Welcome to the University of Michigan
School of Information. My name is Amy Homkes-Hayes
Assistant Director of Academic
Program Development and member of the
MADS team. We're very excited to have you
here today as we spend the next hour or
so together talking about the Master of Applied Data Science degree
program. As we get started I'm going to
go ahead and let our two esteemed
panelists introduce themselves.
Hello, my name is Qiaozhu Mei. I'm a professor at the
School of Information. I'm the faculty
director of the MADS program so you can
see that time I'm working with Amy and
working with Chuck. I'm working with
faculty instructors and staff to make
this program the best data science
program in the world.
Yeah you've been working for like two and a half years, all of you.
I'm Charles Severance.
I'm a clinical faculty member at the
School of Information. I teach a lot of
Python classes, web design classes, and in
the MADS program I'm teaching two
classes: SI 511 - Databases and
SI 611 - Database Architectures. And I'm really pleased with the amount of work I just
teach two classes and you two have been
working on this for a very long time.
Thank you.
So as I indicated we're gonna be
spending about the next hour together
answering your live questions and
talking in more depth about the Master
of Applied Data Science program
or what we lovingly refer to as MADS. So
I have my phone here with me and rest
assured all I'm using it for is to
collect your questions and give them to
our two panelists who are joining me
today so please feel free to ask
anything that might come to mind about
the MADS curriculum, the structure of
the program, how our faculty broach
online learning and teaching, and what
the Master of Applied Data Science
program is all about. We're also happy to
answer specific questions that are
relevant to the entire group that's
joining us on issues like admissions for example or
what happens when you become a student at the University of Michigan School of
Information. So to go ahead and get
started I was hoping that both of you
could tell us a little bit more about
what excites you about the MADS degree.
Oh absolutely. So as you know I took this
job two and a half years ago, so we
were so excited about building this new
program. In our minds is not just yet
another program in Michigan it is the
innovative program that we take this as
the chance to really build you know the
next generation of professional degree,
professional education. We think this is
the perfect opportunity to innovate, to create something that have ever --
have never existed before. So we're very
excited about the curriculum, which
we will definitely talk about later.
So this curriculum is actually a
good balance between rigor and
flexibility and that is actually
customized and it's innovative just for students
and their education. And that's the thing that's most interesting
that I'm most excited about.
So the thing that excites me the most is that the School of
Information is a school, which means it's
a sort of a big structural entity with a
Dean but we're also small compared to
really large schools, which means we're
the perfect place to bend the rules at
the University of Michigan and evolve
what the rules are.
We've done this with undergraduates,
we've done this with transfer student
admission. I mean we sort of go in and we
meet some part of the university and we
just like bend it, and then we sort of
change it for good we think forever. And
 this MADS class idea did that
same thing, meaning that we were not we
did not feel constrained by the rules of
the university. We felt that if we felt
there was something that was a good idea
we would just send our Dean and a bunch
of other people to the rest of the
university and then fight, to fight
to change this. And so I see that there
are things that are breakthrough in this
that excite me
that I'm not sure that we would have
even imagined five years ago that we
could even ask if we could do but now
we're doing them. And so the the first
thing that I like about this that makes
me very excited is the one-credit
four-week format. I have been teaching for
30 years 15-weeks. Three-credits,
15-weeks and why do we do that? Well we do that because room allocation is
really hard on campus and you want to do
that twice a year, you don't want to do
that too often. But now of course
online we don't have to fight about room
allocation and so it's really easy. So what you actually find is when I'm
gonna teach a 15-week class I often have
four or five really good weeks and then
I got like 10 more weeks.What are you doing the ten weeks? Well I
don't know. Let's just practice calculus
for a while or do something. And so what
I love about this format of the MADS
program is we all can do our best four
weeks or best five weeks and then move
on and then another faculty member can
give you their best four weeks or best
five weeks. And so to some degree you
know you have to work in these four
these four or five weeks but you're also
kind of getting a concentrated dose of
what we think is the essence of the
topic that we're teaching, not stretching
it out to 15 weeks just to do it, so
that your time, you're learning three
times as much stuff in the same 15 week
period and one-credit classes. And so
that to me is is the most exciting
thing here. How you can construct
knowledge blocks. So if you came and
did 30 credits you might do 10 classes,
but here you do 30 classes and so we can
teach so much more and as a faculty
member I feel like for the whole four
weeks I'm like peak. I'm really ... I'm giving you your money's worth
for four weeks and not filling and then
someone else can pick up. And so I think
that that's really cool and it has do with the fact that we have a lot of
faculty involved in this and then they
each get their piece but then they give
you their best piece and I think
that's really innovative. And you know
I've done MOOCs before and I've done the
short forms before. The other thing that
I was worried about that now that I've actually started
teaching, 511 started a week and a half ago, is I've always been afraid that --
to speak, to go at the rigor and pace
that we expect of an online student,
we could not ... we could not do that. I mean
the rigor and pace that we expect of an
on-campus student with the support and the
teaching assistants and all that office
hours and all that stuff, I thought we
couldn't do that online. I thought it was
just gonna, the wheels were
gonna come off, we're gonna push too hard
and the wheels were just gonna fall off.
And they didn't. Things like Slack, I mean
I'm like holy mackerel, this is working. I
mean we got some rigor, we're pushing
you pretty hard and people are learning
a lot and it's working. And so those are
the two, the format is what excites me
the most and the amount of compressed
valuable information we can squeeze into this 30-credit program.
I love hearing you say that the format is
working because as you can imagine we
put a lot of thought and deliberate
action into making the format work so
that's great to hear. Can you tell us a
little bit more, Chuck, about the content
that you're teaching and what really
excites you about the content in SIADS 511 and 611?
Well so the content in
particular the thing that I like about
my course is the fact that I've never
been allowed to teach this material on
campus. I don't know exactly why
that is and I don't think that's gonna
last much longer because it's good
material. Historically we never could
allocate in a curriculum three credits
to teach nothing but database and so
we've always taught law plus database or
this plus a little database or that plus
a little database or oh yeah here's a
week of database and it turns out
that database is actually a course skill
in and of its own it's not just an
add-on skill to a web design class or a
data mining class, it's like learning SQL.
And so when the curriculum had a spot
for two credits of SQL and advanced SQL
I'm like, ah! And my biggest fear at that
point was that the on-campus students
would find out that we had this really cool pure
SQL class which I think that on-campus
students have been advocating for for a
long time. And so it was fun. I'm not teaching any,
well everyone knows Python, but I'm not
I'm not teaching web design or web
building or anything like that,
I am only teaching SQL and I'm like what
is the best eight weeks of SQL that I
can come up with? And that was really
difficult for me because I'd never taught
a class like that so it took me a long
time to figure out how these worked. I've
been working on these classes almost all
the time since May of last year
because we'd never taught anything like
this. And so what's cool now is even this
semester already there is a small
one-and-a-half-credit-class that's borrowing
some of 511 and being taught to 40
students on campus. We're hoping to
expand that in the fall because now we
have a lot of on-campus students that
want to do data mining and they don't
get enough SQL, but now we're going to
take the same material that MADS
was developed for MADS because the
unique focus of MADS on data science, and
it's totally appropriate to have two
classes of SQL in that, now that's going
to improve our on-campus curriculum. And
so I'm really excited about how that
is bleeding from what I've always
dreamed of teaching and was afraid of
teaching at the same time because I
hadn't prepared it but dreamed of
teaching that then is now gonna I think
dramatically influence our on-campus
curriculum as well.
That's a wonderful anecdote because it points to the power
of the short time with which we've been
running MADS and how much it's
influencing already the way that we
think about the connection between our
residential programs and our online
program and that we look at the School
of Information and all of the programs
within it as one entity as opposed to
kind of discrete entities that exist in
silos. Qiaozhu, you also have the
pleasure of teaching in the MADS program. Can you tell us a little bit about what
you're teaching and what makes you enjoy
doing it in this format?
Oh absolutely. So I have just taught the Data Mining I class that we offered right in between
Thanksgiving and Christmas as I am
developing the second course of data
mining, Data Mining II.
So personally I have been doing
data mining research for the past 20 years, so I really enjoy the freedom
and the privilege of making my
research, making my understanding about
how to discover knowledge from Big Data
into classroom teaching, right.
So I created the residential data mining
courses and then you can see that we
have been you know making another level
of innovation to make them two credits
for MADS. And the data mining class
starts with how to represent a real-world
data in two different types of data
formats, so this is almost orthogonal to
machine learning, you know, two methods.
We first talk about what we know about itemsets data, about matrix data, about vector data,
about sequence data, then about time search data, about data streams, about network data and what
kind of real-world information in what
context should be formulated into
each of these recommendations. And then
we spent every week talking about each
particular data representation, about how
to affect if they discover knowledge
discover patterns and computing
similarities and distances from data
formulated in this way. And then how we
can actually apply this to reality
not only to characterize a bigger data set
than you have access to but also to use
them as the building blocks for
downstream machine learning tasks, for instance
as features. So this is really cool, this
is very fun. And then during the four
weeks that I have taught Data Mining I, I also
received lots of very useful feedback
from the students. So the math students,
they have very diverse backgrounds,
they have very, very unique ideas about
how I am going to apply these measures to
really change what I have been doing.
They have lots of business insights
they have gathered from tens of years of
practice. And they gave me lots of feedback too
and many of the feedbacks will be
incorporated into
the next time that course is going to
be offered. And we also receive lots of
requests, right, "Could you cover a little
bit more in this?" and "Could you actually
go deeper here?" right, "Could you tell us
that whether we can just take the
homework assignment, apply it to my own
data and get XYZ for that?" That really
makes me excited.
Me too. So there's a
couple of things that I'm hearing that
really stand out and so one is that
we're iterating in the MADS degree
already and really trying to make it the
best degree that we can in the data
science world for folks that want to
apply that data science knowledge
immediately and in the future and so
relatedly we're already seeing that
students are taking what they're
learning in their MADS courses and
they're applying it for example in their
jobs. So both of you have been at the
University of Michigan School of
Information for way longer than I have
and so can you just tell me a little bit
about what you love about this place? So
what makes the school unique and why
might somebody decide to come to a
program that we're offering?
So I would say the thing that I love the
most about the School of Information is
its balance between kind of a liberal
arts approach to things and a technical/engineering approach to things.
Neither of those is right or wrong to the
exclusion of the other, I think it's a
uniquely University of Michigan thing to
not be kind of like engineering dominant
school or a liberal arts dominant, I mean
a university engineering dominant or
liberal arts dominant university in
that they're both strong, powerful,
world-class, and they're not -- neither
is so big that they can dominate the
other, and so you can choose, as a smaller
school like the School of Information, to
find your path between sort of like the
the North and South Poles of liberal
arts and engineering. And we I think find
ourselves right in the middle of that
with strong connections in both
directions both from a curriculum, from a
teaching, from a faculty perspective and
so I think that it is delightful to be
able to think differently,
so I can go and I can say, you know
that's too technical, we that we can't
teach database with that kind of math
the way engineers teach database with so
much math. Let's think of this a
little more as a language that humans
use to communicate to a really cool
creature called a database system. And
those kinds of innovative ways of
thinking about the knowledge that we're
teaching and using is acceptable here.
You can think outside the box.
You are not stuck trying to be exactly
like the next school down the road that
they teach it this way so we're going to
teach it this way and here's the book
that everybody uses and we're just gonna
follow that book like everybody else does.
We're like no, that's not right.
We as practicing people
have feelings about what the most
important things are and I like the
flexibility of constructing courses that
I think are valuable to students, not
just courses that are sort of imitation
of the school down the road.
Great. Qiaozhu, what would you say?
Sure, I have been in the school for a little bit more than 10 years actually after you, right?
Yeah, just barely.
So like Chuck I have the training in computer science.
and when I was on the
market I was thinking about the school's
motive, that is connecting
information, technology and people, but
not until I joined the school I
realized how lucky I have been in
the school. It's really always
innovating and always innovating in the
cutting edge of different disciplines. 
So in the school you can
actually find colleagues from very
different areas. We have people who are
doing computer science, we have people
who are doing behavior
economics, we have people who are doing HCI, who are doing education, who are doing like
social science research, of course
library science, and they have very
different training, they have very
different skill set, but they have one
thing in mind, which is to use the
information technology to really change
the world.
So they really want to answer the
application question, the human question.
How can we actually use what we
learned to change people's lives.
And this is happening
this is happening very fast this is not
just happening
in research, this is happening in
education as well. When I first joined
the school there was the initiative
of building the first health informatics
program. I was in that initiative
and that happened so we have a Master of
Health Informatics program for like
five years in between the School of information
and Public Health, and we had the
first information bachelor's
degree in that happened. And we
have made innovations to our residential
master's degree. And then we have
this MADS.
Well we're also in MOOCs right?
Exactly.
And between there we have MOOCs.
Right. So things start happening very fast.
We're not actually putting the limit. We're not actually putting the limit on us, right?
But we also never relax, it seems as soon as we're done with the one thing we're got to invent another thing.
As long as we believe that this is going to
lead the world to actually change the
education, the research, we're ready to do
it. And this is really what I like the
most about the school.
That's great. Thank you so much, 
both of you. So we're gonna
go ahead now and start answering
questions that all of you are submitting.
So first Qiaozhu, you can you tell us a
little bit about the math that's
required for incoming MADS students and
the math that they may be exposed to
as part of the degree program itself?
Yes, absolutely. So because we
want the students to be able to finish
the degree within one year
if they take the courses full-time, we do
require that students have the ability
of Python programming and statistics
before coming into the program, so
those are the prerequisites of the
entrance to our program. And then once we
got programming skills and stats
skills, then you will find that we have
the end-to-end data science pipeline
waiting for you. That all the way starts
from how to define problems into data
science problem scenarios, how
to collect the data, how to use
computational methods to manipulate data,
to analyze, to process large scale data
sets, to actually do exploratory data
analysis, and then how to use the state of the art analytics
measures, data mining, supervised machine learning, unsupervised machine learning, deep learning
including causal inference to actually
discover knowledge from the data to help
you make decisions and how to use
visualization techniques to actually
explore the data, to communicate the
results to your audience and to
help you make decisions. And
how to actually apply the knowledge that
you have learned to intervene your
customers, to actually use to build field
experiments and then to really change
people's behaviors. And finally how to
put all these things into the
application domain to actually
create something that's unique for
this domain.
One of our core values in a sense from a prerequisite perspective is prerequisites because we
need them, not for prerequisites just to sort of act as filters.
We want everyone who is ready to
take our courses to take them and so we
don't put artificial barriers of like
three semesters of calculus just to
somehow filter down the students because
I think I'm not sure there's anything on
this planet other than maybe physics
that really deserves three semesters of
calculus. Programming certainly doesn't
deserve three semesters of calculus.
So we have a value that when we ask for
a prerequisite like Python or statistics
we're going to use it, we're not just saying
 we want to make your life miserable
and that cuts down on our admissions.
Our admissions process is
very difficult. Thankfully I'm not
involved in it but I don't know
that a lot of work goes into evaluating
the admissions and I'll bet you that if
we made three calculus classes a
prerequisite you'd have a lot less work
to do, but that wouldn't be our value.
That's not our value. Our value is
not to say, well that's a lot of work,
let's just ask for three calculus
classes. So I think that's the key
thing when it comes to math, that we're
not asking for math, we're not expecting
math just for the sake of it. 
If we feel you need it, we'll tell you and
and Qiaozhu's point about the
biggest part of admissions is success in
the curriculum because it's just not
helpful for anybody
for you to come on this campus and then
like drop out. That's not good.
So our admissions is really trying to ask,
are you going to be successful?
That's exactly right. That's exactly right.
So I have a couple of questions that are
actually fairly easy to answer, so
for example, is the program accredited?
It is. So as a University of Michigan degree
program it's fully accredited and so
rest assured that if you are for
example seeking employer reimbursement
and an accredited program as part of
that process we can say confidently that
we're accredited and can document that
accreditation.
Relatedly we have someone that's asking
if the online degree is similar to the
traditional degree in terms of
recognition. So to be clear the
transcript that we produce in the MADS
degree, so all of the courses that are
listed on your transcript and ultimately
the degree that you get along with the
diploma, none of that is any different
than our residential programs so we're
not for example including any sort of
online designation in any of those
places. The recognition is actually
quite similar if not identical to our
residential program. So let's go ahead
and talk a little bit about some of our
other instructors or courses. So Qiaozhu,
you could you tell us a little bit about
some of the other faculty at the
University of Michigan School of Information
that are teaching MADS courses?
Oh, sure. Who should I start with?
There are so many good ones. Maybe just highlight a couple.
For instance the very first course of the program, Being a Data Scientist, is taught by, is prepared by Professor Paul Resnick,
who is the Associate Dean of Research of the school. He's actually a renowned professor
and a researcher who invented
collaborative filtering which is
the backbone of Amazon that you
actually use every day, what is that
recommender systems. If you use Amazon, if you see that Amazon is recommending
products to you, if Yelp is
recommending restaurants to you,
if Netflix is recommending movies to you,
they are using the technique called
recommender systems that was first
invented
by Paul Resnick. And then we have Chris
Brooks, who has been teaching
quite a few courses related to data
manipulation and exploratory --
exploratory data analysis. Chris is actually
an expert in learning analytics so he
has been using what he observed
from online teaching and the data that he
gathered from MOOCs and from other places 
to actually to build the
next generation of teaching techniques.
And we have Yan Chen, who's
actually a behavior economist so her
expertise is to build field experiments
to answer causal questions to actually
you know apply economic theory into the
reality to show that what mechanisms
can actually help people make better
decisions and conduct
better behaviors and eventually
improve the level of social good. And she's
teaching Experiment Design and Analyses.
And we have Kevyn Collins-Thompson, 
who has spent the first half of
his career in Microsoft,
Microsoft Research,
he's an expert in machine learning and he
and I actually co-organized the speaker
or conference that is the top academic
research conference annually in Ann
Arbor next year. He's on sabbatical, he's
actually enjoying his time in Bordeaux.
And when he comes back you will
see that he's going to teach
supervised learning and unsupervised learning.
And we have lots of many other faculty,
of course Chuck, and also Eytan Adar 
who's the expert in information visualization.
So one thing that I would add is that I
think one thing that's led to the
success of MADS was I'm not sure there's
any school in the world that had more
MOOC experience before this started
right I would say 20 percent of our
faculty have before MADS started
have touched MOOCs one way or the other.
That doesn't mean we weren't surprised a
bit and just think we did MOOCs just taking
it up a level but I mean MADS is
taking -- is definitely up a level of
intensity for MOOCs. But you know we'd
be like Chris and Kevyn and others have --
Paul.
Paul, right. That's right and they -- 
so we've had a lot of MOOC experience
which prepared us for this very
challenging
MADS thing but I think we're very well
prepared to sort of take a big step.
I agree completely. So what's really
standing out to me a couple of things is
one kind of the familiarity that a lot
of our faculty bring to what it means to
successfully teach in an online
environment, and something that we spoke
to earlier is that we purposefully
created a structure that in some ways
mirrors or mimics a  typical MOOC
structure where we're giving folks
really rigorous, robust curriculum, but
we're doing that in smaller kind of
doses so to say  in our one-credit,
one-month-long courses. The other is
really kind of how the
multidisciplinarity in the School of
Information which is something that we
pride ourselves on is mimicked in the
MADS degree because we have folks that
come from economics, that come from
computer science, that come from
communicating dat-- library
science and communication, 
that come from learning analytics and health analytics
and in business backgrounds and kind of
all culminate in the degree experience.
Amy, you are also teaching an online class as well, right?
I do, not in this program, but yes. So I know firsthand.
So you can see that we have lots of experience.
That's exactly it. I know firsthand what it
means to successfully teach online
learners and certainly I think all three
of us would agree that it's not as
simple as taking a residential course,
putting it online and then
saying it's done. Yeah, absolutely. So
Chuck, I have an interesting question for
you. So somebody who's already working in
let's say something like data analytics
and using SQL building models for
example what kind of value would they
get from a degree program like this?
Well they might find the SQL class
pretty easy and so then they can work on
the other 28 credits that will
complement them and so I think that no
matter what your
skill is and no matter what you're
currently working on you're probably a
little narrow, and as your careers
progress  the careers tend to
narrow us and we get better and we make
more money and we get better and we make
more money and it's no different than
even researchers. You progress
down a -- you're naturally moved into a
narrowing thing and I think that to kind
of have your walls busted out a little
bit and been moved into a
professional communications class, part
of it is we don't have a lot of extra
classes and so pretty much you have to
take everything and so, yeah, you have to
take a professional communications class.
And you might say to yourself, why? I'm an
expert in SQL and I can sit there and
communicate with a database all day.
And the answer is, you know, in a few years
you'll thank us. I mean one of the things
that that happens in our on-campus
curriculum is we have this one class
that students complain fiercely about
like, why am I taking this class?
I'm blah, blah, blah and I'm this and I'm that 
and I don't need to take this class. And then like we
ask them like three years later what
was your most valuable class and like
"That was a great class."
Like well yeah we kind of had to make
you do it. And so I think the breadth of
the program even if you're an expert in
one aspect of it could be visualization,
could be SQL, could be big data, could be
who knows what, you probably are missing
a lot of things and in particular the
past five years things have changed a
lot. I mean literally the class that I'm
teaching in 511, the technology
that I'm choosing wouldn't be the
technology that I would choose to teach
in five years ago. And so it also is a
way to update yourself as well. And so 
appreciate the breadth of it even if
though you think you're expert and it's
great to know something and have a
couple of classes be kind of easy
because you're already an expert.
The objective of the curriculum is
to build the end-to-end pipeline of
applied data science. It's really to help
you understand how data scientists think
about problems, how they make
decisions, how it goes from all the way
from the real-world application
to delivering the data science
product and really to change people's
behaviors
and decisions.You know I wanted to take
some of the courses --
I was thinking the same thing
I was thinking, as you were talking about
your course I was thinking I want to
take that class but I was also thinking
I don't want to take 15 weeks of your class
because that sounds too hard.
Exactly.
But I could take 4 weeks of your class and
I'd be ok. I probably could make it
through 4 weeks your class, but 15 weeks,
that'd probably be a little bit harder. 
And I think students might be feeling the same thing
that with this 4-week format you
can get the things you need without
having to deal with 15 whole weeks of
like all the best stuff that you can
come up with for 15 weeks.
Thank you. So Qiaozhu,
can you tell us a little bit more about
how the program incorporates projects? So
as you can imagine folks are really
interested in knowing one how much or
little the program enables them to work
on real problems, real problems in
things like business or healthcare,
education or sports, but they're also
interested in knowing where do those
outputs go? So how much are little does a
program like this support some portfolio
development that ultimately could be
useful in a job search?
Absolutely. So in this program we have three
projects, three portfolio-building projects.
The first two of them we call the
milestone projects, and the final one is
called the capstone project. And the goal is
to actually apply what you have
learned so far. The milestone one
happens after you have finished all
these computational courses. Milestone
happens after you have taken the
analytics and you know machine learning
pipeline courses, and then capstone of
course summarizes everything to choose
the domain that you are the most excited
about, find the data that are really useful
there, and then to actually build
something that is unique, that is showing
that how I am really good at this.
So we encourage students to use real-world
data, to use real-world applications, to
demonstrate that we can actually make
what we have learned to do something
that's completely different. The
same idea has been applied to
residential -- our residential data science
programs, that we have the just one
project course at the end that is called
a mastery class. I have been teaching
that class for three years, so students
come in with
their proposed project and the
instructor will work with them every
week just mimicking what's
happening in a real you know
data science team in the industry. We meet every
week, we talk about the plan and
the milestones and then we finally
deliver the user the product or the system
or the report and their presentation and
then students we actually put that
project in their portfolio. And to be
honest many students just got the jobs
based on that project, so they just
tell their future employers
about, "Hey, this is the project I'm doing
right now," and if you wait for two
weeks or two months this will be
done. And then they got job offers.
So one of the things I like to
do to motivate project style classes is
imagine as a student that you actually
want to do something and you have
four weeks. Don't the first two kind of
work with each other, the first two
kind of are connected, aren't they?
The milestone one and milestone two.
Yeah, yeah, yeah.
But think about this -- think about it this way, that if you
wanted to do something wouldn't it be
nice to have a group of people who were
so smart, just had all these classes, and
a faculty member and teaching assistants
and now you're gonna attack this problem
and you can look to either side to get
help at any given time. So yeah you could
do that by yourself but now when you're
doing this after everyone has taken
all these classes and now you've got
this time for which you're going to
produce a project you're surrounded by
smart people. It's a very
productive way for you to accomplish
something, to be surrounded by smart
faculty, smart teaching assistants and
your fellow smart students, who all
together just went through this learning
experience and now you can really do
something great. And so in my on-campus,
I don't teach projects in MADS, but in my
on-campus classes you know I just find
that the students just want to go
further and the project is a way for
them to just keep on going but
then not lose all the support structure
that we have in the on-campus class,
so it gives the same amount of support
structure, even more support structure
for the project classes, and so -- but now
that support structure is to advance
your agenda not necessarily follow the
curriculum that I've come up with as a
teacher.
That's great and Chuck, building
on your point so you're talking a lot
about how we have faculty and teaching
assistants or an instructional team for
every MADS course, so for those
folks that are interested in knowing, 
what does that mean for the real
help that I'll get? So for example in a
residential program I would show up at a
faculty's office for example. Can you
talk a little bit about the ways in
which students get support from our
instructional teams while they're in
MADS courses?
Well there's a lot of different ways,
we have enough staff so we get -- we
understand that part of what we're doing
in MADS is asking you to accomplish a
certain amount in four weeks, which is
kind of different than MOOCs. MOOCs
are a much slower pace. And we understand
that you might even just do most of your
work on a Saturday and a Sunday and so
we need a way so that you can't lose a
week because Saturday at 4:00 in the
afternoon you're stuck on something. And
so we use things like Slack,
not that we're on Slack 24/7 but we want
to get you answers on weekends, we want
you to get answers in the evenings
and we do that. We staff that.
But I think the thing that's even more
important that in a way is an advantage
that we have in MADS over on-campus is
that not every class but most classes
use some form of automatic grading which
gives more feedback, you get quicker
feedback, you get feedback when you make
a mistake, and it also means that our
teaching assistants are not generally
grading the homework so the teaching
assistants don't have to sit there on
their dining room table on a Saturday
with a stack of papers this tall
flipping through them and spending their
5, 8, 10 hours just getting your grades. By
using technology to do some of the
grading, those 5, 10 hours that they would
otherwise spend grading per week are
spent helping students. 
And so whatever time we invest
in our teaching staff, myself included,
I'm not sitting in my dining room table
grading paper assignments either, I am
using auto graders and that's one of the
things we can do online that's quite
nice but it means that our teaching
staff is focused more on the student
questions by far and not just one hour
of office hours a week or two hours of
office hours per week, all week long. And
the nice thing about it is as long as
the class runs smoothly from a technical
perspective then it's really quite easy
for me to drop in every hour and a half
and then answer one or two questions
every hour and a half.
Usually the questions are right on topic,
the students have watched the
lectures, they've given the old homework
a good try and they're stuck on one
little thing and an hour or two later we unstick
them. And so the nature is it's
not like you're spending hours in office
hours having the material tutorially
taught to you in office hours, it's
you're getting the material but
you're stuck on one thing. And so it's
actually I think more enjoyable for the
students, it's more enjoyable for me and
it's more enjoyable for our teaching
assistants, and the students are getting
quicker response than they would if they
were here on campus. You agree?
Of course, absolutely. So all the graders'
Slack channels are game changers.
Game changers, you're right.
Another important mechanism is the live 
office hours. So it's one of the high touch
options that we provide to students.
So when I am teaching a residential
class I also host office hours.
There will be students coming to office
hours bringing their own you know
questions, problems, but within one hour
you can probably help, let's see, you know
10 students, almost.
And the answers to them, we're not going to
give those other students, right?
In the live office hours or Slack when students
have questions they bring the questions
to live office hours and then we answer
those questions and then if the
students can tell us their
questions beforehand we can even
prepare slides, we can prepare lectures
live with office hours. 
And after that, the live office hours, the
recording will be shared to all the
students. That will actually help many,
many other students. And because 
we have an instructional team
working on every course, every one
of us, the instructor, the teaching
assistants, are all hosting office hours and
we cover different aspects.
For instance in my data mining class I cover
more about the course content
and what you need to know beyond
the videos and my teaching
assistant answers questions about the
homework and also there's another
teaching assistant who was my doctorate
student who's now the professor at
University of Maryland just after the course.
So he's talking about how he has
been using the data mining techniques that we
talked about in his own research.
And that is delivered to all the students, to
a hundred and thirty students, not to like
ten students who are coming into my
office.
Well and again because we use
auto graders there is time for that kind
of interaction, conversation sort of just
serendipitous learning of people talking
and because it's easy to listen to and
monitor all those things lots of folks
can benefit from it. I'm particularly
excited as we see our teaching
assistants that we use on campus and
they're going into MADS, they're coming
back on campus, going into MADS. I think
it's really cool because we have 
a lot of great teaching assistants
I wonder when we will start having
teaching assistants form ads that are
actually from MADS, that'd be crazy, right?
Because we got some really smart
students in MADS. We do.
Well so much of what you're both saying
really is resonating with me and I think
the takeaway here is that MADS students
have a lot of access. They have a lot of
access to the faculty that are teaching
MADS courses, to the larger
instructional team including
teaching assistants and they also have
access to the broader UMSI community, so
that may include for example 
staff who have specific
expertise in things like career
development or alumni who might live
close to where they live across the
United States and the world in fact.
So I'm gonna go ahead and answer just a
couple of questions again that I think
are relatively straightforward. So for
example we have some folks that are
really interested in understanding about
pace. How many classes they can take at
any one time or whether they can take a
break for example between semesters or
months. So yes that's all possible so as
Qiaozhu can tell you better than me
when MADS was conceived it was conceived
as a flexible degree option and what
that means is that we wanted students to
have some capacity to make choices for
example about the number of credits that
they take per month. And so in the MADS
degree for instance we have a lot of
working professionals who might take one
credit in months where they know they
have a lot more work going on in their
jobs and then take two or three credits
in months where they don't for instance.
Or we've had a handful of students who
have indicated that they want to take a
few months off in the summer for example
so they can get an internship in a data
science role and then come back to MADS
after having that internship experience.
So all of those things are possible in
our degree program which I think is
indicative of the way that we talk about
it, it's flexibility and it's what -- its
inclusivity for students that are coming
from lots of different kinds of
backgrounds and working experiences.
Exactly. So how to ensure rigor and
flexibility has been the largest challenge 
in building this program.
Yeah I mean and to some degree what that 
does mean is you can't really stretch it. I don't
think you can stretch it over five years.
I think that the problem is is
that it's not like MOOCs where you can
kind of go in and grab a little bit and
that's tasty and it was great. These things
do build on each other a little bit and
these courses are rigorous and if you
take two years off you might have to go
back a little bit and so it's you
know I wouldn't say that it's completely
flexible I mean you can -- what you said,
all those scenarios, I think we've
designed those in, but this works
with not walking
away for a really long period of time.
Important distinction to be sure.
Of course another reason is that if you come back in five years we'll be different.
That's right.
I have a question. I've had
some students come to campus. 
Have you had students come to campus?
Oh yeah.
How many? Tell me. I mean I've had 
students come to football games and then
come and visit faculty on the way to a
football game. This doesn't have to be
just virtual.
We probably met like 20 students already.
20? I've had like three.
Yeah, we've had many who've come to 
Ann Arbor for example for a visit or who
live in the southeast Michigan area and
so in a number of manifestations either
at events or in individual meetings for
example.
And when we go out for conferences 
we stop by and say hi, we have coffee. We have lunch.
So Qiaozhu for example met a group of MADS students in China.
Yeah I mean I think this virtual is 
efficient but it's nice to think that it's not it doesn't
only have to be virtual. This is a real
campus, we have real people, we have a
real building, we have real hallways, we have real offices, we have office hours and
mine our from like from one to three on Mondays
and you have office hours and I have
MADS students that find out when my real
office hours are and stop by.
That's certainly an option. Not anything
you would ever have to do but
certainly something that you could do.
And it might be years from now, it might be that you end up graduating with MADS and several years
later you finally ... but we've had people
come from very far away.
That's right. So we've been talking
a lot about how MADS encourages
cooperation and collaboration between
faculty and students and the larger
UMSI community. Could you talk a little bit
Qiaozhu about the amount of group work
versus individual work in the degree?
Yeah so well first of all your project courses, we do encourage students to work in groups so typically
students will be working in small groups,
like two people, and with that they
can both enjoy all the great
benefits of collaborating with people
and also they have the largest freedom
to deliver what they have
learned, to actually make it 
their personal project.
And in the regular courses we see
lots of collaborations on the Slack
channels with the students helping each
other a lot and they are probably more
helpful than the instructor and
the TAs because there will be someone
online 24/7. They chat, they ask and answer questions from all different time zones.
I would say one of
the things is that even though we use
a lot of project classes in our
on-campus 15-week classes, I
don't know what fraction do, but a lot of
them do, we don't -- we keep the
group -- organized group work into the
three classes that spend the
whole four weeks on group work
because there's some overhead to making
groups and managing groups and adjusting
groups etc. that in a 15-week class we
can kind of handle it. 
But I totally agree with your
observation that there's a ton of
collaborative learning. A ton of
collaborative learning. Students
will just say I'm having a little
trouble with this and then pop pop pop
pop pop pop pop, it's like way faster
than me or the teaching assistant can
get to it. And I love that, I love like
that's kind of like a self-organizing
learning community and what I love to be
able to do is go into that Slack message
and say "perfect!" and then move on.
I mean someone else has already answered
the question and my job is just to say,
"yeah, that was the right answer" rather
than like, "oh man, I'm running late and I
missed a couple hours" or whatever. 
But the learning is very, very collaborative.
I agree completely.
And beyond the curriculum there are also like
special interest groups that the students
created.
Absolutely, so if you have an interest in sports analytics or finance ...
Absolutely. So since we're talking
about ways in which students can
traverse the MADS curriculum and the ways
in which we make deliberate decisions
about whether or not for example we
offer a group assignment or an
opportunity for peer engagement versus
not can you talk a little bit about the
amount of courses or kind of
conceptually how we think about
foundational math and computer science
and stats versus true kind of data
science topics like machine learning
natural language processing? Kind of
what's the blend in the program of those things?
Well as we said that we're 
building the end-to-end curriculum
of data science, so every course in
our curriculum has the very strong flavor in
data science. We start with 
a set of computational courses
for instance we have Data Manipulation, 
we have SQL I and II,
we have Exploratory Data Analyses, we have 
two big data courses, Efficient Data
Processing and Effective Data Processing,
and these courses are helping us to
build the programming
background and also to teach students
the ability to really play with, handle
large scale, heterogeneous,
high-dimensional data sets. Then there's a
complete pipeline of analytics and
machine learning courses. We
start with data mining courses to
extract features and we have a
supervised learning which means that
machine learning with labeled examples.
We have unsupervised learning, of course.
We have deep learning course, and then we
have a unique course called
Machine Learning Pipelines to talk about how
to actually make the pipeline of machine
learning work. And then we have
specific applications of machine
learning in particular, you know type of
data, like we have natural language
processing course, we have metric
analytics course and then we have causal
inference, and that's an area missing from
the big data science program.
And then we have a set of courses that are
trying to help us deliver the
data science results to the audience. We
have Information Visualization, we have
Communicating Data Science Results, 
we have even the course called
Presenting Alternatives that's being built
right now. And then we have courses that
are applying all of these into real
scenarios.
We have application-driven courses like Social Media
Analytics, Learning Analytics, Search and
Recommender Systems, and we have
Experiment Design Analysis.
That's great. That's a really thorough
overview of the way in which we tackle
data science at a more kind of topic
level and how much of the curriculum is
really, truly kind of focused in those
data science topics where they use good
programming and good math for example
but we're not necessarily focused so
much on offering like lots of math
courses for instance. So as we think
about the courses and the
opportunities for engagement and how
students might apply their learning,
research obviously also comes up. So can
you speak a little bit to how much or
little students might have an
opportunity to participate in faculty
research?
Absolutely. They have all the opportunities 
as the residential students have and in fact it's
actually even easier because 
they have more flexibility in the course
schedules. If they have time 
they can reach out to faculty
members involved in their research. There
are actually students who have already been doing that.
So when I think of
like getting involved in research at a
master's level, of course the master's
degree is not aimed at getting you
involved into research and so
what's cool about research at the
master's level is because it's kind of an
optional thing it's not like you have to
feel bad if you're not doing it. 
I always tell students to think of the
classes that they're taking as like ways
to make new friends with faculty members.
And so you take a class from
somebody and you kind of figure out the
measure of their work and we all sort of
expose our work in the classes that we
teach, we don't -- we always take the lens
of our research interests,
we don't hide that. And like you say you
now have kind of -- you've met somebody,
you've got to know them and the research
is usually something happens after
you've met somebody and you say, "you know,
you kept talking about sports analytics
during this class and I'm interested in
sports analytics," then ask them a
question in Slack and then all of a
sudden ... and then it goes.
And so I think that's one of the joys of
taking a master's degree and just
playing with research. It's not
like you're a PhD student and
if you're not doing research 
you're really unhappy
he's like your time, your
clock is running. And so you can look at
your master's time -- I'm not
I'm not recommending people but if you
run the slower path you get more
exposure and more time to fit things in.
I know when I did my own master's
degree which was a residential master's I
did five years and I was frustrated
it took so long but I got to do a lot of
different things in the gaps in between
my classes. And I'm curious
what you say but my recommendation is
the best way to get a faculty member to
want to work with you from a research
perspective is not to tell them so much
what you're interested in and say you
know, "I'm interested X, Y, Z, why don't you
help me do this," but what's
important is for you to figure out what
that faculty member's interests are and
then walk in in a way to help us. The
example I give is let's say you'd
never played basketball in your whole
life and you'd really like to learn how
to play basketball and so you'd come and
go into the University of Michigan
basketball coach's office and say, "why
don't you teach me basketball?" Well
that's not gonna work very well
because our basketball coach has
people that for 18 years of
life did nothing but basketball
and so you got to figure out what's
gonna be good for them and how it is
that you make that initial contact with
faculty is you should understand their
research. You should not be like,
"here's my great idea, you should help me
make my great idea work." I mean I'm
curious if you would agree with that advice.
Well yes and no actually. I think
for lots of us it's different than that.
So a student comes into our office
and saying that "hey I'm really
interested in your research, here's
your paper that I have read."
And that makes us excited. And even
better than that, "here's something that I
think if we did this instead of that could that have
been better?" That would definitely
intrigue our interest. But there's
another type of interest that the
students coming in bringing their
very unique problem, saying that
"oh here's actually a problem that if
we could solve the problem that we can
actually make you know a million people
lead better lives, but I don't know how
to solve that. Do you think
this is a real problem and do you know
how to solve it?"
And so the way that works for me is often in 
project classes where the students pick
a project and I'm just kind of like
the you know the grader/mentor/guide of
that project and I'm all of a sudden
like wait a second, that's kind of
interesting what' you're doing. And so now so
at least they're doing something that I
can look at and say you've made some
good progress on that, let's see
what we can do with what you started
with and I've actually had that happen
to me in my on-campus classes. I'm like,
holy mackerel that's a really cool
project, let's keep talking.
Absolutely. So we're at the hour but we're
having such great conversation that
we're gonna go ahead and continue for a
few minutes so for those of you that
have to end your participation in
the webinar because you have additional
commitments throughout the day,
thank you for joining us. This
recording will be available and so if
you want to catch the end of it in an
instance where you have to depart now we
welcome you to do that. For those of you
that can stay we're going to go ahead
and take just a few more minutes and
answer a few more questions. So I have a
few demographic questions that I
will happily answer because it's related
to the kind of data that I track in my
role of thinking about and acting on who
is a participating MADS student. So for
example we have one question about what
does the typical MADS student look like 
and the good answer is that there's really
no typical MADS student. So if you were to
look across for example years of
professional experience we have about
20-25% of folks
who have very little professional
experience so about zero to two years
and then conversely we have about 20-25% of
MADS students who have 16 or more years
of professional experience. And so what
this is enabling us to do is to have
some really great conversation and some
informal mentorship for those folks that
have been working professionals for a
long time and those folks that are
relatively new to being working
professionals.
Relatedly we have some folks that are
asking us about the typical size of
MADS cohorts. So I can tell you that
right now we have about 185
active MADS students. We would
anticipate that our next cohort and
future cohorts would look about around
that number you know somewhere around
the 200 you know 250 number of
students who are kind of coming in and
joining us. So it's a great number of
students to work with, we have lots of
activity as both Chuck and Qiaozhu have
pointed out and that's something that we
certainly want to build on which is to
continue to invite good, strong cohorts
of students, not something
unmanageable but conversely not
something that also feels like it's
perhaps too small to really meet our
objectives of building this strong community.
Is that 200 twice a year or 200 once a year?
It depends on the year, Chuck. So for now
we had a fall start and a
winter start. We're going to --
our application for our fall 2020 start is
actually open as of now so we strongly
encourage folks to start their
applications. So we'll start another
cohort in fall 2020 and then we'll see
what happens.
We see a future where we're likely
recruiting multiple cohorts a year
but we're just not quite there yet so
for now we're focusing on our fall 2020
starts. Yeah absolutely. Great question.
And then finally we're getting a lot of
scholarship questions so I'm gonna just
address this in a somewhat superficial
way. So I will tell you yes we do
offer scholarships and that's somewhat
unique for an online degree program and
in this last round we actually offered I
can't give you the exact percentage but
I can tell you that we gave out a lot of
scholarships to incoming MADS students. 
So in an instance where you're applying to
the MADS program the good news is that
you automatically are considered for
scholarship consideration and what that
means is that when we review your
application for admission we also review
your application for scholarship. So we
do offer scholarships. We can't disclose
at this point what our scholarship model
will look like for this next year but I
can certainly tell you that that is
something that we do and we're always
happy to have a conversation with
someone around the cost of the degree or
how they might fund the degree or the
ways in which they can pay a tuition
bill. So the other piece of good news is
that the University of Michigan has a
structure in place where we have a
couple of different payment
options that people can pursue. We also
have a lot of folks that are pursuing
employer reimbursement or participating
in education through employer programs.
Amy one thing I think you should amplify
is that there are humans involved in
this process and it's not like you're
going to call an 800 number and then hit
your touch tones like press one, press four, press twelve.
Wait a sec, I don't even have a twelve on my phone.
Meaning that people should ask ...
People should not ... it's humans
involved in this, right. Humans that care.
Yes, absolutely right. So you're never
more than a Slack message or an email
away from from a real human.
That's exactly it. Absolutely. And then
finally we have folks that are really
just interested in understanding what
kind of access we give to University of
Michigan resources. So the three of us
have probably not surprisingly been in
conversations together in groups where
we talk a lot about how do we ensure
that MADS students get access to the
resources that they want and need. So the
good news is that MADS students do have
access to most of the the same
services that University of Michigan
residential students have, so
things like the library and buildings on
campus and recreation and opportunities,
so all of those things are no different
for MADS students than they are for
residential students.
They get ID cards?
They do. You get MCards where you can use them
for student discounts, none of that
changes. And you don't actually
physically have to come here for an MCard, 
yeah, you don't have to.
Yeah so in an instance where you're living in a
place that's not geographically
convenient to Ann Arbor, Michigan we can
happily work with you and the MCard
office to ensure that you get an MCard,
yeah.
Are we the first people that did that?
Let's say we are. I don't think I can
verify that claim but we're going to make it.
I love bending the rules. I love us changing the rules.
So can you talk a little bit about the job outcomes and
kind of the way in which you're seeing
the professional landscape for data
profession -- for data science folks
changing and evolving?
That's a good question.
That's a good question
I'll start and let you think
of the real answer so I'll make one up.
So I don't have the data, Amy, that you
have. I would say that a large
fraction of our MADS students already
have a career and some of them
are exceedingly successful already and
so you're talking to our
master's students like, did you get your
internship, how is it going to go, you're
going to miss class because you're doing
interviews, that doesn't feel like it. And
the ones that I've talked to at length
and personally they're sort of thinking
this of this is like, "I'm going to grow,
I'm doing great, I could just
keep doing this forever, but I want to do
a growth thing." And so I think what we're
going to find is that it's not about
getting the job the way it is so much
for on-campus cohorts, it's about where
this lets them go. And I think that it's
going to some degree be undefined.
I think that they're I would say
that the ones I've talked to are
satisfied in their job, successful in
their job, think that there could be more
and are going to let MADS kind of show
them the path to more, rather than
on-campus it's like, "I don't have a job, I'm
gonna get a master's degree, I'm gonna do
an internship, I'm gonna get a job and
then I'm gonna be good and pay off my
student loans," or whatever. So it's
just it's more of like it's an
opening of something that they're
already in a good place. And maybe I
haven't talked to enough students. I haven't yet met a
student who's thinking of this as "I'm
going from not job to job" the way a lot
of our master's students on-campus
think.
Yeah so we have been hearing lots of
discussion about you know data
scientists are the sexiest job in the
21st century. There's a big lack of data
scientists but the question is where
are those jobs? I have been
working with lots of companies, 
lots of partners, lots of my friends
in this domain. We realized that the
biggest lack of these jobs are not
in secondary. They are everywhere. They
are in more
traditional businesses, every
disciplines, every sectors
and we have the biggest difficulty of
finding the right people. And why?
Because they not only need people 
who have data science skills.
They need people who both have data science skills 
and understand the business.
So the question is how can you actually train people into these people.
And the answer is if you know business already then come here and get your data science skills.
And that's more
efficient than if, you know, you may have
the tools, you may have the hammer, and
you're going to look for the nails.
So in our experience with residential
students we have worked with
residential data science students,
most of them found jobs in
places that when you heard about that
you first think that, wow, and they said,
"Oh, that's reasonable." For instance
we have a student who graduated from our
MSI program, he spent a few
years at the university in the Development
Office, and then he went to California.
He's now the Associate Dean of
Development at the University of South 
California. He wrote a book, which is
Data Science for Fundraising.
And who would have guessed. Who would have guessed.
Exactly. And we have students who've just done the capstone project from the
program and then got hired at Mayo
hospital and now manage
the data science team there. So
these are the places that are lots
and lots of jobs available, in
hospitals, in transportation
companies, in Ford, in General Motors, in Nike, in sports teams,
in universities, and you know
in many other, like in Home Depot.
So they can't find people who know both the
data science skills and the business.
This has been something that's been from
as long as you and I have been here. This
is the the nature of the School of Information.
We produce graduates
that are are hyper-valuable in lots of
places that you might not expect and 
so it's sometimes -- it's not
that there's some line that you just get
in the back of the line and when you get
to the front of the line you get hired.
It's everybody is a bit more of an
individual and I think we spend time
trying to train people in our
curriculum how to communicate their own
skills because they're not just getting
in a line to get a job right they've got
to be able to have a portfolio, they've
got to be able to communicate what they
were. I mean I remember when we graduated
our first bachelor's students we
spent a lot of time explaining to them
how to explain to their job what it was
they were good at and and they all got
jobs and they were all great jobs and
they were all many ways surprising and
like holy mackerel. And part of
it is that there are lots of places
in this world especially data mining
places that they're not sure exactly
what they're doing and if you brought in
like 10 PhDs in computer science and
drop them in the middle of this
organization the place would explode the
next day because there's just too much
like intense brain power there whereas
if you have an organization that's
thinking about data mining and they need
to bring some talent in one of our
graduates has softer edges and
will figure out what needs to happen in
this organization and how the
organization needs to change and not
just say "I've got nine skills and we're
gonna use all of them this week and
you're gonna change to meet me." Our
students are trained to figure out their
environment and then have
a whole bunch of skills. They might not
be super expert in any of those skills
but they have so many skills that
whatever it is that organization needs
then they can grow that skill in that
organization. And so I find a lot of
our students over the past decade tend
to change the organizations that they
become part of in very good ways.
Exactly. That's what we are extremely good at.
So we've really talked I think about 
what makes the School of Information
University of Michigan
School of Information graduates unique,
how they make value-adding contributions
to not only the jobs that they go into
but how those professions evolve over
time and that they're equipped to manage
those evolutions and that a degree like
the MADS degree is applicable for folks
that are launching data science degrees
as much as it's applicable for folks
that are now in data science roles and
want to really bolster those skill sets
or folks that are working in industries
where they see data science problems
that they want to solve.
I also think
there are people who evolved into data
science from some career and they don't
feel they have the credential to move
into leadership in data science 
and so a lot of data scientists are
self-made over the past 15 years as the
fields evolved and people may not have
the confidence nor the credential to
kind of move into say a leadership
role or have a broader -- just it helps
people say "I'm gonna grow a bit" to
have a credential.
The best data science
practice always happens within the
context and if you know
the data science techniques and you
know the context you're going to be the
leader.
Great points. So we did get
a couple of admissions based questions
and given that we're coming to the close
of our time together I will offer for
folks to go ahead and email us at
umsi.mads@umich.edu. In an instance
where you have a specific admissions
question on things for example like what
Python or statistics MOOCs should I
participate in in order to be prepared
for the degree or what's the TOEFL
requirement in order to be a competitive
MADS applicant: so we're happy to answer
those admissions questions. I will also
tell you that our website is a wealth of
resources on what to expect in the
application process and there we've also
outlined exactly what our admissions
criteria are
including information on things like again
TOEFL, our entrance assessments, ways in
which to prepare for Python and
statistics. So as we wrap up I want to
give both Qiaozhu and Chuck just a
quick opportunity to say any last kind
of burning things about MADS or the
School of Information.
I'll go first and I'm gonna actually riff a little bit on
your admission thing and you feel free
to correct me if I'm wrong. One
thing that has been I think a joy
to me and something I'm very proud of at
the School of Information is our
admissions process not just for MADS but
for our undergrad, for our community
college transfer, for our master's, and
that is that I like to tell people we
don't care so much about the numbers.
We don't take a big spreadsheet
and sort it by GPA or GRE or whatever
and I understand that that it's easy to
do that if you're a highly desirable
school, to just take and sort the
spreadsheet by numbers and just have a
thing and throw away the bottom 80% and
then just kind of play around with the
top 20%, because we believe as a
core value that that's not the top 20%.
The top 20% can't be characterized by
numbers, by entrance scores, by interest
in admissions. And the way I
think of it is our admission process
leans toward trying to produce a cohort
of people that you'd like to hang out
with. A cohort, a good group of
people. This is a group of people that's
diverse, it's different, we bring
different perspectives, we're not trying
to find all of the calculus geeks and
then just have nothing but calculus
geeks here. And so I encourage people to
share who you are in the admissions
process and not try to meet some
imaginary perfect template and there are
so many places in this world where the
admissions process is a template process
and you make one mistake and you're
kicked out. Again I'm not involved in it
and I'm really glad that I'm not
involved in it because I know it's a ton
of work to think of each person as an
individual and admit them as an
individual. How did I do Amy?
You did pretty well Chuck because we do use a
holistic review process and we are
looking at the whole person and
everything that that person brings and
why they want to be
here when we make admissions decisions.
I think it is our value, our core value to
not be so big or so awesome that we turn
it into numbers. We're all people. 
Everybody is a person and the
students are people too and so 
that's my wrap up is that
especially in this situation where
the geographic distance is getting
bigger I don't want that friendship,
intimacy and human aspect to go and I
think we're working very hard to not let
the human aspect of -- what it
really means to have a good education
includes people and it's not just facts,
figures and quizzes but it's people.
I agree completely. Qiaozhu?
Well yeah. I agree with whatever
Chuck said. So I just want to end with
that this is a Michigan degree,
so we're trying very hard that we
have to meet all the Michigan standards
and we have to be the leaders of the
world. We have to keep innovating
otherwise we're not Michigan. We
are fortunate to have star
instructors like Chuck, we have the
administration juniors like Amy,
we have our administration team
that's very visionary and who spend
lots of support on this and we are even
more lucky to have you and we wanted to
work with you and to build this program
the best data science program in the world.
So well put. Thank you so much to both of you.
So it was such a pleasure to
be here with all of you today
Python guru Dr. Chuck, Program
Director Qiaozhu, me. I mean I can't think
of two better panelists to start us out
on this next MADS application process
and journey. And so for those of you that
are here just a quick reminder this is
not our last webinar so for those of you
that want to join us again
in instances where we're focusing on
admissions more specifically or career
and professional development more
specifically I would welcome you to come
back, ask more questions, meet more folks
in the University of Michigan School of
Information. But otherwise thanks again
for joining us and as always Go Blue.
