Welcome to the University of Michigan
School of Information webinar on the
Master of Applied Data Science degree.
We're so happy to have all of you here
today with us as we broadcast live from
our homes surrounding the Ann Arbor,
Michigan area. This is the first time
that we've tried a webinar in this
format and we think it's going to be
really successful. But please do bear
with us in an instance where we have a
technical challenge, as I'm sure most if
not all of you are practicing social
distance, so are we here in Ann Arbor.
And so I have two esteemed colleagues
joining me today who're going to answer
all of your questions about the MADS
degree from their homes. I'll go ahead
and let our faculty Dr. Christopher
Brooks and Dr. Yan Chen introduce themselves.
Chris, would you like to start?
Yeah, great. Thanks, Amy. I'm Chris Brooks, 
I'm faculty here at the School of
Information. Some of you might know me
from some of the MOOCs that you may have
taken on applied data science, and I
teach in the program and I do research
on learning analytics.
Hi, I'm Yan Chen. I'm a professor at the
School of Information. By the way, my name
is not Experiment Design and Analysis as you might have inferred. That's the name of the course, the MADS
course that I teach, and because I use
that account my laptop sometimes
automatically defaults me to that. So I am
a behavioral and experimental economist
and I study economics of internet,
statistical principles of experimental
design, and behavioral market
design, if you've heard of that name.
Nudges and allocation algorithms and so
on. So I do both theoretical and experimental research.
Great, thank you so much. 
So as those of you who have been
with us before know, we will take the next
hour or so answering your questions over
YouTube. I welcome you to type in any and
all questions that you have about the
MADS degree. I'm fairly confident that
between the
three of us we'll be able to answer most
of your questions, and if we can't answer
your question today, we always welcome
you to email umsi.mads@umich.edu.
with any questions that we're
unable to get to today. So just to get
started I'd like to invite Yan and then
Chris to talk a little bit about their
experiences teaching in the MADS program.
We're really fortunate that both of
the faculty that are joining us today
have produced and taught our MADS
curriculum to current MADS students, and
I'd love to get some of their insights
about what it's been like to be a MADS
faculty. Yan, can you tell us a little bit
about your experience?
Yes, so I taught the SIADS 631 which is Experiment
Design and Analysis. You won't forget
that because it's on my nametag.
So I developed this MADS course from the
residential course that I teach. So I
teach experiment design and analysis at
the undergraduate level and at the PhD
level, so what I do in the MADS
course is to condense and take the most
important and the best part of those
courses and customize it to into a
one-month module and so for every
week there will be online lectures.
The recorded lectures, you will do a homework
assignment in the form of, you know,
reading, data analysis, you get a dataset
every week as a homework assignment that
asks you to practice some of the
statistical techniques we just learned
such as instrumental variable
regressions, difference-in-differences
analysis, and you will also be playing
games on MobLab, and these games are
designed to demonstrate the principles
of the core concept that we learned
in the class. During office hours I
would pool the data that was just
collected from your class and look at,
will look through these patterns
behavioral patterns together. We do not
have a final exam for my module because
all of these core concepts are
incorporated into these weekly exercises
and quizzes so it is a condensed
version of my residential classes, but it
is highly interactive in the sense that
you will not only get to learn the
concepts by listening to the instructor
but also by experiencing the concepts
such as free-rider problems. You
will actually in a MobLab game you would
actually be in a public project and you
have to decide whether to free ride or
not. And so in a chemistry experiment
it's very difficult for you to imagine
what's it like to be a molecule, but for
an experiment design and analysis class
in the MADS program, you would experience
what's it like to be a subject,
a participant in an experiment and an
experimenter. So you will get to
experience this from both perspectives.
That's great, Yan. What I really
appreciate about so much of what you
just shared with us was not only the
breadth and depth of an overview to a
course like Experiment Design and
Analysis, but also your astute point that
in the MADS program we're taking our
residential courses and re-engaging them
in a way that fits into the MADS
structure, but we're not sacrificing the
pedagogy or the rigor of our courses. So
I really appreciate you using examples
such as the use of a tool like MobLab,
which I know you also use in your
residential course when teaching a
MADS course. Chris, can you tell us a
little bit about your experience
teaching in the MADS program?
It was amazing. So I taught in the MADS program one of
the first courses that we launched, the
data manipulation course last September,
and I taught it again in January, so I
would say almost 200 students now have
gone through that course. It was
absolutely amazing. One of the benefits
of teaching in the MADS program versus
my residential program is instead of
preparing my lectures every week all of
those are canned, all of those are in
videos, and I get to spend my week
actually engaging with the students
through the office hours, through our
Slack channel, doing some one-off
examples in Jupyter Notebooks, for
instance. And Jupyter is like a
technology that we use underneath in the
MADS as kind of a foundation for
this stack. So I really found that class
wonderful. We use auto graders throughout
so students in that class are able to do
many different tests and get many
different evaluations of their work. The
other class I teach is a visual
exploration of data. It's a visualization
class essentially, and in that one
there's no auto graders, it's all graded
by our graduate students here at the
university. We also have plenty of -- well, we use Zoom actually for our office hours
and so we have plenty of contact with
people, and in all of my classes I try
and use real data and real datasets and
try and source some of those datasets
from the MADS students themselves. Since
those courses have ended, I've actually
engaged with several of the students who
are interested in some very specific
domains, sports analytics being one.
Several of those students who took the
the first couple of courses with me
ended up doing some sports projects with
me, did some sports projects on their own
and went to the MIT Sloan Sports
Analytics Conference for instance this
year, based off of that. So it's been a
cool way to build cohorts and
collaborations and a lot of people
maybe wonder, What's it like to
do an online program? Am I going to know
anybody? And it's amazing to see
the connections that build between
people and the sorts of projects that
come up.
I have to agree Chris, even
though we're an online program, we use a
number of opportunities, such tools such
as Slack for example, or research
opportunities with faculty, or our weekly
office hours like both you and Yan
talked about to engage our community of
MADS students. And like you I've been
surprised but pleasantly and how many
students we've gotten to know really
well over the last year who've been
engaging our program. Yan, we have a
question about the kind of math
background that's expected in the MADS
program. So as we talked about in our
admissions process, we don't require for
example a specific math background in
order to apply, however at the same time
folks are interested in knowing for
example what the statistics or the math
including things like linear algebra or
multivariate calculus might look like in
MADS. Can you talk a little bit more
about how you use statistics and math in
your course?
Yeah, so in my course is primarily statistics, so we assume the equivalence of STATS 250B at the U-M,
which is the first intro undergraduate
stats class, and you know that class
started, normally starts with,
you know, what's a random variable, and it
goes through, you know, hypothesis testing,
classical statistics, all the way to
linear regressions, and that's where
it stops, more or less. And we basically
pick up from there. We assume that you
know what type I, type II errors are, but
we actually would review it first before
we use these concepts. So for instance,
when we do power analysis, you know, when
you run an experiment, how do you compute
the sample size? You have to know the type I, type II error and
statistical power, but we do a quick
review first and then we build on that.
We would assume that you know how to run linear
regressions and then we pick up from there.
We would introduce, for instance, how do
you estimate average treatment effects,
heterogeneous treatment effects when you
have a panel data structure?
How do you do difference-in-differences
regressions? How do you construct an
instrument, and do instrument of variable
regression to tease our selection from
your dataset? So for each concept that
we introduce, we use a concrete
experiment that has been published and
use that dataset to demonstrate these
different principles and ideas. So we
would assume if, let's say that you've
taken AP Statistics, then you have all
the basic concepts, and everything that
we built on top of that we would
introduce them as if no one
has heard of it before. We'll just
do a very quick review of the stats
concept in, let's say AP Statistics,
[inaudible]. You would
use ... I do not use linear algebra in my
class, but I understand some of the other
classes would. I use a little bit of
differential calculus. So for instance,
when we analyze auctions, we first derive
the equilibrium bids. If
everybody's rational and they're
Bayesians, what do we expect
them to bid? And to do that, you have to
set up the objective function, take
first-order conditions and solve for B,
the equilibrium. So differential calculus is helpful
in understanding these different
concepts that we actually use as a
component in our regression models.
Chris, do you want to talk a little bit about
where linear algebra is used, for instance?
Yeah, there's a couple of places where
linear algebra is used in the course.
It's important to note that the MADS degree
also does have a math methods course
that you would get introduced to very
early on. I think it's the second course
or the third course in this sequence, and
it reaffirms a lot of the concepts that
we're expecting people to know, so a lot
of machine learning uses matrices to
represent data and transformations on
data, and so that's where some of the
linear algebra comes in. Also some of the
calculus comes in when you're looking at
algorithms that use things like gradient
descent, and I believe those are also
introduced in the math methods courses.
Thank you, that was a really
thorough overview to both how we employ
statistics and math in our curriculum. So
as Chris mentioned, we don't have a
prerequisite math requirement. We do have
a statistics assessment that all of our
applicants must pass, so we and you feel
comfortable with the basic statistics
knowledge that you would need to be
successful in the degree. And then as
Chris aptly mentioned, we have a math
methods course that is early in our
curriculum that enables folks to get up
to speed on concepts in linear algebra
and calculus for example in addition to
Bayesian statistics that are all
concepts and skills that we use in
future MADS courses. Chris, for those
folks that are hardware engineers and
have a little bit of scripting
background -- so not as much as a software
engineer for example, but at least some
exposure to -- how easy or hard would the
program be for someone like that?
Yeah, I mean that's a good question. So actually one of the -- we have a few electrical and
mechanical engineers for instance that
are in the program and some of them are
some of our top students. Certainly an
engineering background demonstrates
for instance, like a degree in
engineering demonstrates kind of an
ability to engage with this kind of
material and so I would feel pretty
confident in that. The challenge though,
one of the challenges that we really
look at with all of the applicants is a
programming background, and so are you
able to program. Now that doesn't mean
you have to be a computer scientist or a
software engineer, it just means that you
have to be able to program or be
to pick up enough programming in order
to engage with courses. We do have a
programming requirement, and I
think Amy, you can you can talk more
about that, but largely throughout the
MADS stack of courses we use Python as
the programming language, and there's
lots of great resources to pick up and
improve your programming, including
several MOOCs that we teach at the
University of Michigan, myself, Paul
Resnick and Steve Oney for instance teach
one, Chuck Severance teaches another one
that's quite quite popular. And so I
would say that there's -- this was really a
question about hardware engineers, but
what I've observed is the early need for
mechanical and electrical engineers is
to pick up Python programming, but in a
conceptual level they're able to engage
with the material very well.
Excellent. So to Chris's good point, we do also
require a Python assessment, and we
require it in one of two ways: either you
can take a Python test that enables us
to understand that you and us --
that we can assess you and you
can assess yourself on foundational
Python skill, or you can take the Python
3 certificate in Coursera to Chris's
point that's taught by UMSI faculty and
then you can supply that certificate to
us and that you can use as an
alternative form of competence to the
assessment. I did want to mention that
Chuck, Dr. Chuck Severance's Python for
Everybody is a great introductory course
to Python. It is not the Python that we
assess in order to get entrance into
the MADS degree, so we would require
Python at the Python 3 level, which is
above the Python for Everybody level.
So if you're brand new to Python, I do
suggest starting with Python for
Everybody and then moving into Python 3.
You can either complete the Python 3
certificate or if you already know
Python, you can take the Python
assessment.
So Yan, can you tell us a little bit about
things that you consider in designing
experiments?
Oh no! Okay, well we will wait for Yan to come back, it does happen as we all --
Okay, we're going to take a quick break.
We'll be back in a minute.
Okay, thank you for bearing with us. As we
indicated, we anticipated that there
might be some technical challenges as we
spend this hour together, but please stay
with us any time we have a technical
glitch, we're working as fast as possible
to make sure it gets resolved. So Yan, I
was wondering if you could talk to us a
little bit about how you or what you
consider in designing experiments.
Can you be more specific?
That's a good point. So this question from one of our viewers was simply: What are the things you consider
in designing experiments and studies? So
perhaps you could talk to us a little
bit about kind of the core kind of
curricular components of an experiment
design and analysis course.
Yeah, okay. So let me first say that experiments are
used all over the place, in the tech industry and nonprofits, by the
government. They're not always called
experiments in the tech industry, they
sometimes call it A/B test. How extensive
is it? So Hal Varian who's the chief
economist at Google said informally, but
he allowed me to record him, that there are
at least 10,000 experiments conducted in
Google. Facebook does the same thing, as
are pretty much every other place that I
know of. Microsoft, the most important
business of the day -- again, this is
quoting Susan Athey, who was the chief
economist in Microsoft -- was the Monday
meeting of all the different
executives, and during that meeting they
present first the results of their A/B
testing or experiments, and decisions to
change policies, to change interfaces, are
based on these experimental results. And
so we would basically use lots of
these online experiments to introduce these basic
concepts. In fact, I have my syllabus
right here. So the textbook that
I'm using is called Running Randomized Experiments: A Practical Guide by Rachel Glennerster.
So this is from the MIT
J-PAL Lab, so you probably have heard the
Nobel Prize in Economics was awarded to
Esther Duflo, who was the director of that
lab, and Abhijit Banerjee was also part
of that lab. And we also recommend another
one which is called Field Experiments by
Alan Gerber and Don Green. This is a
little bit dense in terms of [inaudible]. So we we start by introducing the idea of experiments,
of exogenous variations, and we then go
through individual choice experiments,
and the idea. So what we use is the Uber
block, the Uber tipping experiment, which
actually amazingly is written up and
circulated. So remember in the old days
when Uber and Lyft first started? Lyft
had tipping, but you can't actually tip in Uber.
In the app. You can sort of hand cash to your driver at the end of the trip.
So in their 180 days of change, they decided that our customers, our drivers
actually demanded tipping, so let's add
that feature, but let's not just add it
to everyone. Let's look at what the
tipping changes, you know, the attitude of
the driver, whether it changes the
demand from the consumers. And so they
did it as an experiment. They randomized
all the cities in Canada and the United
States into the treatment arm and the
control arm. They used a technique
called clustered random assignment and
within clustering they also
used mark random assignment, and so we go through basically an anatomy of this
experiment and see why do you do clustering, and when you have to do clustering
what are the disadvantages? And what's
blocking, why does blocking improve your
power? And so we go through this
analysis, and so why would Uber do that?
Why don't they just give everybody,
change everybody's interface all at
once? So that's one example. So the
techniques of randomization blocking,
clustering, simple random assignment,
complete random assignment. So you will learn
all these terms, and to find out what
happened to the Uber experiment, you have to
register and take the class. Then we look at
statistical power, for instance.
You decide to run an experiment, but how many people do you need? How many schools do you
need? How many firms, how many
subsidiaries do you need to run this experiment?
And so we look at the statistical
principles behind power calculation, and
we'll give you numerical examples, we'll
give you a dataset and say, you know, if
you're given this data set and you're
asked to design this experiment, how many
subjects do you need in the control
condition, how many in the treatment
condition? So for instance Google derives
about 80% of its revenue from
the position ads. These are sponsored ads,
these little sections sometimes at the
top of your screen, sometimes at the
right hand side of your screen. How
many do they put there? So, you know, is
three an optimal number? What about four?
If you add more, does it detract from
the expected revenue of the top three?
So this would require you know something
about auction theory, but auction theory
is actually not developed to give you a
very precise prediction of what about
having four positions as opposed to three? And for that you need a large randomized experiment
to estimate the effect so when
people land on the page some will see
three ads and some will see four ads.
And because they're randomized they
should have statistically equivalent
samples from the control that people
will see three and the treatment of the
people who see four. And so how do you
compute the sample size? We also look at
various behavioral economic
interventions such as information nudges
for instance when you inject social
norms into an online community. How does
that change people's behavior? And from
that, how do you evaluate the
effect of this type of intervention?
We can look at what did everybody 
do before versus after, then we
introduce the definitive
difference-in-differences regression
technique to estimate the experimental [inaudible].
Yan? I'm sorry, it's interesting that you should talk about online communities and studying online
communities or using online communities
as one way in which we understand
experiment design and analysis since our
program is also an online community, and
in fact someone asked if we use our
MOOCs or our MADS program to research
online learning and behavior. So I know,
for example, Chris, that you do study
learning analytics and are already
starting kind of the process of
understanding learning in MOOC
environments or in online degree
environments. Do you want to build on the
kinds of concepts that Yan's talking a
little bit about around how we might use an experimental approach to
studying online learning?
Yeah, in fact. So one of the MOOCs that I
offered, we took a look at how we, just
changing the backgrounds inside of the
MOOC -- and we used an A/B test and
randomized controlled experiment where
half the people who saw the MOOC saw one
background and half saw a different
background and the video was me shot in
front of a green screen --
to try and understand if there was sense of
inclusion differences between men and
women. And so in the background there
were two data scientists working behind
me either two men or two women depending
on the condition. And so definitely
we shared that at the Coursera
partners conference for instance about a
year ago as well. So one of the things
we're interested in doing is helping
change education through our research
and our practice, so while my background
is learning analytics, that's kind of the
research I do, I think as a school we're
also very interested in trying to move
forward with many of the push these
platforms forward and to bring that
sense of social awareness to them using
the very techniques that we're teaching
in this program: experimental design and
analysis, machine learning, for that one
for instance I did a Bayesian analysis,
and there's an uncertainty class 
for instance that's coming up, one
of my colleagues Matthew Kay and so forth.
Great, thank you. Yan and thank you
for really giving us a thorough kind of
understanding of how one might think
about, kind of conceptualize and act on
experiment design and analysis. Now Yan, I
know that given your background in
statistics for example, you can talk
about other ways in which the program
incorporates statistics. So for example,
we have a causal inference course coming
up that I know is really heavy again in
statistics and econometrics. Can you talk
a little bit about the way in which
we incorporate statistics, really kind of
rigorous statistics into courses like Causal Inference?
Yeah, so Professor Alain
Cohn will be teaching the causal
inference class in the fall. So causal
inference is essentially a set of
statistical techniques that enables you
to tease out causality given a dataset.
So we often see a lot of correlations, so
if you run a large-scale regression
analysis, or a small scale, you often see,
for instance, in the summer we see
ice cream sales go up and the
people drowning in city swimming pools
also go up. So does ice cream cause
drowning?
We know from common sense that probably not.
But how do we know that it's merely
correlational versus causal?
So this course, Causal Inference is
all about the statistical techniques
that you use to differentiate between
correlational versus causal effects in
in a dataset. So for instance, we
originally saw that in this
micro online finance community,
microfinance community, that people who
belong to teams tend to be more active.
So does teams make them more active?
Or we also see that before teams
were created on the website, those people
who eventually joined teams were more
active before. So there is a selection
component. More active people,
lenders are more likely to find that, oh
now there's a new mechanism called teams,
and therefore are more likely to join
and they continue to be active. So the
two competing hypotheses would be
more active people join teams or
teams merely gather these active people
versus, you know, okay, more active people
are more likely to join, but controlling
for that, is there any additional effect
that teams have in terms of making people [inaudible]. And so you
could use, for instance, combining
recommender systems and instrumental
variables, you can create, as an
experimenter you can create an
instrumental variable and which is totally
exogenous to tease out causality.
So that's an example and of course he
covers more techniques, largely
from econometrics but also some are 
from statistics.
Thank you. I appreciate that overview. I certainly
think that it speaks to the ways in
which we think kind of holistically
about the core facets of data science
and understanding that statistics shows
up in a number of different places both
in how we think about and actualize
experiments when we talk about where
there's causality or not, in how we
present visualizations for example, so
really using statistics in a number of
different ways. I'm going to go ahead and
answer just a couple of admissions-related questions before I turn it back
over to Chris. So we are hearing
from a number of folks that are curious
about for example how many students are
in our cohort. So currently we have
almost 200 active MADS students. We would
anticipate adding another approximately
250 to 300 in the fall, so that's really
kind of the number that we're looking
for now. That's a rough number, it's not a
hard-and-fast number. We're very pleased
with the positive response that we've
gotten so far from folks that have
been admitted to and enrolled in the
MADS degree, and we anticipate that it
will continue to scale. And certainly the
feedback that we've gotten from our
students has been largely positive about
the experience that they've had with us
and with each other so far. In an
instance where you do live in the
Southeast Michigan area and you are a
MADS student, you may avail yourself to
on-campus resources. So MADS students are
degree students at the University of
Michigan and for those students that
live nearby, that may mean that you
choose to engage in residential campus
activities. This of course would be in a
post-COVID-19 scenario where the
campus is open. As Chris and Yan and I
can tell you, it's currently not, so we 
don't go to campus and nor do any of our
students, but certainly that would be a
possibility in the future. For those of
you that are not living in Southeast
Michigan however, it does not mean or
preclude you from still engaging in our
campus community. So for example, you
still have full access to the library,
you still have all of the career resources
and access to the Career
Development Office that our residential
students hav. We livestream events in
the School of Information for our MADS
students to join as well as campus
events that are put on by some of our
partners, for example, we have a data
science institute at the University
of Michigan that we will often
livestream or point our students to
their events in an instance where
they're being live-streamed. We've had
several students that have come to visit
Ann Arbor and have participated in things
like a football game for instance. So
there's lots of opportunities for
students to engage in the campus
community, be it in person or online, and
certainly we're sensitive to ensuring
that all MADS students have a full
University of Michigan experience
regardless of where they live. For those
folks that have applied, I can tell you
that we are close to starting to release
admissions decisions. So in the initial
round we indicated four to six weeks.
We're approaching the six week mark, and
my admissions team indicates to me that
they're very close to finalizing the
reviews for that first round and making
admissions decisions. So for lots of you,
you might be thinking about how you're
funding the MADS degree. I will tell you
that we do offer scholarships and that's
actually quite unique to an online
program, and how we do that and how much
we offer, those are things that are
really made by -- choices that are made
by our admissions team with support from
our Office of Academic and Student
Affairs. It varies from year to year so
there's no hard and fast rules on how we
give out scholarships. The good news is
that you don't have to submit an
additional application to be
scholarship eligible, so in an instance
where you're admitted into the MADS
degree you are also automatically
considered for scholarship. And then
someone did inquire about how to
demonstrate English language fluency in
an instance where you're unable to
access a TOEFL center right now. So if
you email umsi.mads@umich.edu
we are happy to provide
alternate means through which you can
test or we can verify your English
language fluency. So we recognize this as
an issue, we don't want this to be a barrier
for anyone. We want you to submit your
application. We're happy to work with you
on alternative means of recognizing your
English language fluency, and by emailing
us we can point you to some specific
resources. And then finally we got a
question about the difference between
completing the degree in a year versus
two. So the good news about MADS and
particularly starting in the fall of
2020 is that we have a really flexible
curriculum. So you can take as long as
three years for example with just one
credit a month to graduate. You can take
three credits a month and do it in
twelve months for example. So there's
lots of flexibility in how you traverse
the MADS curriculum. You can take courses
for two months and then take a month off
and then come back into taking courses.
And we've observed students that have
engaged in this model in a number of
different ways: students that have
started on one credit and then moved up
to two, students that have started in two
and then gone down to one. We see
students that indicate that they want to
move very quickly and we have other
students that want to move very slowly.
So the good news is that we have a
number of different ways for which you
to traverse the curriculum, and it's
really designed with that intention in
mind. Chris, can you talk just a little
bit more about that idea of kind of how
we've designed the MADS curriculum to be
flexible and responsive to a diverse
number of learners?
Yeah, so one of the things -- I was on the committee that
originally designed the program, and one
of the things that we really wanted was
to give that sense of flexibility. And so
the first thing we did is we switched to
one-credit unit one-month-long classes,
and this has a couple of interesting
pieces to it. The classes are I would say
rigorous. There's no padding. You have
four weeks to get through a concept, so
unlike in a residential
class where we're sometimes trying to
fill 13 weeks, we've got 10 weeks worth
of content or nine weeks and we're
adding extras, we're very focused
in the MADS classes, and this allows
people to really get a lot of value out
of them per credit unit that they're
paying for and registering for. It also
allows you to scale how much effort
you want to be able to put in or you
have available to you. So a lot of our
students are employed, either
self-employed or employed by somebody,
and are taking different numbers of
classes in different months based on
their obligations for their employment.
For instance, I talked to one student who
had some things crop up with his
business. He knew that he was going to
have to be onsite for a few months and
so he did not enroll in as many
courses those months because he was
going to be working longer hours. So
that's just one example of how the
flexibility of this one-credit unit
program works.
Great, thank you. So Chris,
you alluded to the fact that the MADS
degree is not less rigorous than the
residential program, that although they
take two really different formats they
still really are based on kind of the
same premise in so much as how we teach
data science and that in some ways MADS
has some benefits, like we would say the
residential programs have, with some
unique benefits as compared to our
residential MSI, for example. Can you talk
a little bit more about what some of
those differences or similarities are?
Yeah, so I think a lot of the content
is very similar or can be very similar.
So our MSI program actually is made up
of several different tracks, and data
science is just one of those tracks.
We also have the human-computer
interaction, social media, a library, etc.
so we're just focused on the comparison
with looking at the data science piece.
The big differences are on campus,
you are locked into three-credit unit
classes or sometimes four-credit unit
classes almost throughout, the classes
are a little bit longer, it's a two-year
degree, but there's an internship in the
middle of it. In the MADS program
there's no internship and you can do it
in as short as 12 months in these one-credit unit classes, and part of that is
because if you're doing it in 12 months,
you're going all 12 months, whereas we
don't offer most of the MSI classes in
our residential master's in the summer
because those students are sometimes
going off to do internships. What I find
from the students as they're choosing
between these two programs is they're
often doing it for that internship,
whether they feel they need to be
residential and in the United States to
do that internship versus they feel that
they can find internship opportunities
of their own or they're enrolling in the
program for job movement either
horizontal or vertical within their
existing field. Most, many of the students
I would even say maybe most of the
students in the MADS program are
employed, whereas almost none in the MSI
program or residential program are
employed, at least at that time. At the
same time there are still these
opportunities to engage with us as
faculty, so I've actually employed some
of our MADS students to do research work
for me, I have the video chats with
students, I've actually given more
advising hours to MADS students than I
have to residential students this past
year. So the online format is flexible,
but it doesn't have to be limiting.
Great, thank you. That was a really thorough
response to how we navigate across our
different programs and acknowledging
that all three of us as University of
Michigan School of Information employees
value all of our programs, and as we
indicated there are definitely some
unique benefits to the MADS degree
while also acknowledging that the MSI
course is still a great program too
depending on what your objectives are. So
Yan, relatedly to kind of what
differentiates MADS versus some of our
residential programs, we of course talk
about what synchronous, like, you
know, where do MADS students show up live,
what's asynchronous, when do
they just, when are they kind of in
Coursera watching lecture videos or
doing readings or assignments?
Can you talk a little bit about when you meet with
MADS students live and what that's like
versus when they're engaging in
asynchronous learning?
I think maybe ... I'm looking over at Yan, I
don't see her, so Chris, why don't you go
ahead and tackle this one for the moment
until Yan comes back?
Can you repeat the question please?
Yes, of course.
I was just asking you to talk a
little bit about how synchronous versus
asynchronous learning works in MADS.
Right. So, it changes depending on
the course, but the majority of the
learning starts with you logging into
the Coursera shell and being able to
watch videos, and these videos are
roughly analogous to lectures. Now
through those videos there might be
various different forms of interaction,
and there's Jupyter Notebooks, so we've
got a programming environment built
right in. The synchronous part comes in
through weekly office hours and in my
courses for instance, we have three hours
of office hours every week.
The instructor I would give at least one
of those sometimes, two of those, and then
we would have graduate student
instructors also give a few
of those. One of the things I did last
semester was a Twitch livestream every
Sunday as well where I would just work
on some Python projects and I would be
able to share those with students, and
people could just drop in and it was
kind of like a hangouts code time where
we could talk about things, whether
they're directly related to the course
content or not. Some of the courses also
use video from the students as part of
the assignments, for instance, small
presentations or demonstrations back to
the students as well. But the majority I
would say of the format is asynchronous,
and one of our goals with the program
was to allow people to be able to
complete any of the courses in an
asynchronous mode if it required. And so
the we use Zoom for our office hours and
we record those office hours and they're
made available and we give summaries of
those office hours as well, and this is
really useful for time zone issues
or other obligations. Now you may have
mentioned I think earlier that we use
Slack as well. We have a degree-wide Slack.
All of our students are on Slack, and you can
think of this like the building that
we're in. These are the halls, the
classrooms and so forth,
and you could hang out in that Slack and
ask questions and keep abreast of
announcements. We have a jobs channel in
the Slack, we have something, a couple of
student groups that have started, one was
called Sports Analytics, the other was
called the MADS Weekly Workout where
people were just trying new
problems to keep their skills fresh. And
we're looking to expand on those kinds
of offerings for the students as well.
Great, thank you. Yan, welcome back You know what, we're just rolling with it.
We're doing what we can do. So to
Chris's point about jobs, can you just
talk just generally about where you've
seen some of our UMSI grads go on to?
So what kind of jobs do they get for
example, or what are those jobs like?
So our MSI program is a lot
more diverse in terms of the areas
covered than MADS currently, so I am more
familiar with the information economics
for management track. So the MSI
equivalent of MADS is information
analysis, I guess. So for the, you know ... and in some courses, some of my
courses are cross-listed with information
analysis and also HCI, so our graduates,
some of them become consultants, you
know, at Deloitte or The Boston Group, and
there are people who have the analyst
title. We also have a fair number
from the HCI track, they
become UX designers, and we also send
people out to Amazon, they become data
scientists, and we have students going to
Facebook, to Uber.
Yeah, a fairly diverse number of companies
and also I think to more traditional
sectors like, you know, the Ford Motor
Company. So those are our MSI
graduates. I think if the statistics is
current actually about 20% of them go on
to PhD programs and yeah a few of the
IEM track, the information analysis, the
information, okay, the IEM,
Information Economics for Management track, there are more analysts,
people taking analyst positions.
Right, yeah I think that that's a really great
overview to where and what kind of roles
our students end up in. So for example, we
would anticipate that MADS students like
our residential data science students
may take on data scientist positions in
large companies. I know for example you
mentioned Amazon, Facebook, Google, Uber,
as well as in government agencies or
NGOs or even nonprofits, for instance. And
certainly the reputation and strength of
the School of Information is shown in
our job outcomes in so much as where
our students go. I know that in conversations 
that I've had with our Career
Development Office that our information
analytics students or otherwise our data
science students, which MADS are, 
are some of the most in-demand
in a number of different large and small
organizations taking on data science
positions as Chris mentioned earlier
within a number of different subfields,
including health, sports, consulting,
finance, amongst others. So I certainly
think that we don't have graduates
yet, but we're very excited to see where
our graduates go, both of those that are
going to propel themselves within the
organizations that are already in
and those that will pivot into data
scientists positions from industries
that they will be leaving. And we're
seeing both as in so much as what our
MADS students are telling us. So Yan, I
did also want to just mention, thank you
for talking a little bit about PhD
students. Although the MADS degree
itself is not a PhD-granting program, we
could anticipate, for example, that one
or more MADS students do apply to PhD
programs. Certainly we're
well-versed as faculty for example in
how to navigate that process, and so we
could see a future state where we are at
least supporting students who want to go
on to get a PhD. So Chris, could you
talk just a little bit about our machine learning courses?
Yes, so we've got a breadth of
machine learning courses. This has become,
for computational data scientists has
become sort of the bread and butter now
of what's done. So Kevyn Collins-Thompson
is teaching two of them coming up,
and people might know him if they've
taken some of our applied data
science MOOCs. So he's got a supervised
machine learning course as well as an
unsupervised machine learning course and
the difference between these is really
the task. Do you know what you're
classifying already? Do you want to
make a prediction task or are you trying
to cluster things together and try and
see what's similar about things? We also
have a deep learning course that's being
put on, and I think we're using CARIS as
the Python library in that. We have a
natural language processing course and a
networks course. The networks course is
being taught by Daniel Romero, who is
also one of the colleagues in that who
helped teach some of the MOOCs. I would
say if you look at our program, the first
third of the program is fundamentals
and getting ready. So there's data
manipulation, data cleaning, visualization
and so forth, and then after the
first third we have a milestone, and that
milestone is project-based. It's two
months, so it's two credit units long, and
it's an opportunity to put into action
a little bit more what you're doing in a
self-directed project. Then I would say
the second third of the degree is really
machine learning and more advanced
topics. And after that there's another two-credit unit project class where you
actually can build a portfolio piece of
what you're doing. And then the last
third of the degree is the applications.
Now you'll still learn some methods
there, but you also take three I believe
different applications courses. We have a
number of them being developed right now,
for instance, social media analytics, but
we actually have a variety that people
are excited about maybe offering that we
haven't done yet, and we've one that's
come up naturally from interaction with
students is about sports, there's
learning analytics which I'm you know
quite keen on seeing developed and so
forth. So in that lap at the end of that
third there's a capstone project that's
a three-credit unit, a portfolio piece
that you would build as well. So I think
that that's an important part of the
program. It's not -- you don't come
out becoming a machine learning engineer,
you come out having a strong breadth
across as well as projects that
demonstrate your depth and ability, and
you get some say in some direction and
what those projects look like as far as
building a portfolio of your own.
So Chris, it sounds like you're saying that
the sources for the projects, although we
may aid you in finding data sources for
example, that you do get a lot of
direction in how you choose to apply
your data science skills in those
projects because ultimately they become
the portfolio that you might use with employers.
Yeah, I think right now the milestone class just
started actually, the first milestone
class just started and students are able
to propose their own projects, they get
feedback on those, they can do them in
teams as well, which has been interesting.
Of course, these could all change as we
as we learn from what works and what
doesn't work in the milestone classes,
but we do have this interest in helping
all of our students throughout our
program not just learn what we're saying
but be able to put it into action and so
the school has this slogan that
information changes everything
and we want to help people change
everything and so if you're passionate
about financial markets, if you're
passionate about health care, if you're
passionate about the governance topic X
or homelessness or these things, then we
want to help you be able to put your
energies and your learning in that, and
so this is really an applied data
science degree. We're not just going to
throw techniques at you. You will get
techniques thrown at you, but we're not
just throwing techniques at you, we're
also giving you an outlet to explore the
application of those techniques in a
meaningful way.
And Yan, isn't it true that one of the courses that you're
actually thinking about is a data
science for the public good or a 
course that kind of focuses on how we
might apply data science to public
problems for example?
Yes, so I'm going to prepare a course called Data Science for Social Good, and so this talks about -- it's
going to be using the data science tools
to do interventions such as we do, you
know, water preservation, we do some
traffic accidents, you know, getting
people to make more zero-interest loans to small businesses in
developing countries. So it's
given towards problem-solving, so we have
a number of social issues, and how do you
motivate people? How do you use the tools
that we learned in the core, in
experiment design, recommender systems?
How do you use them towards solving
social problems? And I am also as we
speak trying to get the data from the
Harvard group on how we feel the new app
that they launched to get people to
enter their health information for
COVID-19. I'm excited to to be
assembling several large datasets so
that the students could use to see
how one might design solutions to social problems.
That's very exciting and what I
appreciate is that you are helping
gather those big datasets because we do
have a number of courses that focus on
big data and how to analyze it and
yet also having a lot of potential
social impact in how we think about and
use data for social good.
So as we spend our last couple of
minutes together I just wanted to remind
folks that in an instance where we don't
get to answer your question you are
always welcome to email us at umsi.mads@umich.edu
and we are welcome to answer your
questions there. I do see a couple of
specific questions for example
that we may not touch on in our last
minute or two together but we're happy
to answer those questions individually
over email. So Chris and Yan, could you
spend just 30 seconds telling us
why you would tell somebody to come
to the School of Information MADS
degree program? So what's your takeaway
on why MADS? Chris, I'll let you start.
Yeah, so I'm a computer scientist by
background. I did not go to a school of
information and so it's been amazing.
I've now been at the School of
Information here for seven years, and
it's an amazing diversity of student
body and of faculty, so I get to work
with colleagues who are not just
computer scientists and not just
interested in the algorithms and really
focus on application, and to me that's
one of the promises schools of
information have for changing the world
for better, is the application of
techniques. In the MADS program 
the application of those techniques are
data science techniques and my
background is computational data science
techniques, so building applications that
are going to use data to change things.
I think that that's a powerful value
proposition. What you get out of this
program will be something that you can
use regardless of the industry that
you're in in part because you can tailor what it is
you want to be doing. A lot of our
students are employed, but you don't have
to be employed. You can do this full-time.
We actually have one or more students
who are on the campus residents in the
graduate student residence
even though there's no need necessarily
to do that. So it's a super flexible
program, and I think that that's the
biggest opportunity here with the MADS degree.
Thank you. Yan, what would you say
to a person that's interested in joining MADS?
You know, apply, and I think it would be a great experience for you. So, the faculty are all at the cutting
edge of research on data science, so I think
you get exposed to the newest techniques and methods.
And the most pressing problems in our society [inaudible], get to know how we approach things.
I really appreciate both of your sentiments, that we're creating an agile curriculum for
folks to come into from a number of
different stages and places in their
careers and lives, and that we enable
them to use things that they care deeply
about to tackle thorny data science
problems and issues for the betterment
of society and to change the world as
Chris indicated earlier. So thank you so
much for joining all of us today. As I
indicated before, please do email us at
umsi.mads@umich.edu if you
have any additional questions and
otherwise we greatly look forward to
reviewing your applications. As always, Go Blue!
