by case basis and but that's part of what Georgetown is University and we want to be able to support that and so yes, is the answer. But maybe a few more hoops and might you know like people courses.

Cool, if you want to Chinese language for something and it's full. It's full you know that's kind of a reality. But we're definitely eager to work with you try to help. You certainly that's a common thing from a court suits wanting courses outside the school foreign service the college and elsewhere.

But I do think taking a class in a specific language is it really common from a port students. We do allow students to take other elective courses and they make sense within the policy in data science curriculum, so that again. It is kind of a case by case basis. But for folks that are interested in a foreign language. We typically have certainly have not seen that be apparently elective options. So we're going to ship to application deadlines. And the admissions process quickly an will take some more questions. We only have about 10 minutes left in the web and R.

Uhm but our application deadlines. We do have 3 application deadlines are 2 earlier deadlines for early action deadline in our priority deadline. You should apply to those if you're interested in being considered for a scholarship, which we briefly talked about before so early action deadline is December 1 or priority deadline is January 15th. Those 2 deadlines. I think someone had was asking about the competitiveness. Their equally competitive in terms of scholarship in conditions consideration. The only difference in those 2 deadlines is the desicion timing.

So it's really manage the applicant if there interested in getting a decision earlier since they apply by December 1, they'll give a decision by January. If you apply by January 15th. You'll get a decision by March and our final deadline is people. One folks are not guaranteed to be considered for scholarship because at that point. Most of our scholarship funding has been allocated but it's full admission consideration so for these scholarships. You're automatically considered all admitted students be applied by our first two deadlines.

Based on your original application materials so there is no additional application for scholarship.

And then for requirements. I think we talked a little bit about this as well. So we do take a variable istic approach when we review applications. So we don't see it taken a numerical cutoff so I mean, we look at everything that you submit so there is an application. Obviously and then there's a $90.00 application fee will require your resume transcripts from your degree granting institutions for the application process you can upload unofficial electronic versions and then we will need the official versions. If you're committed and planned to enroll so will follow up with you at that point but to expedite the application process you can just upload.

Electronic copies to your application, we require the standardized test that theory is strongly preferred but I do believe will accept the gmat for data science, but Jerry is strongly preferred if you're a non native. English speaker your tile floor to floor. Ielts scores are required and then there's 3 letters of recommendation. We do prefer 1 academic and one professional impossible, however, that's not a hard requirement. We really just want to hear from folks that know you really well. You can speak to your strengths and how you can contribute to our program so keep that in mind.

And then your academic cement, I would say this is probably one of the most important pieces of the application. So there's a prompt that will ask you to to answer and speak to this is really only piece of the application that we can hear from you as an applicant about why you are applying to Horton specifically a DSP program to take some time with it. We are really looking for a good fit. I think we've spoken about that before a genuine interest in our programs. And so you know, don't be too nervous about it is more of a conversation so just telling us a little bit.

About your background in what has kind of sparked your interest in pursuing this degree at this point in your in your academic or professional career and then what skills are you hoping to gain from our program an from a court that can help you in your general career goals moving forward.

And recommended experience we also discuss a little bit about this. But there's no formal prerequisites. So we do recommend. I think a college level calculus course demonstrating some evidence of technical ability. Whether that be coursework or professional experience in computer science advanced math or statistics and then like we mentioned some familiarity with programming languages such as R and Python are helpful that's not recommended.

So we have our key Contacts here. Uhm I will be sending out a recording of this web and R so you'll have copies of this information as well, but feel free to reach out to the admissions office. If you have general missions questions or me directly and then I know Mike and failure. My failure dumb bird art happy to answer any questions. You have about the program curriculum and just talk to you more about it from the factory director perspective, but let's see have a few more minutes for questions.

Sure, so again we don't use numerical cut off. Somebody is asking that the average GPA and Jerry scores admitted students. We need to give a very holistic approach and do not use numerical cut offs. But on average. We do see folks coming in with about a 3.5 undergraduate GPA and then Jeremy scores. Typically, we see an average of about a 160 and the verbal section. I think a 160 to 162 in the Clock section for our data science student section average tends to be a little bit higher.

And then a 4.2 and analytical writing.

And then someone's asking about getting the application fee waived we do have opportunities for fee waivers mostly those are for folks that are affiliated with specific organizations so any military connected applicants any PPA teach for America. Peace core on that list on our website under the FA QS about people that are automatically approved to receive a fee waiver and will do some need based fee waivers. But that's on a case by case basis, you can.

Email SM accorded missions attach your resume and kind of state for need for application fee waiver and multiple look at it.

Alright I think that we've answered most questions that have been asked. In the chat section will maybe have anything else on the left my car. Eric do you guys have anything to add?

OK, that's a good question, we have one one final question until we apply if you apply early action. Is there an earlier date. We would need to commit to the program if accepted on the answer to that is. No, we have one commitment deadline, earliest commitment deadline is April 15, so if you're admitted by the early action or priority deadline. Earliest we would ask you to commit is April 15th. We will have some later commitment deadlines, depending on when you except when your offer of admission goes out if it's after final deadline. Yes, I will put up the contact info again.

And again I'll be the recording will be emailed to all of you and you'll have the slide.

Yeah, so, so on average first semester will be taking a total of course load about 4 courses in those courses are going to be divided in either. It would be a full 2. Two and a half hour course or it's going to be 270 minute courses on there and so essentially you know with that. Initial course word that's that's the time in class that you can expect to spend each week on it.

Average week and then for any of one of those courses. There's usually usually an expectation about 2. Three hours of some kind of reading or homework or some kind of a group assignment or under the feature that says on that, so that's kind of where it is, and so, so one of the things that we want to emphasize that we are a full time program right like an expectation is when you come here that this is what you're doing.

Especially that first semester, which doesn't mean it's impossible not to work part time on the side of that first semester. But as Mike said. It's really difficult. There's a lot of new material usually coming at you from a lot of different angles. Lots credit demand. If your time right from the start, but not only that when we say we're full time course will be mean is that our courses are offered during the day right and so you know you might have course offerings at.

Really is not in the morning or you know later in the afternoon, but they're definitely going to span. The average 95 working time you're not going to be night courses or other features like that, so as of right now that's usually the best verification. That's the course. It's kind of Taper Offen. Individuals kind of go off into trying to look for opportunities in DC that 3 three course load actually kind of light that load a lot, so we only have 3 classes this semester, so some students have more capability of organizing.

And there, we can touch away when they can successfully kind of work part time on the side or having internship experience or use that time to engage with a big science community in DC or red large. Yeah, in some of our courses are in evening electives. But basically you want to schedule your life and so forth expecting them to be during the day, but there are the occasional person.

I just know the second year in particular, fair number of them have you know 2 full days.

Available to work, they will work that out in their schedule that that's quite common.

So great, so I'm very happy to see such a big turn out and to see your interest. And thanks to Lindsey and Eric for doing this as well. And so we're here to talk to you and hopefully this has been helpful. But we also are here. If you have other questions or if there's anything else we can do for you.

Or $80.00 weekend afternoon.