So can I ask
Wil and Mike well, to
turn your cameras back on and then we can open
the open up the floor up
for any questions, please use their raise hand function
or type a question or a comment into the chat function.
Tanya, I see Tanya has also reappeared.
Tanya, have you got a question
Yeah.
When we're taking questions, I'll be happy to ask a couple.
Okay, great.
Shall we go with Alex first, then?
Okay. Alex,
you've been brilliant.
You've asked the question every panel.
And I'm not trying to.
Yeah, keep on.
The couch with the questions.
But they've just been really, really interesting presentations.
Thoroughly enjoyed
the whole of this conference has been great, so it was lovely.
To hear from the three speakers, from Miguel, from
from Wil and Inaki and certainly Will, because I'm also basically right.
I'd love to know more about what you're doing over in computing.
One of the
I sit in the School of Education and Humanities,
and one of the things that we struggle with continually is
is churning out great qualitative research
is they're really good, but they are scared to death.
The quotes, you know, numbers of math. And,
you know, I'm trying to find different ways of approaching
the teaching of stats with students, you know,
potentially just by changing the name of a module
to maybe we call it measurement and meaning
you know, that might be that could be, you know, a possible way in that
the people won't be so terrified.
But what I want to ask the the speakers
is whether you're
whether they think that I guess particularly
well because of what you were saying you were doing.
But whether, you know, gamification
in respect to the learning of how to
how to separate, you know, large data sets
and so on, you know, is that is there scope there?
Is that something that you're already doing?
Is it
I don't know if the speakers could comment
on that within their respective courses.
Amazing.
Okay. So it's like go first.
And it was partly a me
It's not something we're specifically focusing on,
but I do think it's a really interesting point.
And so, I mean, my game connections
partly come from my old school teacher
training course, which is quite an in-depth
technical programing course as well.
So we're doing
computer graphics and shader programing and things like that there.
And then bringing in Python stuff on the data side.
And I think when so I have a lot of students
who, as I say, come from different backgrounds that are less technical.
And that doesn't just mean sort of maybe mathematicians or statisticians
coming to learn that their software skills.
It's also I've had people with a fine
art background and a marketing background and people like that,
and they have a similar kind of issue, I think around both
the technical skills on the math side, they find it quite intimidating.
So as you say, sometimes changing the model
title just makes it seem less scary.
So they don't think ahead of time.
I can't do that.
They actually have a go and find they can
I think making it,
it sounds a bit blasé, but making it interesting
and engaging students to ask each other questions
is the best way that I've found to keep them engaged with things.
So we do quite a lot of
impromptu sort of presentations with the students on the course,
so I will ask them to go away
and look at a methodology that I've just described for
maybe I'll give them 10 minutes or 15 minutes or something like that
and then say, after that
you're going to give me a very short presentation
on how you think that works, and then everyone else
is going to ask you questions and you'll give them answers.
And I think trying to empower them to investigate things
and ask each other questions on the same level really helps
to keep them interested and avoid them feeling like they can't do that
because they can see that other people are
great.
Thank you very much. Well.
Do me well or in yaki want to say should we take megaworld first?
You want to. Yeah.
Yeah.
Just just that we come in because in
my experience in I'm professor in computer science,
so I spoke to engineering students, which is a different thing than then.
We also like to a business is quality with my students are C-level
people, associates and managers and on this kind of stuff
and these are two different worlds completely because
for example in the executive in
the kind of
training they need that should be connected through
through so business has story on
and for example with those years ago to put these managers
into the situation of matching domain competition with color
and that's completely the wrong way
because they will say okay we can get into competing groups
and then you can do that as a single no single metric of the match.
And then we think that's wrong from a business perspective because
because that's completely disconnected to return of investment
how which is the cost of the training.
So, so that kind of situation maybe
could be okay for a computer science monitor this kind of stuff.
But when you want to teach business analyst so this kind of greed
that is different from the hardcore data scientist, let's say
you have to follow a different approach
and you need that the statistics is not an impediment,
but because statistically something like something that is in the process
but you just simply have to be can understand.
Actually, I have an engineering degree and then I have a second
degree in social sciences.
My name is to two statistics in the second degree in social sciences.
The first one, I was only doing my third level and was developing this.
I simply low level for the degree and then we will have statistics again.
This is where I say, okay, that's right.
That's the reason of this is because it was thought very differently.
So I don't think that that might solve problem in in this sense, but
I think.
Yes. So
very interesting questions, Alex, and interesting point.
So in our experience, so in the in this Mastering
Computational Social Science, we are two co-directors.
Okay.
My my colleague is she is a social scientist.
I'm an engineer so we share our experiences
and I'm teaching quantitative methods to social scientist
and one side and two engineers and the other side and
maybe there's some kind of misconception
in the sense that,
like, we think that social scientists are very scared of statistics
and this kind of thing and engineers are not.
But I assure you they are
a lot I mean, random processes
are very hard to grasp, but when you first learn.
Them and.
And also
they start like a tradition on kitchen
statistics that is very rooted on
on their origins and the origins of statistics.
So statistics are the same before it were invented to work
with robust mathematical tools with more data.
But we don't have that problem generally anymore.
So we have computers.
So one thing that I've been trying to do in my teaching
and my courses and on the statistics to engineers
is to start
playing with computers from the very beginning.
So from that, get the very first class.
I mean, I use I talk in a conference
by some data scientist in Pinterest
that shows the audience
how to very quickly and very easily do a permutation test,
which is something that you see a somebody distribution unfold and
and it's
the best way, I think, to learn statistics.
So that's one thing that I am trying to do and one thing that we would
we would be trying to implement in these new masters.
And I've been also trying to play a little bit with gamification.
But I have.
Little experience yet and I tried to
like to use this kind of those in which you put some
game with
your students so that they can build
for you both a question and they can both several options.
And then we can discuss and this is working quite well
and because I have been I've been having trouble lately
engaging them in the blackboard, you know,
and with these new tools, they seem to to engage more.
Yeah.
Thank you.
Thanks so.
Great.
Thanks very much.
And Will perhaps embodies that, starting with computing early.
If he was programing at the age of seven on a BBC
that's fantastic.
But I take that on board.
Tanya, do you have a question?
There aren't any more questions that have been posted
in the more hands yet.
So you have a hand up.
Let's let's hear your question. Yeah.
Thank you.
Thank you very much.
This has been really interesting to hear you how we teach
data skills from different backgrounds in different disciplines.
So I had to the first one goes more to William and invite you.
So you're both in charge of masters who have master's degrees
where teach where students can come and learn about data skills.
And I was wondering because sometimes in this project,
so we were dealing with undergraduate students.
So maybe some of them have been trying to avoid
having courses on these issues.
But then as they advanced in their studies, they realized,
oh, this is something that I'm going to be needing later in the
in the labor market. And so maybe, oh,
there's this master's degree that I would like to take.
But like what happened, for example, in your in in your degrees?
Do they need to have some background or is interested enough
or do they need some technical having already done?
I don't know how many credits in statistics or some.
Yeah, some background or do you just take everybody
if they show enough interest?
So that was one question.
And then for McGill, my question was actually
the situation that you were describing.
I think it was something similar to what we encountered
in our interviews with people working, for example, in NGOs
especially smaller ones in the development sector.
So they've heard about all of the advantages that
implementing a data strategy within their organization can bring them,
but they
also see the challenges and they don't really know
what this will I mean, the, the understand that this might have
there's also opportunities and it could facilitate processes
and augment like managing projects and evaluating them, etc.
But they're still kind of struggling or hesitating to incorporate
this into their work or have a data analysis working for them.
And so my question to you is, this is kind of what you were trying
to explain to your public your target audience.
And so I was wondering if
in the time that has gone by,
so there were two years in the last two years were very special.
But if you know, if somebody if some of these
participants have been able to implement
these so strategies that you were teaching
and then if there was like one most important argument
that you could use to convince an organization, administration
and NGO who's thinking oh, maybe I should think more about these issues
or change something in my work.
What's the like one most important argument to tell them
what you're going to win from this or what in the future?
So, for example, maybe a student or a graduate who's looking for a job
on the labor market, they can use that as.
Yeah,
kind of an idea, too. Hmm.
Yeah.
To get in contact with a company or an administration or whatever.
I hope that's clear.
Thank you very much.
Thank you, Tanya.
Michael, do you want to take
the last question first and then we'll go to Inaki
and Will for the first question?
Yeah, the thing is that
before I left for this period that I have been teaching many people
in a let's say from a management perspective on an NGO,
it's a similar thing because some NGO manager is just it's a memory.
So it's somebody that is not there is not a rapist.
This is does not need to become a data scientist.
And the problem is,
my experience is that where we need to do is not to convince them
about the benefits of analytics or much learning or whatever.
On the opposite, what we have to do is to turn them in.
And what is this?
You wonder whether the costs because that's something
that people don't understand because we are living in there.
I have started doing much learning and I was doing better
learning in the industry around 2000 or something like that.
And I always say I will do better learning in English.
It was not sexy or profitable.
The people I I know, anything goes much better because.
But because another is the problem is that, you know, the vendors can
they say they've made some learning going to do many things.
For example, if they if somebody comes to your company
and they say, I have a model for this particular toy,
I'm going to audition for this particular point
that they really want better.
And if I will, then, you know, one thing that seems like
that's a
good first lesson because they cannot know
because they say some of the thing is that this is a big problem.
We have a process not only much internally
particularly that, and this cost a lot of money.
And these involve many.
For example, we have never played with an NGO of what human trafficking
and they simply don't understand that they don't have enough data.
And even if they have
among the data, if they cost if they want to process
big data or whatever Ibraheem prohibited for them in the short term.
And these are for me, the important thing is something like
you have to be sure I understand how those things are inside.
And this is where I am
in in these lectures and in this play that we have presented
the idea is that is total they do one play
being a data scientist, if they get this right,
and the other is saying that this primary same problem
is trying to interpret the business value of some numbers,
but without going through all the math and statistics
because you only need to know a little bit of that.
Okay. So, so that's basically the thing.
I'm not sure I have long said probably everything
but that does my book
does. That's a great response.
Thanks for that.
So should we go to Newquay first?
If you have a response to changes first question.
Yeah, thanks for the question.
So and
the question was that about the requirements of the Masters, right?
Yeah. Yeah.
So the short answer would be we don't have any requirements
regarding statistics. I'm computing.
Of course,
we value I personally value a lot
of extracurricular activities that they may have
and the interest they, they, they show because I mean,
when I was 18 years old, I didn't like statistics at all.
Now I do.
So when you grow, you start to realize this is important.
And I take this
I don't know this course that occurs on something or by phone or whatever.
So I value this a lot because people grow.
People realize that they need
or they want the other things and that's normal.
But then we of course have some level in initial courses
so the two level of the, of the students, we have
an initial course on our computing.
And I know the initial courses statistics
are wonderful.
I wonder if you had told 18 year old Inaki
that he would be running a data science course
when he was a professor later what he would have said.
I don't know, probably you wouldn't believe me.
Okay.
Well, would your seven year old will have
thought you'd be running a master's course in data science?
No, I don't think so. Definitely not.
I can remember the first class I took where I had to do statistics.
I didn't enjoy it.
Yeah.
I mean, in terms of our
master's program, we the only real requirement we have
is that there's some level of undergraduate degree
to one or above ideally, but there's no strict requirements
on what kind of background that has to come from.
We try and cover everything from the ground up.
Obviously, being a master's program, there's a certain amount of learning
that the students are going to have to do themselves.
But I think the what they bring in, in terms
of their expertize from different subject areas, a bit
like what I was talking about earlier
really adds a lot of value to the course because we get really
different diverse perspectives on all kinds of stuff
when we're having class discussions and looking at the
if I pose a question like how can this particular algorithm be used
or how can you imagine using this statistical technique,
I'll get such a myriad of answers from people from different areas.
I think it's extremely valuable having all of them on the course.
That doesn't mean that, you know, if someone has a background
and has been doing Python and data stuff since
the age of whatever, seven 14 something like that,
they're probably going
to have slightly less effort to put in to get to the same level.
But I think everyone balances out once they get started with the
application of data
skills anyway because very few people have those in advance of the course.
Yeah. Yeah.
Okay. So that's fantastic.
That's been a really great panel discussion.
I think that's raised a few interesting issues
for the types of courses and resources
that you've all been involved in developing
and if there are any undergraduate students.
I think some really good tips as well on preparing if if they're interested
in and taking this forward to master's level teaching.
Thanks very much.
So I'm going to hand over now, I'm afraid, Rachel, we're 3 minutes.
We're running 3 minutes late.
So we take the we've taken some time off here for the concluding section,
but I'm sorry.
I just wanted to say thanks very much
to will make it out and inactive for a really interesting discussion.
Yes. Thanks very much, Kenny.
And thanks to our speakers in that last session
and all the speakers we've had throughout this conference.
It's been really fascinating listening to the various conversations
and perspectives.