So I make that 2:11 or so.
I think we'll we'll get started.
Welcome, everybody.
If you just joined us
to the Development Council, Dissemination Conference.
Tanya, is there anything that you want to say
before I start with introductions?
No, she's just disappeared. Okay.
So I'd like to welcome you all to the last panel session
of this conference.
As advertised, unfortunately,
I was just saying that Heather Krauss,
who's founder and CEO of We All Count,
unfortunately, is unable at the last minute to be with us,
thus due to a mix up in time zones.
So there you go. Even a data
a data conference can get the data mixed up.
You have to ask the right questions.
I'd like to introduce our panelists.
Will says who's going to speak first Will
says that
he started playing games and his BBC micro when he was seven,
which got him interested in programing
and he became particularly interested in artificial intelligence
while studying at university.
So that led to a doctorate
and into applying optimization and AI to engineering problems.
And he's now the course leader of a new
masters in data science that will be
that has begun here at the University of Gloucestershire.
He'll be followed by Miquel Angel Cecilia.
I hope I've got this pronunciation right, Miguel.
Miguel is originally from Madrid in Spain.
He has a degree in computer science, and he's worked both as a lecturer
and for several companies in the ICT sector.
He's now with a Ph.D.
in computer science.
He's a lecturer at the University of Alcala
and a virtual tutor at the Open University of Catalonia.
And finally, Inaki UCAR
is a postdoctoral fellow at the UC three M Santander Big Data Institute.
So I'm looking forward
to hearing what you have to say in the theme for this last session.
Is having discussed some of the issues to do with the data
revolution, we're now moving on to the implications for teaching data
skills in the university and beyond, which was one of the key issues
that the Development Council project was very keen to inform.
So I'm looking forward to seeing if we've learned
the right lessons as far as the panelists are concerned.
And if you stay on to the end, you may hear more
about some of the materials and the outputs of the project.
So we'll begin with Will If you're ready
Hi. Yeah.
Thanks
for the introduction.
Yes. So thinking about the,
the data revolution and whether it's had
implications for teaching of basic skills at the University
of Gloucestershire, well, obviously as a start.
You know, we've started this Masters, as Kenny was saying.
We also have two undergraduate courses
One is a level six data scientist apprenticeship,
and one is a PSC and Applied
Artificial Intelligence which cover similar areas.
All three of those courses sit within the game's
technologies area at the university,
which gives us access to some nice computers and things use there.
So I started teaching in these data related topics kind of after
the data revolution.
It started because I think it started probably.
I mean, deep learning kicked off sometime around 20, 11, 20, 12
really started to gain momentum.
And then this began to phase more into the public
consciousness, probably around 20, 15, around that kind of time.
I've been
working around the edges of the field for a little bit longer.
I developed an interest today on my undergrad
and I think in recent years one of the biggest changes
that I've seen in the teaching is that people have become far
more aware of the ethical issues in the field.
I had the previous speaker start to talk about some of this as well,
and I think these ethical issues take a lot of different forms
from dealing with data in unbiased ways,
which is slightly more complex
than just being a decent person, although that's a good start.
All the way to legal requirements and things like that.
So I've made sure that those are included in our courses,
and I think a lot of courses around the world in general
and also within the courses
that I've worked with over the last couple of years are starting
to include these elements as well and really look deeply into them,
which is a positive step.
But there's always room to go further and try and teach these things more.
I think we've also changed the way that we teach things,
so we tend to move on from
certainly in my area at least, we're not doing so much
of the large lecture, students listening, that kind of thing.
We're doing lectures in computer labs.
We're also more often doing sort of short 20
or 30 minute sessions, followed by a practical session,
followed by some more theory, things like that, trying to help improve
of the learning of technical skills, which could be quite difficult
to digest because normally there's this quite
small amount of theory to teach, but it's got quite a lot of depth to it.
So I think that's made
a big difference as well
in terms
of who I'm working with or who I'm teaching more for me.
What's been really interesting about starting the MSI
is that it's specifically designed as a conversion course for people
who maybe don't have such a book
around in artificial intelligence or data science or computers.
And I got a really broad range of students from all kinds of areas,
and I think that's becoming more common because although
the deep technical skills are kind of
perhaps not required but are very, very helpful indeed.
If you want to work in in data science or in data cognate areas,
but also as last week was staying as well, it's
hard for data scientists to work in glorious isolation
or people who work with banks, which all you generally need to work
with domain experts, people who know about the kind of datasets
that you're working with.
And one of the nice things about someone who comes from
perhaps different field is they may well have insights.
And if that's the kind of field
they want to work with data in, there's a really great strength there
in that they can potentially be both the domain expert and the data expert
I think it's important that they
address the technical skills
they need to pick up, but it's certainly great
to see people coming in who can use their skills from elsewhere.
Within the data field.
Just to touch briefly
on how the pandemic touched it, and then I'll finish up.
I think a lot of the teaching moving online was probably smoother
for us than it was for some because we were already recording and
and live through just before
the pandemic kicked off
But it's been really interesting to see how some students
have responded extremely positively to online teaching
and to One-To-One interactions with teachers
and others would much prefer to get back into the classroom.
I think we haven't quite reached the point where
we're using online technology so smoothly
that we could replace everything that's interactive,
but it's definitely demonstrated
really nicely that there's a place for that
and that it's possible to do that even in really technical domains, like
the data domains.
So that's that's me.
Excellent.
Thanks very much.
Well. Fantastic timekeeping.
So next we have Mike.
Well.
You know, and can we share a couple of slide like.
Yes, sure. Yeah, I wouldn't know.
That's probably true.
Yeah.
I should be, I think.
Yeah, I agree.
I you see my slides now.
Yes, we have that. Yeah.
Yeah, it's okay.
Thanks. For having me here.
I will be very quick because, you know,
I'm also the director of the Master of Data Science and my university.
But today I wanted to share with you a couple of minutes
about the outcome of this Erasmus Plus project
that ended in 2020 and it was called Dataset.
But that's education and data scheme
for in-depth analysis for business of future business owners.
So it has nothing to do with university courses
and it's typically to be delivered and to make on a visit to school
or even Southern University in some sort of entrepreneurship
school or something like that.
But there's no regular university course and
this is right. And
the first thing is that of course the students of,
you know, very different content from universities
and a lot of the parties, these things that you can see here,
probably most of you recognize that this is a business model campus,
which is something like the de facto standard
in almost every business in school.
Across the globe nowadays.
It's part of a methodology called unified, but
it's basically a way which entrepreneurs put together
in a single page, all the essentials of their business model.
And the problem that we have with periods
when and temporally is went through and they start a business
that is related to data competing values, analytics on this kind of
stuff that we label, I think that businesses
is that at some point in this canvas that we have typically in business
schools, they struggle with so they struggle
with because the struggle comes because they don't really grip.
And those do not necessarily understand
was data cited as was training them or they have this training.
All of these metrics from a model can be connected then
to profitability or to business value. So
the brilliant
was about trying to fill that gap from,
you know, an entrepreneur that knows about some business idea,
but they need to connect the points to it
and to really make sense of their of their business model.
And that's the outcome, the final outcome.
I'm not going for the details
in this interaction because all of this is in their work
in the work piece of the project they can share with you this night.
But the idea is that the interpreter is done with an initial business
canvas and then go refining that canvas in several stages.
So first, the vision about big data, they need their value
to sponsor like man or whatever.
Then they go to an analytic canvas that up to reason about,
you know, that the quality in terms of regular multi
learning metrics and how those images connect to the outcomes.
And then in a later stage and maybe optionally
they go from their minimal viable product or from something that
can be something like a prototype, even if it was low quality,
who would think, you know, when, say, the road data,
it takes regulation, which is in many cases
what we are or a or a problem that they are comfortable with are.
And also, you know, that they mention they have to deal with
the cost structure, which is typically not viable
if you are using big data and there is difficult to
make something like reasonable assumptions of a data usage
and how how much do they need to pay on the cloud?
The most expensive services in non-Muslim or whatever.
And and that's also a problem for that.
And that's basically a label so that students
are rather unconventional now we take this,
we take the typical tool from business schools and then we are
that that the the data businesses
just to give you an idea of the kind of campuses they use,
is that something like complimentary campuses is specific to data.
That is, for example,
the reason about the volumes of data and the kind of statements.
That's another
one is that things that you can find in the web,
but it's we are simply putting them together with framework.
And this is no reasoning about, you know, the typical months
in learning and stuff
so that they go a little bit deeper into the to this concept.
And there is also something like data ethics campuses
of May that we some of the diversity if we can use this
or I've used it so of the data or something like that.
So the final outcome of all of this is that they have something
they can enhance method of coming to a
reasonable campus
without this business potential business owner.
How do the needs of becoming themselves the possibilities
so they know they have to know enough to make sensible business decisions
and maybe in the later stage they would hire a debate that's hey,
that's out of my sight.
Thank you.
Okay.
That's great.
Thank you very much,
Mike.
Well, can I ask you, you referred to material that's available online.
Would it be possible for you to post into the chat for everybody
the link to the program.
Sure I would.
In the workplace right now.
Okay, great.
Thank you very much for that.
Then we have the Web page.
Yes, that's great.
Thank you.
Okay. The next.
Oh, I see.
Rachel has appeared. Is this something you want to say, Rachel?
Are you just.
Now? I was just going to tell you what you are just now.
Oh, thank you.
Thank you for that.
So our next in fact, our final for this panel,
our final speaker is Inaki.
Inaki, can you you have 5 minutes.
Thank you.
Thank you very much. Hi, everybody.
So, as Kenny said, I was a fellow at the agency
at the university the kind of stuff that I am, I'm
here to talk about our experience, teach innovative skills at university
and what led us to, to
do a new master's degree
that starts on September this year.
On computational social science.
So basically, as you already said, and the increased integration
of technology into into our lives has created unprecedented
volumes of data on everyday human behavior.
And everybody, of course, is as thinking about social media.
But I would say that nowadays half of our lives
have been online like the one thing that we are doing right now.
Right.
And this leaves a trace of obviously
so big data as well as small and medium data is becoming
increasingly popular in all disciplines.
And traditionally we had like
two separate domains, like statistics.
Then there's computer science, right?
So statistics basically were invented to work with small data
and was heavily and
mathematically oriented.
So depends heavily on mathematical results
to robustly work with the small data.
Then computer science came in and kicked in, right,
with computers, internet,
and much more data on the table and new tools
that didn't require those robust and mathematical methods, right?
Yes. Half a computer.
And you can unfold their random processes with this computer
and over time.
Well, obviously in university we had this
clearly separated structure in
our statistics departments and computer science departments
that were completely different and separate.
We still have those structures in place,
but we started to converge.
Right, and converge towards what we call nowadays data science
and data science.
The first data sciences were either statisticians
that were self-taught in engineering and computing
or computer scientists, engineers that were set out as statisticians
at the university and
the federal we started to,
like, fill this gap a few years ago.
We have we already have, for instance, a degree
in data science that is quite new.
In fact, I think this year is producing our first graduates
and so in this degree, basically the idea is to build
that profiles in that intersection directly, right?
So we have profiles that are and
that has expertize in both data science,
in both sorry, computer science and statistics
And we also have
a couple of master's degrees from ours
for a number of years now on big data and statistics.
One is heavily
oriented towards the mathematical
part, and the other one is towards the computational fact.
Right.
But they both are still oriented toward
have STEM profiles.
When I said hard STEM, I mean mathematics
physics, engineering, because, you know, biology is also considered STEM.
But we can all agree that they don't have the same background in terms of
mathematics and computing.
And then
we have another branch, which is social science,
which a social scientist is an expert in studying human behavior
and finding patterns in data about population groups like ornaments
and follows political scientist and so and so forth.
But then at least in Spain,
social scientists have been traditionally very qualitative people.
Right. So there's another got there.
We started to fill the gap
on certain statistics and computer science.
And now we have still another island of social scientists that
don't have.
They have a little bit of expertize and statistics to work
with more data, but they don't have access
to that medium and big datasets that are available nowadays online.
So our idea was to fill all this gap
already in many, many countries around the world.
Like there are a number of master's degree already in Europe
and also as some of them in the United States, for instance,
but there was nothing in Spain about this.
So we decided to fill this gap
with a new master's degree in computational and social science
and a computational social scientist.
Of course, there are many of them already, but traditionally
they have been like physicists that learn social science
rather than social scientists that learn some computation and statistics.
Right.
So we are looking at the other side of this,
but so we just find a computational social scientist amputation
scientist and a social scientist that is somehow interested in digital
data and is comfortable with more computational techniques
to analyze medium to big datasets which means that data scientists
are still the ones that have their expert knowledge
in statistics and or computing but still,
when you have an interdisciplinary group, you still need
a common background
because as a social, scientists don't know what is possible.
Then even if they have the expert knowledge and they don't know how to
what possibilities are there and
and being disciplined doesn't work.
So that's the purpose of this this master's degree
and about the.
Yeah, so about the pandemic.
What we have a similar experience that William.
So basically, we and this took us by surprise and we have been
yeah.
So dealing with these in with it in the best way we good.
And there are mixed experiences there and we hope we can adjust
our teaching methodologies smarter than new online
methods.
Fantastic and excellent timekeeping.
I think we've got three models of timekeeping there.
Thank you very much to all three.