Computer Science and Artificial Intelligence
Find out more about studying Computer Science and Artificial Intelligence at Sussex.
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Data Science and AI webinars
Watch our recent Data Science and Artificial Intelligence webinar below, which includes a presentation from academics at the forefront of Data Science and Artificial Intelligence. There is also a lively panel discussion with faculty members, current and former students sharing their experiences of taking these courses and the opportunities they led to in the workplace.
You can also watch taster lectures focusing on our Masters courses in Data Science, Human and Social Data Science and Artificial Intelligence and Robotic Systems.
- Webinar transcript
Great, well people are starting to join us. Welcome everybody and thank you very much for joining this session. We'll just wait a couple of minutes for those people coming in through the joining link, so we'll start in around a minute or so. But thank you so much for joining us. While people are joining, in the chat, if you would like to just say hello and maybe where you're calling in from, just so we know kind of where in the world people are attending the session from, that would be really nice.
But it looks like we've got a really good number of people who've joined us already. Oh, fantastic - thank you to the first person coming in on the chat with where they're calling in from. So thank you very much. Brilliant. Okay, so Nigeria, India, Palestine, Germany, Turkey, Amsterdam... brilliant, a really broad range. Someone from Bangalore, India.
Oh, wow. We're getting lots in now. I can't keep up! From Uganda. Pakistan. China. Wonderful. Welcome. Okay, so my name's Leo. I work in the international office here at the University of Sussex, and so it's a real pleasure to welcome you to this Data Science and Artificial Intelligence event with the university. So Julie, if you just want to run through the next slides I'll do the quick introduction to the session and today's event, and then I'll hand over to you.
So today you've got first up your presenters. So, Dr. Julie Weeds will be talking to you for the first 45 minutes today. And then we've also got Dr. Ian Mackie and Professor Enrico Scalas who'll be delivering some of the taster lecture content in a little while. So for the first 45 minutes of today's session, you're just going to get a bit of an introduction to Data Science and Artificial Intelligence at the University of Sussex in general, and get a bit of a sense of what's on offer at the university.
Once we've gone through that, we're going to split up into three separate groups and there's going to be a link that shared in the chat feature of this event that will allow you to join one of three taster lectures that will focus on the three courses that we're talking about today. So Data Science MSc, Human and Social Data Science MSc and Artificial Intelligence and Adaptive Systems MSc.
So you'll be free to choose which of those you want to join. So we'll be doing that in about 45 minutes. After the taster sessions you'll then return to this main room and join me again and we will have a Q&A with some current students and alumni from these courses. And you will be able to ask them questions about their experience at the University of Sussex.
So for now, for the first portion of this event, I'm going to hand over to Julie. Do you feel free to use the chat as you have been and place questions in the Q&A, and I will try and answer those as we go. And we can also hold some of those questions for the Q&A at the end as well.
So thank you for joining us. And Julie, it's over to you.
Hello, everybody. Thank you Leo. Yes, my name's Julie Weeds, and I'm going to be talking to you today about Data Science and Artificial Intelligence at the University of Sussex. First of all, I wanted to know a little bit about you. If you haven't already introduced yourself in the chat.
If you have, that's fine. That's brilliant. But maybe for the people are joining us now, please do introduce yourself. Maybe say where you're from, maybe where you did your first degree and what your academic background is. Because what I imagine is that there's lots of diversity in the audience in terms of what degrees you've done previously, and you might be coming from computer science backgrounds, you may be coming from humanities backgrounds, from business backgrounds, from physics, life sciences.
So let's just see a few of the kind of different degree subjects that you have previously studied. So yes, you can see I've seen some computer science, quite a lot of computer science and IT. But I can also see psychology there was a political something in there as well. I saw early on political science, pharmaceutical sciences.
So we can see that, yes, we do have a wide range of different backgrounds that are joining us here today. And that is great because Data Science and AI is made up of - as we're going to talk about now - people with a great different range of backgrounds like yourselves. Okay. So first of all, what is Data Science? Now, this is a difficult question.
It's a science of data, but what does that really mean? A lot of people would say, well, it's the science of big data. How do we process and how do we work with really big data? What is big data, even? That will be something that we will talk about on the course and a little bit here today.
But a few other definitions here that people have given over the years in various literature. So from Joel Grus: 'the extraction of insights from messy data', 'a methodology by which actionable insights can be inferred from data' and then Mike Driscoll's one I like: 'a blend of red bull fueled hacking and expresso inspired statistics'. So there are some different definitions of what data science is. But what does it actually mean that, as a Data Scientist, you might actually do? Well, you're going to be trying to extract meaning from and interpret data, and this means that as part of that, you do lots of other smaller kind of subtasks in trying to do that.
So part of that may include collecting, cleaning and munging data, munging data being the process of getting it into a format that you can actually work with it. And actually this process of cleaning and munging data into a form that you will then be able to explore can take longer than we expect and actually be the kind of one of the harder things in terms of getting started with data science.
But once we've done that, then we want to explore that data, find out what it tells us. We want to find patterns, build models and algorithms. We're going to be designing experiments and then on the basis of those experiments, making decisions. And finally, certainly if you're doing this within the workplace, we're going to need to be able to communicate those decisions, those insights to your team members, to engineers and to leadership. So what kind of skills do you need or will you be building up if you're working in data science? And data science lies at the intersection of, say, of mathematics, computer science and the domain where the data comes from.
And this is one of the reasons why we see such a diverse range of people coming into data science, because we see people coming onto our courses, maybe from traditional mathematics backgrounds or computer science backgrounds, or backgrounds such as physics where there are a lot of math and computer science elements already. We also see people coming in who have skills in the relevant domain, and then data science sits in the middle of that here.
So if we were just mixing mathematics and computer science, we'd probably be doing something called machine learning, if we were just mixing computer science and the domain expertise we would be processing the data, by mixing maths and the domain would probably be doing statistical research. But when we mix all three of those things together, that's when we're really doing what I call Data Science.
And therefore, by the end of the course you will have built up your skills in all of these areas. Maybe the domain expertise might be something which you're bringing in with you or it maybe you're bringing in more computer science and math skills, and then you'll be exploring different potential domains in which you wish to work in, but also building up those skills as well.
So that's thinking about data science skills, and it is worth thinking about the fact that big data is absolutely everywhere. This is a few years old now [these figures come from 2019]. But looking at the amount data that's generated every minute in the internet and this was in 2019. So I'm sure now in 2022 it can only be more than this.
But in terms of what data is being generated... the amount of people that are sending text messages, the people that are subscribing, new subscriptions to music streaming services, emails being sent, swipes on Tinder, people tweeting, people scrolling on Instagram. All of that is data on the internet which, potentially as Data Scientists working in companies or in research or in policy, might be interesting to be able to extract some influences from.
I'm hoping everybody else can hear me. I can see that one person saying that they can't hear. I'm really hoping that is just one person that everybody else is able to hear. Brilliant. Okay.
Okay. So that was data. So you might be thinking, well, I didn't come to this because I'm interested in data science. I'm interested in artificial intelligence. So now I'm going to say a little bit about what I think of as artificial intelligence, because data science and artificial intelligence are very linked. They are very related disciplines.
They involve many of the same skills, but they are different things. And therefore, if you do an MSc in Data Science, you will be doing slightly different things and probably have a different focus to if you do the MSc in Artificial Intelligence. So what is artificial intelligence? I take this definition from Bob Copeland given in the Encyclopedia Britannica that it is 'the ability to reason, discover meaning, generalize, or learn some past experience'. Or another definition, 'the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings'. And I think we can see therefore the links with data science here; we're trying to discover meaning from past experience so that past experience may be represented in data. So that's where there is overlap between artificial intelligence and data science. But artificial intelligence probably includes many other things as well, which aren't strictly data science. When we're thinking about how digital computer or robots might perform tasks that are commonly associated with intelligent beings, that might be understanding text, it might be understanding visual inputs, it might be being able to navigate around the room. They are all things that we expect intelligent beings to be able to do.
So artificial intelligence should also be able to accomplish these tasks. So that's to say really that AI is more than machine learning. Machine learning sits within both the areas of data science and artificial intelligence, but both of them are more than machine learning. A lot of people come to our degrees having heard the buzz about machine learning, wanting to learn about machine learning.
But there is more in both of these fields than just machine learning. And even within the kinds of research fields which use machine learning, such as natural language processing, which is my research area, or computer vision, which use a lot of machine learning as a kind of current state of the art approache to solving problems in their areas.
There are other parts of these areas which aren't so typically associated with machine learning which would still fall within the general umbrella of artificial intelligence. How can we get a computer robot to be able to interact through language or to be able to make sense of what it can see? So what MScs do we offer here at Sussex?
We actually offer five MScs now. So we have the MSc Artificial Intelligence and Adaptive Systems, but we also have a variation of this which we've introduced this year, which is an MSc Artificial Intelligence and Adaptive Systems with an Industrial Placement Year. I've put the links here on the slides as well, but they're all to our Sussex study pages where I'm sure you've already probably been looking, in order to get to the webinar today. And we've got the MSc Data Science. We also have a version of that as of this year, which is a Data Science with an Industrial Placement Year which makes that a two year course as well. And then we have the MSc in Human and Social Data Science. All of these degrees can be studied full time or part time. I can say a little bit more about each one of those in turn turn in a moment.
So I think it's probably best just to press on to that. So let's just talk about each one of those degrees in turn. So we've got the MSc in Artificial Intelligence and Adaptive Systems, which is probably our oldest degree in this area.
It hasn't been called this for that many years, but we have been teaching a variation of this course since the 1990s. We had two degrees, one was called Intelligent Systems and another was an MSc in Evolutionary and Adaptive Systems. Over the years these two degrees have kind of come together and become the MSc in Artificial Intelligence and Adaptive Systems that we offer today.
There are various different strands or areas of study that go on within this degree. Some of the things that you might look at within this MSc are: artificial intelligence and cognitive modeling, robotics and autonomous systems, and the one that which probably most closely overlaps with the data science degree because as part of artificial intelligence adaptive systems, you might choose to focus on data science machine learning and natural language processing.
And then there's also computational biology and consciousness science. For students who come on this degree, you learn about the foundations of all of [these areas of study], but you will then tend to focus on one of these strands. And one of the key strands is probably your main interest in coming on the degree. Most students that come on to this degree have a background in a scientific or technical subject. We then have the MSc in Data Science, which was first delivered in 2016, so that means we must be in our fifth year now.
And traditionally we've had students coming on to this course with backgrounds in mathematics, physics, computer science, and life sciences, and some other students have come on to this degree with other backgrounds as well. But typically most of our students have come from backgrounds in one of these areas. And again, it may be because they want to think about, say, physics as the domain of their study where there's lots of data in particle physics or in astrophysics and be looking at how to work with big data in that area.
Or they may be computer science students that are interested in data science more generally in business, and wanting to improve their skills in this area to take out into industry. Or they may be interested in working in research in machine learning or natural language processing. And again, this would make a good background degree for that. So that's how I'm seeing data science and then we now are in our second year of the MSc in Human and Social Data Science. First delivered in 2020 this is aimed at students with backgrounds in business, humanities and social sciences, including psychology and we have two main strands within this degree program with students focusing on human social data science in the context of digital media, which is co-delivered with our School of Media Arts and Humanities. And then we to have a strand in innovation and policy which is co-delivered with the Business School and the Science and Policy Research Unit. Two of these degrees, the MSc in Artificial Intelligence and Adaptive Systems, and Data Science, have an option for an industrial placement year.
And the idea here is that year one is the same as the one year degree, you have your two modules and your dissertation, and then in year two you do an industrial placement assessed by presentation and a report. There are some variations on that where we've seen some students do their placement and then come back to do their dissertation.
But essentially you do the one year program plus a year in industry. I will say that it is your responsibility to secure your placements with our help. We will help you to find and secure placements, but we cannot guarantee placements to any students. It's the responsibility of the students to to secure their placement.
But it is also very, very easy to switch between the different degree programmes. So if you sign up for the degree with an industrial placement year and then decide that you don't want to do a placement, or you haven't secured a placement and therefore you don't want to do a placement because you can't secure one, then you can switch on to the one year degree.
And similarly, quite often students will switch the other way around. They'll sign up with the one year degree and then they will find a placement. So they'll want to do a placement as part of their degree and then they switch on to the degree program with an industrial placement year. So in that sense, it doesn't matter which of these degrees you sign up on, sign up on one of the degrees and then you can switch to the other.
I've seen some questions coming up, but I think that they're mainly being answered in the chat by my colleagues. Just to say that yes, we do expect industry to pay you when you do and your placement year. I have, I think, got some more information about that later. But definitely you're expected to get paid on your placement by the company that you're working with.
Okay. So what does the MSc look like? Here's the full time version of this, and I've split this into the three different degrees here, and then we've got the three periods of study. So we have our autumn semester, our spring semester, and then we have the summer period, which is when you do your dissertation projects.
So all of our students do four modules in the first semester. If you're on Artificial Intelligence and Adaptive Systems, you do one core module that everybody on this course does and then three optional modules that you will choose to take from a list. On Data Science and Human and Social Data Science there are three core modules and one strand-specific option.
In the spring there are two core modules and two optional modules for Artificial Intelligence, so that's another four modules that you will be studying in the spring on this degree. And then on Data Science and Human and Social Data Science, you have three core modules and two strand-specific options. So you actually study five modules, but that's partly because one of those modules is your dissertation project proposal.
So actually what it means is that you get going on your project a little bit earlier on this degree by doing some dissertation work in one of these core modules. So that's really just to get you going early on your dissertation project. And then in the summer you complete your dissertation project as a Data Science or Human and Social Data Science student. Or as an Artificial Intelligence and Adaptive System student, you would go through the whole dissertation process in the summer. For both degrees, it's the same amount of credits that you will be obtaining. 60 credits for your dissertation project, 60, 60, 60 on Artificial Intelligence that's 180. And then on Data Science we have 45 credits for the dissertation project because 15 are in one of these core modules here, but it's still 180 credits in total. I'm just seeing some more questions come up; I can come back to those if others don't reply. [With regards to] the question about how hard is it to complete a dissertation in just three months, it's about identifying the right problem, and actually potentially in the Data Science degree, getting started on it early in the spring so that you're actually working on it, setting it up in the spring time so that then it's a case of completing that project over the summer. If you know what you're doing and you worked out a good proposal, I would say it's perfectly straightforward to do your dissertation in three months, but it can't include the process of working out what your project is as well. You do need to know that in advance.
Okay. So that's the full-time version of the MSc year. Just to say, I think I mentioned this earlier, all the MScs can be studied part-time subject to visa restrictions. So I think this might not apply to international students or some international students. But just to say that in general, if you're doing the kind of MSc without an industrial placement year, this means that the one year programme becomes a two year programme and essentially you accumulate half of the credits each year, you do half of the modules each year, then you carry out your dissertation over two consecutive summers.
If you are doing the industrial placement year, we advertise it's a three year program where you carry out your two modules over two years and then industrial placement year is one year full-time. However, subject to the company agreeing to this, there would be no reason why you couldn't do the placements over two years as well if the company that you're working with is agreeable to a two year part-time placement. And many companies are much more flexible with regard to part-time working now than they were a few years ago, so it's good progress in that direction. Okay. So that was part-time flexibility. I want to say a very, very small amount about the different modules that you might do on these different degrees just to give you a flavour of all of the degrees before you go off and learn more about a particular degree in your taster session. So here I've got the core modules for each of the degrees, and what you will see is that there is some overlap between all three of these degrees. So students from all three of these degrees will study, as one of their core modules in the first term, Mathematics and Computational Methods for Complex Systems.
Some data science students do replace this with a module called Data Analysis Techniques. This tends to be students from a highly mathematical background, or potentially from a physics background. But other students on Data Science, Human and Social Data Science students, and Artificial Intelligence and Adaptive Systems students all study Mathematics and Computational Methods for Complex Systems.
And this is one of our foundational modules on maths and programing, which will help you learn the foundations that you need in order to be able to study machine learning in the second term, which we'll see is also core to all three of the degrees. Then our Data Science and Human and Social Data Science students all study data science research methods in the first term, and then students look at Algorithmic Data Science which is the kind of computer science foundations in algorithms and distributed systems which apply in many kind of aspects of data science. When we're working with big data, we need to be able to have things running in a computationally efficient way across a number of different computers. And then for Human and Social Data Science students who typically come in from a non-computer science background, we have a more foundational module for on systems for information management, which again introduces them to certain aspects of computer science, which they will need to learn and understand in order to be able to engage with the rest of Human and Social Data Science. All of our students do Machine Learning in the spring term, and then Artificial Intelligence and Adaptive Systems students have a core module on adaptive systems, and our Data Science students have wider topics in data science as a core module and their dissertation research proposal as another core module in the spring term. And then all students go on to do a project in the summer. Just to mention a little bit about some of the option modules that you might take,
and again, I think you will learn more about these in the individual tasters. But just to list here some of the options that are currently on offer, on Artificial Intelligence and Adaptive Systems you can see that some of those core modules from Data Science are available on this degree. So you can optionally take those topics. We also have options in Advanced Software Engineering, Applied Natural Language Processing, Artificial Life, Intelligence in Animals and Machines, and programing in Python.
So you can see some of those strands, those focus areas I mentioned earlier, such as the more 'data science' strand of Artificial Intelligence versus potentially the more 'computational biology' aspects of Artificial Intelligence such as we might find in Artificial Life, and Intelligence in Animals and Machines. All being offered depending on what you want to study.
Then in the second term you've got more options, which include more Natural Language Processing, Image Processing, Intelligent Systems Techniques, and Neuroscience of Consciousness. But I'll let Ian talk more about that in a second. On Data Science, we typically expect students to identify with one of these strands or pathways through the kind of different options that are on offer.
Although we're trying to simplify that for this year in terms of the option making process, but this still stands in terms of the modules that are on offer and the backgrounds that you might have come from and therefore what you might choose to study as your options. So students with a mathematics background typically might take Programing in C++ in the Autumn term and then go on to look at Statistical Inference, or Monte Carlo Simulations, or Coding Theory in the second term. Our computer science students typically focus on Natural Language Processing, but also might take Image Processing options, or Web Apps and Services, or Web Technologies as their options in the second term.
Then we have options for students with a physics background on Advanced Particle Physics and Particle Physics Detector Technology. And we also have options for students with a life sciences background on Genomics and Bioinformatics. And then there's the option to take any one of the other options from the other strands that're also of interest to you as a student coming from a life sciences background.
So that's Data Science. And then the optional modules on Human and Social Data Science tend to link to the two schools with which we co-deliver this course. So we have the Digital Media strand where students typically take options from Media, Arts and Humanities such as Digital Journalism or Media Law and Ethics. There's also the option to take Applied Natural Language Processing, which is an Informatics module, due to the large amounts of text that students in this area may want to process.
And then there's options on New Developments in Digital Media, and Race Culture in the Media in the springtime. I think we have a few other options that are being made available for next year or have come in this year. I know there's one on Techno-Feminism which has been particularly popular with our students this year, and that will be on offer again next year as well.
I haven't added it to the slide there. And then on Innovation and Policy, we have Policy Making and Policy Analysis in the first term, and then Artificial Intelligence and Policies for Technological Revolutions, and then Industrial and Innovation Policy in the Spring. Students picking this strand would typically take these three modules as their option block to make a very coherent strand of options.
So before we split, I think I've got a few more slides on some of the other things that are offered to all of our students. So first of all, it's worth mentioning that we do try to partner with industry and have a kind of applied industrial focus to our courses. And we have talks and mixers with industry. Hackathons, which I'm going to mention a little bit more about later as well.
We offer mentoring to our students, both group mentoring and one-to-one mentoring with industry partners. There's the possibility of getting involved in internships of up to three months. We have had students who take internships over the summer and then come back to do their dissertation projects in the Autumn, so that's a possibility. Then as of this year we've got our first students going off doing industrial placement years, and expecting many more students from the cohort coming in to want to take up this exciting possibility of doing a year in industry. A little bit about our partners, too many to list but I've got a few here that I know we've had students go to in the past to do internships or projects with them.
So we have Brandwatch, a local social media brand analysis company. They look at social media to see what people are saying about different brands and work with companies providing that kind of analysis. We typically have students going there for internships. Some other companies here as well, smaller local businesses as well as larger, more national businesses we've had students go off to. We are going to meet Minh later at the student panel, who is working at Unilever this year.
So a large range, diverse range of different kind of companies that we can potentially introduce you to. As part of that, we do have, at Sussex, a Centre for Data Intensive Science, the Data Intensive Science Centre at the University of Sussex, that's DISCUS, and this is a centre which aims to bring together students and researchers from across the University who have an interest in data science. They put on events, some of which are specifically for MSc students, and they do also offer support with finding placements and career opportunities and also offers support with identifying cross-disciplinary research projects, so say you want to do a Psychology project with a Data Science focus, [DISCUS] can help link you up with the people in Psychology that might have the data or the domain expertise that would be useful to bring into your project.
They also run the mentoring sessions, and what I mentioned earlier about hackathons with industry, again, this would be something which would be organised by DISCUS but also with the industry focus, bringing in students and other researchers from across the University. I mentioned mentoring, so that's been running for the past two years. Each year we've tended to match up approximately 30 of our students. This year, that's about 30%.
Pretty much everybody that wanted a one-to-one mentor got one, and essentially this is matching up a student with somebody working in industry. They have a one-to-one session online for about an hour each month or every six weeks. Most of our mentors are alumni from the course or variations of the course, or Ph.D. students who've gone on to work in the industry.
Some of them aren't. So we also have links with other companies not through alumni, and we've got, certainly over the last couple of years, a few of the companies where our mentors have been working and then they've been sort of talking to students, and the focus here generally for many students is about how to build up a kind of network which will be useful beyond graduation.
Most of our students are thinking about how to get jobs in the industry when they're in their mentoring sessions. The mentoring is not only for industrial placement students, it's potentially for all students. We may have to prioritise at times, and we do have priority for our scholarship students for mentoring, but in general we're able to provide mentors for other students who want to have mentors.
That would be students on all of the courses, not just international students, not just home students, not just students on an industrial placement year. So you do not have to be on an industrial placement year to have a mentor. Okay, so just to say, I'll just mention scholarships, that we are also partnered with the Office of Students who see the need for more people with Data Science skills as a key priority for our Government's industrial policy. So they are offering support to universities to create more links with industry and also to increase diversity within the field, which is why we are able to offer certain scholarships to students in minority groups. And this year we've got 25 on offer which are worth £10,000 each, they're funded by the UK Government.
You have to be a student on an AI or Data Science postgraduate conversion course, although by conversion, that basically means you haven't done AI or Data Science before. You may have done computer science and maths before, that's fine, tt still counts as a conversion. And you do need to be from a minority group, and the three prioritised minority groups are female students, black students, and students who are registered as disabled.
There's an application form which you can find on our website. Again, in that application form, if you have any of these other characteristics it's worth noting those as well, because that would also be something that we would be able to consider in terms of giving you priority for one of these scholarships. But you do have to be one or more of these underrepresented groups to qualify for the the scholarships.
There are other scholarships, so check out the course web page to find out what you might be eligible for. And this can vary according to whether you're a home student or an international student. Okay. So what are you going to do after your MSc? Well, there's lots of different things you might go on to.
I have got a few more slides here which kind of go through some of the things some of our students have gone on to previously. But in the interest of time, I'm not going to talk about that because I know we do have some students coming in for the question and answer session in about 45 minutes.
I'm going to skip through those slides. I've said questions there, but I think you've been asking questions as you go along. So what I'm going to say is I think we should head back to Leo and he can manage the process of going off into breakout rooms. And I'm going to stop sharing.
Julie, thanks for thanks for a great first presentation. Right. So thanks for all your questions so far. We've done our best to answer some of them in the chat and we'll have more time for Q&A towards the end of the event in about 45 minutes. Now is the time though, to head over to the individual taster lectures.
So we're going to be doing one on Data Science, one on Human and Social Data Science, and one on Artificial Intelligence. And you can choose, depending on which course you've applied for already or which course you're interested in applying for, which of those sessions you're going to join. Now, we're going to do those in separate Zoom webinars. So I'm going to share a link with you in a second to that.
So Julie, if you want to head over to your your session, the Human Social Data Science one, and we have two other faculty members in each of the other two as well. So first of all, basically in the chat, I'm going to share these links with you now. So you may want to actually stop writing in the chat for the time being and asking questions because everyone will need to be able to access those links.
So this is coming your way now and you will see that each of the sessions has its own links. Make sure you click the right one and it will then give you the option to leave this session, and you should say yes, you'll rejoin later, but essentially you'll be back in 45 minutes. But click on the link in the chat for the session that you want to attend and then you will be able to join those sessions.
So go ahead and do that. I'll bring up a slide on my screen as well. Just to explain, for those of you who can't see the links in the chat, I'll also share a link with you so if you can, if you can't click the link in the chat, then you can type the link on the slide into your browser and it'll do it that way as well.
I can see lots of you now finding your way out of this room. So I think that's working. So do go ahead and join the sessions and we'll be back in this room in about 45 minutes for the Q&A with the students. So I will see you then.
Great, okay. Well, thank you. I think we'll probably have more and more people joining over the next couple of minutes. And we've also got an additional panelist who's going to be joining from one of the sessions as well. But for now, we've got a panel of five soon to be six. Oh, thanks for the comment in the chat. It's great to hear that you've enjoyed the sessions. I've just got a message from one of our panelists saying you can't turn on cameras, so I'm going to correct that.
So great. Okay. So there's Julie with us then there's one, two, three, four, five of you. And I think we'll be joined by Michael as well in a second. So cameras are on. Can you unmute? Yes. Good. All right. I'm glad that there wasn't an extra layer of challenge to get you to
all be unmuted as well. So brilliant. Well, first of all, welcome to our panelists and welcome back to our attendees. I really hope that those of you who made it into the three sessions really enjoyed them. We had a session on Artificial Intelligence, on Data Science, and a separate one for Human and Social Data Science.
And I'm excited to tell you that we are joined now by Alexis, a student of AI and Adaptive Systems, Ahmed, PhD in Informatics, Minh on the MSc Data Science, Teresa, an AI and Adaptive Systems student, and Emily, a graduate of I think Human and Social Data Science. Or was it Data Science? That's right, Human and Social Data Science.
I thought I remembered from last year. Have I got that right, everybody? Or you want to correct me? Please forgive me if I've got that all wrong. So welcome to you all. So what we've got now is a really great opportunity to just have a bit of a conversation with with people who studied these programmes or are studying these programmes.
So what I'll do first is, I've given a very brief introduction of each of them, but if you could just take a minute, each of you, to just explain the course you did and sort of how you found what you're doing now, kind of what you're planning for the future. Just anything you'd like to share.
That would be great. So I'm going to work my way around the screen. So, Alexis, do you mind going first?
So like you said, I'm doing the Artificial Intelligence MSc, and I actually come from a very different background. I studied languages, so linguistics and education, and I was a teacher for multiple years, and I was very interested in the technological part of it, of languages and education.
And that's why I chose this degree. I decided to go in the direction of NLP with Julie, of course, which has been great. And I've been doing also a module on education and AI in education, which has been great. Fantastic.
Thanks, Alexis. And then Ahmed, you're the next one in my field of vision.
Hi, I'm a PhD student from Informatics supervised by Julie. I mainly work with Natural Language Processing, and I come from a computer science background. I work with Arabic at the moment, but I am tending to work in different languages. Yeah, that's mainly what I do. Data science stuff and natural language processing.
Fantastic, data science stuff is what we're here for Ahmed, thank you.
And then, Emily, you're up next to my screen.
Thanks. Hi, I'm Emily. I studied the Human and Social Data Science Master's last year and graduated. And in September, I started working as a cloud developer within IBM, and I work on a lot of different projects, from using NLP and blockchain technologies across the supply chains of different companies, and using all sorts of different artificial intelligence for businesses and implementing it.
And thanks, Emily. And then Teresa.
Hi, I'm Teresa. I'm on the AI course. I'm a part-time student in my second year of doing my Master's degree. I did computer science as my undergrad several years ago, and I've been working in industry since. I took this course because I was trying to decide whether I want to do a Ph.D. or not.
I don't think I do, but I have thoroughly enjoyed the course. And at the moment I'm trying to decide how to segway my sort of new skills into my career. I'm currently a systems analyst, and whether I do more sort of industry / data / AI stuff.
Great. And Minh, there you are.
Hi, guys. So my name is Minh, I'm studying the Data Science MSc, and I came from a kind of mixed background. Yeah. And I'm currently doing a placement at Unilever.
Great. And then, Michael, I found you in the attendee list. I hope you're the right, Michael. And welcome. Thank you. So. So you may have missed it because you joined us a little bit after, but if you just want to quickly introduce yourself, your course and a bit on your background, that be great.
Okay. Basically, my name is Michael, I have a background in economics, I'm currently studying Human and Social Data Science at Sussex. After graduation, I fell in love with data science and I started my journey did some roles, worked in the community of data scientists back in Africa, Nigeria precisely, and then signed up for the Masters in Data Science at Sussex and the experience has been really terrific.
Brilliant. Thanks so much. Well, thank you to you all for joining. That's great. Now, I'm going to I'm going to confess that my background is not at all in this sort of data science or computer science area either. So I'm going to rely on Julie a little bit to help me work out what are the best questions to be asking you all.
But really, the most important thing today is the opportunity for those attending to join. We've got ninety attendees in the meeting today, so lots and lots of people interested in studying these subject areas at Sussex. I'm just having a look at the chat, if you want to ask questions as attendees it would be great if you could use the Q&A feature as it's a bit clearer, but we'll try and use the chat feature as well.
So there was a question from earlier; sorry, remind me, have any of you done a placement as part of the course? So, Julie, you'll tell me if anyone has. Minh, yeah, that's right. So there was a question about, does the industry placement have to be done in local industries or can it be done in other places? Do you want to talk a little bit about your placement and of what you know about the placement option then? Julie, you can obviously come in on this as well.
Yeah, sure. So I guess what I'm doing is in Unilever in the R&D department, so I mostly work with ice cream. So yeah, ice cream R&D. So basically they want to apply some data science and artificial intelligence there. So like for example, optimise the formulations or the ultimate data capture process to facilitate their, let's say, formulation development.
Yeah, Fantastic. And so and so I'm assuming you didn't do that in the local area of Brighton. Where are you based?
I'm currently in Bedford. So I recently moved here.
I can jump in on that as well. There's no requirement for placement to be local. Probably more of our contacts with companies are local ones, they are the companies that we have the most contacts in, but we do have contacts with with national companies.
You can set up your own placements with with any company and actually you could go international as well if you wanted to. You'd need to look at visas and things like that, but for the purposes of the degree, there is no reason why you can't be anywhere to do your placement.
Yeah, great. And obviously that leaves the options far wider as well. So there are lots of questions here and I'll try and pick the best ones, but Julie and Enrico, thanks for joining as well, do let me know any questions you would like to direct to anyone because you know everyone a bit better than I do.
There's a question here, and maybe this would be good for Emily to answer, I don't know what support you had in terms of looking for jobs while you were a student. And obviously you've gone on to work at IBM. But there's a question about, does the university kind of help students in finding relevant jobs after graduation, whether they're on the placement year version or not?
So is there anything maybe you could share on that?
Yeah, definitely. So I came into the Master's with an idea of some of the things that I was most interested in, and I studied Psychology as an undergraduate. So for me, the technical side of things is really, really new and quite scary. And I think there were a few things that really helped me in terms of building my confidence.
So I had an amazing mentor for my year of doing the Master's, who helped me to build confidence in being able to articulate some of the more technical side of things so even though I didn't have necessarily have a large amount of knowledge, I had enough to be able to talk about it in a way that that was engaging with employers.
Also, there's a lot of support from the department side of things and DISCUS as well. There's always lots of opportunities with DISCUS for meeting different different people from different organisations. And then also sort of broader as well there's the career hub, career something, I can't remember, but they regularly have a lot of relevant jobs that are complimentary to the Master's. So I think that there was a good amount of support from that perspective in getting me ready in different aspects.
Yeah. Brilliant. Yeah. I mean, I think there's lot support there and there's also a lot of knowledge from within the school that can kind of give you some support as well. There's a question that someone's asked which is kind of open, I think, to any of you to answer, but maybe someone we haven't heard from yet, which is simply kind of how much time you spent on campus I guess, learning in the class, versus how much you're spending time working on projects and doing kind of you know, sort of independent study. So we may have covered this in the presentations, but a student perspective would be really great. So I'm throwing this out there to you. If anyone wants to unmute or put a hand up to cover that, that'll be fantastic.
Yeah, Teresa, please.
Can you hear me now? Yes. Excellent. Sorry, my headphones turns off if I don't talk enough! But yeah. So you get scheduled with a certain number of classes a week, usually on campus. At the start of term there's not usually a huge amount of extra stuff other than the classes you're supposed to be doing, reviewing your notes, that kind of stuff. Towards the end of term it ramps up. So that's when you have assignments and projects and things like that. And at that point, I'm usually doing at least as much project work as I am classes, if not more.
I'm part time, so I probably have about 10 hours of classes a week and then I end up doing maybe an extra five in the first week of term, and an extra 30 in the last week of term as I desperately try and get my projects done before the deadline.
Thanks for that answer, I don't know if anyone else, I mean, that sounds about right. I don't know if some of the faculty members would like to come in on that?
Yes I'll just comment on that one. I think that sounds about right. I think would say it was about half and half. And therefore what Teresa's saying is probably about right in the sense that, you know, it might start less than half on direct to self-study and become more as as things become closer to deadlines and things like that.
So, yeah, I would say that as a full-time student, you might get 20 hours, 16 to 20 hours scheduled throughout the week, and you'd be doing that again in terms of self-directed study.
Brilliant. Another question that's come through. Again, I'll throw this out to anyone.
I mean, I guess it's about Data Science so maybe one of our Data Science attendees, but someone's asking about if one is transitioning from veterinary medicine backgrounds to data science, what advice would you give that individual to be able to secure a Master's? And I guess also by extension, so to be ready to do an MSc in Data Science?
So has anyone got any thoughts on that? I mean, some of you have come from different backgrounds. So again, would anyone like to offer their opinions on that? Veterinary medicine is obviously quite different. But for anyone, anyone got any thoughts or opinions on that?
Yeah, sure. Teresa, go ahead.
Yes, so I did computer science, but about half of the people in my course hadn't done anything particularly technical before they joined. And it is quite a ramp-up. There is a lot of support. There's a lot of extra classes and stuff. But I would say if you are thinking of doing it and you haven't done programing before, it's been a while since you've done some maths, then I would recommend maybe if you've got some time in the summer beforehand trying to just pick up the basics, because especially if you're doing the full time course, you'll be doing projects at the end of the first term.
And if you're trying to learn how to program at the same time, it limits your choices on the more interesting projects you can do. I know, speaking to some of my colleagues, after the second term they're like 'I would quite like to do my first term project again now that I know so much more'. So I would recommend trying to do some it beforehand if you haven't done them.
But as I said, there's loads of extra classes. They put on extra workshops and things to help people get up to speed.
Brilliant. Thanks, Teresa. I see we've got Alexis and Michael with hands up as well, so please do add to that. That would be great.
I just want to add to what Teresa said. Basically, it's more like you look at your interest early. Most of the people on the course are really lucky, they're coming in very early, so there's quite a number of communities you could get involved in early to kind of get yourself used to the concept and do the science.
Basically, Python seems to be the one major language. You want to familiarise yourself with that. It gives a good prep to come in and also not struggle during the course.
I just want to agree with both of them. And since I came from a completely non-mathematical background I can say that the maths is the most important thing to brush up.
So that's where I struggle the most. I also did some linear algebra and calculus during the summer, so that's what I would recommend. Coursera is amazing with that, they even offer to attend courses for free. So that's my recommendation. And of course, a little bit of Python ahead of the course would be also a good idea.
Ahmed, I see your hand's gone up as well.
Yeah. The programing is important, has been covered. Maths is important, has been covered. But also one thing is, try to practice to write more. Because most of the assignments that you take would be mainly expressing and presenting your thoughts and your understanding of the module in writing.
So try to practice writing a bit more. And generally don't get dragged in learning programming and maths just for the sake of it - try to contextualise it, so what the module needs. Then go and say 'to be able to do this module, what do I need from maths and Python?'
Yeah, that's generally really good advice.
Okay, brilliant. Well, thanks for a really complete answer. I think that's really helpful. We've got a few other questions here. Some of them are a little bit similar to what we've asked before. Actually I'd be really keen, because Julie and Enrico obviously know you all quite well and obviously also know the courses extremely well, if there're any questions that you would ask our panel that you really want to bring out of their experience. I'm not going to do that right the second, but we've got about 10 minutes left. So if there are any questions you think I should be asking as a competent interviewer, or that you would like to ask, please do that. There's a question here about the kind of the one-to-one support the students get from faculty.
I know that every student's assigned a tutor to kind of help them, but if anyone has got any kind of perspectives they can add on the on the support they've had from faculty, that would be great. Again, if you want to stick your hands up. Alexis, yeah, brilliant.
Thanks. Yeah. Ian, who is here now, was kind enough to organise an Informatics Help Desk, so I've had somebody to help me with maths and programing on a weekly basis, like twice a week, which has been amazing. Joe has been helping me so much, and he actually graduated from this course last year. So he had firsthand experience and knowledge, and that was very helpful.
Thanks, Alexis. And then we've got Emily as well.
So when I was doing the Master's, I was working part-time as well. And so I found it really, really difficult to actually make any of the sessions that were, you know, additional help sessions, and things like that. But I found that all of the faculty that I spoke to were just so empathetic and compassionate and able to support, from, I had quite a few conversations with Julie which were great, and with the supervisors as well. I think they're not scary people. They all want to help and help you succeed as well. So that was really good for me in terms of additional things as well. Brilliant. And then a couple more hands.
Michael's hand's up if you want to come in.
Oh, yeah. Thank you, Leo. Basically, I think the readiness to help, which has been said earlier, and then the fact that there's quite a number of, so we have things like the Maths Helpdesk that regularly come up. We have also the faculty members willing to help if you want to have a one-on-one session.
And I think amongst your colleagues you can also meet in a group study, which also goes a long way to help in collaborative learning and all of that. So I think that's really helpful too.
Thanks Michael. And Teresa, if you want to come in as well.
So as well as all the stuff the others said, there's also quite an active discord community. I mean, especially last year during the pandemic when we weren't there, it was very active. But even this year, if you get stuck on something, or you just want to share something interesting.
We've also got peer support as well. We meet up with our colleagues on campus and help each other. We work together in labs and that kind of stuff. So there's lots of support.
Fabulous. We've got really just a few minutes left, so probably only time for maybe one or two more questions. Julie and Enrico, is there anything that I've missed that that you think I should be asking, or any of the questions that you've seen in the Q&A or chat that you think? There was there was a series of questions from people from different backgrounds.
So we have the standard Computer Science, Mathematics, Physics and Life Sciences, I think we already covered this. TBt maybe for these potential applicants, they are also interested to see what the difficulties can be, etc. I don't know if the panelist would like to comment a little bit more about their personal experience and coming maybe not from a computer science background, what were the main challenges, etc.
I can keep elaborating on that.
I think, like I said before, the maths was very challenging for me, so that was the most difficult part I would say. I had done a little bit of Python before, so that was really helpful. And like I said, the courses on Coursera on linear algebra and calculus. So just brush up your maths. You have to put a lot of work into it and that is definitely the case, but you can do it if you put the effort and you put the work, you can definitely manage. There is enough support from everybody.
Brilliant. Thanks, Alexis. Emily, you've got your hand up as well.
Yeah. Just to sort of elaborate on that. You know, it's a change in mindset, particularly from a non-technical background where you might have done something that's very essay-based or something like that, actually sort of having a more adaptive approach to the assessments as well.
And really you know, understanding them first before tackling them I think is really good in sort of getting the fundamentals. But with any sort of technical role, and Master's as well, particularly with data science, you need to have such a varied skill set. It's not just the technical bits. It's the 'so what?' 'So how does this actually benefit these people or society?' or 'where is it relevant?' and then I think that, actually, that's something that can be really motivating.
And I think that that's something that maybe actually gives you a bit of an advantage as well, is having that different of sort of perspective. So I guess it's the different building blocks. So while some things might be more challenging, actually there're other beneficial things that are more natural. So like the essays and things and the written side of things, that was great.
And that was definitely much more in my comfort zone. But then, you know, you balance the two and collaborate with people who will be able to help you as well. And you can help them.
Yeah, big respect, especially to those who are coming without a background in this and sort of, you know, taking the plunge into an area that's quite unfamiliar.
So well done. It sounds like you found it rewarding, so that's great. Michael, final words from you. I think this will have to wrap up in just second. Please do add your comments to that as well. That would be brilliant.
Yeah, I think basically because my background is economics, I have kind of an idea of a problem, I want to solve. And then that's like a motivation along the line. And one of the ways to get motivated is also to join things like groups. You have competitions there, so they're quite helpful to keep the spirit going, the motivation going.
Fabulous. Thank you. Right. Well, we're nearly at the end of today's session. We've been going for about 2 hours, so quite a long run with all of you.
But I can say that we still got around 100 attendees in attendance. So thank you so much for being with us. And I hope that today has given you a really strong sense of what Sussex has to offer and, you know, really kind of get you excited about joining the university. I think it says a lot that so many of our current and former students were happy to turn up today and give that perspective, and it's extremely kind of you all to do that.
So thank you very much for being there and obviously also to Enrico and Julie taking up part of their precious day to kind of give you a better sense of the courses on offer. I know that there have been lots of questions that are kind of more practical about accommodation, about scholarships and things like that. Do get in touch with the international office for those questions because we are all here to help you.
And if you're a UK student, then get in touch with the UK recruitment team as well. There's always somebody there to help. So we've had to avoid those non-course related questions today just because of the number of questions have been coming in. But there will definitely be people who are there to help you.
So that's that's all for me. Julie, I don't know if you've got any final words from you or if you if you want to wrap it up there just to say thanks and goodbye.
I would just like to say thank you very much. Thank you to all of our students for sharing their experiences, it's been great to see you all and to have your perspective today.
And thank you to everybody for coming. And I hope it's helped you decide what you want to do next year and what the right course for you is.
Brilliant. All right. Well, I'll let everybody go then. Thank you so much for joining us. We have recorded these sessions. They will be finding their way up on our website in due course.
So if you want to check out some of the other recorded sessions you couldn't attend today, you'll be able to do that. A big thanks again to all of our panelists who were kind enough to join us today. I'm hugely grateful for that and hope you all have a lovely rest of your day, wherever you are in the world. Bye everyone.
- Webinar transcript
Good afternoon, everyone. Or good evening, maybe, depending on where you are. Welcome to the session on Data Science. I'm joined by Professor Enrico Scalas who's the convener of the Data Science programme, and he will be presenting for 40-45 minutes with a taster lecture. So I'm going to hand over to Enrico.
Thank you very much. So I hope you can see the screen and hear me.
I would like to apologise in advance for the following reason: where I am standing now, sometimes I have small breaks in internet connection and my voice is typically fading. So if this is happening, maybe Rob will tell me and I can repeat the part that faded. So welcome to this taster session on the MSc Data Science. I think I can probably start straight away, so the topic I would like to address is the future of data science.
Of course, this is a taster session, and so I am putting together several topics that typically are covered in modules. This is not essential, it's sort of an overview seminar. It is not the typical lecture you would receive on one side, but on the other side, it gives you an idea of what is going on during lectures.
So here you have my contacts in case you would like to write me separately. I am the convener of MSc Data Science, as Rob mentioned, and I'm also working in the Department of Mathematics as a Probabilistic. So, this is some self-promotion of the University of Sussex to start with. And indeed, our is one of the most beautiful campuses in the United Kingdom, I would say. It is in the heart of the South Downs National Park, so you are surrounded by a natural landscape of typical English countryside. It's not what it used to be, maybe 2000 or 3000 years ago, but still it is very typical in characteristic of England. Essentially, when you are in Brighton and you go north towards London, there is a small range of hills which are called the South Downs and Falmer, where the University is located, is essentially this Natural Park.
Just to mention also that connection by train from Brighton Station is quick, normally it takes sort of 9 minutes from Brighton Station to Farmers Station and that's why I am saying here we are only minutes away from the lively, diverse and student-friendly seaside city of Brighton, and also we are quite close to London. We do not have a direct connection to London, but you can find trains to London in both in Brighton and in Lewes.
These are the nearest stations with direct connections with and with London, and Lewes is even closer than Brighton. I think it's 5 minutes from Falmer Station. So these are pictures of the campus, this is the chapel, it's a multi-confessional chapel. And here you have some service buildings, and this is the slope going to the library.
It's a picture taken from the library. Okay. So let's now go into more details, and essentially focus a little bit on the future of artificial intelligence. Of course, there is a famous joke that has been attributed to several people, but Quote Investigator (which is a website) has attributed this quote to the Danish politician Karl Kristian Steincke, who is actually a Danish politician with a German surname.
But however, I don't know how the correct Danish pronunciation would be. And in his 1948 autobiography, which was with the nice title 'Bye and Thank You', and the quote is: 'It is difficult to make predictions, especially about the future'. So in Danish would be something like 'Det er vanskeligt at spaa, isaer naar det gaelder Fremtiden.' Something like that.
Sorry for Danes who are here, if any, for my bad pronunciation. However, I have identified three main important trends both in artificial intelligence and in data science. The first one is the probabilistic causality. The second one is the approximation of solutions of partial differential equations using machine learning tools. And the third one is a rigorous results on machine learning and deep learning to really understand why the methods work. Let us start from point one probabilistic causality.
So if you remember how all the fuss with data science started, there is a famous article in a popular science magazine, actually it's more than a popular science magazine, it's a magazine that is magnifying a little bit the Silicon Valley, etc. So Wire, yes. In Wire they wrote, maybe a little bit more than ten years ago if I remember correctly, this article or this column in which the author was essentially claiming that with the large amount of data we have available, as you have seen in the presentation by Julie, Julie Weeds in the previous talk, we have this data and we do not need theories or models anymore, but we just analyse the data and we extract the truth from the data. So this is the idea of that particular article, some science without theory and without models. To be fair, this is completely wrong, in my opinion, for several philosophical and also practical reasons. But luckily there has been, in computer science and artificial intelligence and data science, this trend of using a probabilistic causation model in order to understand what is going on and how data is really generated and which are the relationship between variables that made up that data.
So, for instance. Connected to probabilistic causation, there is a theory for Directed Acyclic Graphs that is represented in this picture here where essentially every node, these circles here, represents a variable and every link here represents a causal relationship. So for instance, here you have a red node labeled with D, which is the direct cause of this green node labeled by S.
So this could be, for instance, a disease causing some condition. And this is the most straightforward case of a Directed Acyclic Graph where you have essentially a simple direct cause and effect. So the analysis of this situation is rather straightforward. There might be functional relationships between S and D. So typically, for instance, if you are considering statistics, statisticians that use what they call general linear models, and maybe there is a linear relationship between S and D for instance, but the theory of probabilistic causality, whose main contributor is Judea Pearl, generally it's not necessarily needed to define the specific functional relationship between variables. You may have situations in which you, for instance, find the correlations between D and S. So these two variables may be strongly correlated, but there is no causal relationship, no direct causal relationship between D and S just because they have a common cause. This is the case in which you have a common cause and this is represented by this variable R, which is a cause of D and a cause of S.
So this is a typical situation where you have a so-called spurious correlation, and if you do not have a theory or an idea of how your data is organised, you will make the mistake of assuming that one of these two variables is the cause of the other, especially if you do not have specific measurements of R. The third case that I am representing here is the common cause plus a direct cause, so it's another possible Direct Acyclic Graph where R is a common cause of both D and S, but then D has also a causal relationship with S in the particular cause of S.
So if you can express by experiment, for instance, the effect of R, you will be able to see the direct effect of D on S, but if you cannot, R is acting as a confounding variable. And again you are in trouble if you do not know anything about R and sort of see strong correlations between D and S, and you have maybe even a theory that these are the causal S, but you cannot understand the perturbation of the confounding factor.
So this is a very important part of the future of data science in my opinion. A second topic that I would like to cover is the approximation of solutions to differential equation problems, in particular, partial differential equations. Why I am focusing on this? Because essentially deep learning, and in general neural networks and other machine learning tools, are quite powerful interpolation tools.
And so they are very good in solving mathematical problems for us. And they do it very quickly (of course, after a training phase). If you are interested a little bit in machine learning, you know that neural networks are layers of neurons connected to other neurons with activation functions which may be non-linear, and weights. And if you have, say, some input variables here and some outputs variables here and you start teaching the neural network to recognise the outputs variables, even the correct and input variables using several methods, including the famous Backpropagation algorithm which is starting from the output and computing the weights, optimising essentially some distance between the output of the network and the actual outputs. Yes. If you do all this, it takes a lot of time, but once you have done that, it takes a second, even less than a second, microseconds or below to get the result of a very complex computation. So this is an example of the paper of some colleagues of ours from Airbus and Imperial College who are using neural networks to solve partial differential equations, and in particular the partial differential equations that you would have to solve to study the profile of a wing for an aircraft. So, Sanjiv Sharma, I think, is working for Airbus and Francesco Montomoli is working for Imperial College.
So this is an example of this. And to be fair, the methods are very good, however, you have to be very careful when you use them. So I am really very worried for the future, again, for this reason. So we are now building a dual machine tools and these beautiful machine learning tools are able to solve very complicated problems.
However, they work very well only in a narrow range of parameters and variables. If you use them in another range, they will give you a completely wrong answer. And of course, if you are an expert in the field and you are using these methods, you know that. But now let us consider a situation where people are not studying these things deeply and they are just users.
And the generation passes, and they maybe transferred this knowledge, right? And then another generation passes and this knowledge is transferred. But maybe then this knowledge is lost and you use the machine learning tool in some wrong regime. And if you are using it for critical problems such as the design of an aircraft, you can determine disastrous outcomes.
Okay. So if you're lurking in the background here, there is no point. That if your knowledge is not deep enough, if you do not study enough, you may be the source of a disaster. And finally, the third point I wanted to briefly mention, because it's definitely probably outside of knowledge for most of you, is proving rigorous theorems on some aspects of deep learning. It is very important because it is only by using mathematics and using theorems that you can be sure that something is correct.
Okay. And this is also resonating with the previous aspect. Okay. So I see that there are several questions coming in the chat and I will stop sharing the screen for a second and have a look at questions. So the first question is about the deposit fee and I cannot reply but I see that there has been a reply in the discussion.
Let me see if I can find any questions. So hi to everyone, by the way, again. Assistance on applications. Yes. So scholarships are available and I will post a link. Essentially, if you look at the links in the main session, there is a link where you can find the information on the the MSc programmes. Maybe I can show you how you can do that.
So, for instance, let me share the screen once more.
And let us abandon the presentation for a second and let us go to a web browser. So I'm looking for the website of the University of Sussex, here, for instance, and I want to study Data Science Master's. Search and you will see the programmes in data science here. And let us pick, for instance, the data science MSc here, and here you have plenty of information including the typical requests for application.
Like you should normally have an upper second class undergraduate degree or above. And the qualifications are clearly physics, engineering, science, computing, mathematics or life sciences. You might have other professional qualifications that can be taken into account, but of course if your background is, I don't know, in the history, psychology, philosophy, economics, we have to check whether you may stand a chance of passing our exams.
But if you go down here or you pass the modules, you pass our pictures and here there is information on fees and scholarships. And here is 'How can I fund my course?' So in here you can see several opportunities for scholarships. It seems that there are scholarships for students coming from certain countries here. This is depending on where you are coming from.
So I wanted also to point to the Artificial Intelligence and Data Science postgraduate conversion scholarships for people from groups currently underrepresented in the fields of artificial intelligence and data science. And I think if you go here and find out more, you can find if you are eligible for this particular funding. So you can see also the number of scholarships available this year.
25, the deadline for applying and how to apply. Good. So let me stop sharing again for a second.
Yes. What degree background is necessary to study data science? I think I answered this question just now.
What are the fundamental topics that one needs to prepare for September? I would like to mention some programming, like for instance, if you have experience with Python, R, you could revise this, and probability and statistics, basic probability and statistics. If you are applicants, you will receive a specific letter with a link to where you can find further information, the online version of these MScs is not yet available.
We are working on an online Data Science MSc, but this has not yet been approved by the University of Sussex. Fundamental subjects they should know before starting this program: so I was telling you, Python, R, other programing languages and also probability and statistics. Many of these questions are on the same topic, so I will go on with the presentation.
I have 10 minutes more, roughly. Maybe a little bit more. It should be 1330. So it is time to share screen again.
So just to give you an idea of the things that you will learn in in the programme, I have here created a first Monte Carlo R program. So now you are probably aware of the debates in data science between Python and R, now we have a series of modules using Python and we also have included for the next academic year, especially for those students coming from backgrounds where they have not [worked with] Python before, Programming through Python modules.
But if you are not a programmer or you have never programmed, you might consider that this is not really the MSc program for you. Because we cannot teach you to program as if you were a student at the beginning of your bachelor's studies. So we have to [assume] that at least you have seen some programing in the course of your studies.
However, we give you plenty of examples. This you can see here is a code in R. By the way, even if Python is present in many modules that you might follow, you might have to learn how to program R if you are not yet able to do so. And even MATLAB or even other programing languages.
So you need to have a great flexibility and a great will to learn, and also self-learn how to program even though we will try to provide you all the material which is needed. But be careful that again, if programming is not your cup of tea, these particular MAasters are not suitable for you if you do not like programming or you do not like to work for hours on a computer.
So this example here is taken from my Monte Carlo simulation module. It is very trivial, so let me run it first and then I will comment. Let's see if it gives some error. No. So what you see on the screen is a histogram of random numbers that are uniformly distributed between zero and one.
So the random numbers uniformly distributed between zero and one are generated by this command here 'runif' in R. In other languages the syntax is slightly different, but more or less this is the situation. This is 1 million points. So the capital N is the sample size. And this command 'hist' is creating the histogram. The second floor of the square root of N is computing the square root of N, and its integer part is the number of beings . And probability 'equal to true' in the command 'hist' means that you normalised the histogram so that the integer below the histogram from zero to one is equal to one. So providing probability density function, for those of you know what a probability density function is.
Incidentally, if you do not know what a probability density function is, you might again reconsider and think, if you have never heard about the probability density function then perhaps this is not the right program for you. Or at least you might be willing to study probability and statistics using some textbook, some links, etc. before you come. And then the rest is just to plot the theoretical value, which is just one. From zero to one and zero elsewhere.
And this first plot command is plotting the histogram points here as points between zero and one. And in the Y and the X axis and between zero and the maximum 0.3 on this end. And then this lines command here is superimposing the theoretical value of the probability density function. So you see perhaps this blue line here. And this blue line here is the theoretical value of the probability density function.
And you see that the histogram of the empirical Monte Carlo simulation, it gives you fluctuations around this line. And this is a typical behavior of histograms generated with Monte Carlo simulations. And as you increase the sample size, the dispersion becomes smaller and smaller as a consequence of a theorem behaving concurrently, which is in itself a consequence of the strong flow of large numbers.
Yes. So this is an example of what we teach. So it's a good time to go back to the chat and see if there are question. So there is a question - if students can have extra credits to offer more courses during the semester. You mean if you can follow more than the prescribed modules or courses?
In principle you can, but it is not recommended for the main reason that already following the standard modules, your time will be full essentially. So I would not recommend to follow extra modules. You can follow them as an auditor, so someone who goes there to watch the lectures, but I would not recommend taking the exam. Secondly, I wanted to find out if we can get resources to prepare ahead of the session commencement. Yes. So as I was telling you before, the applicants (those who have applied and accepted the offer, etc.) will receive, after the acceptance of the offer etc., a refresh letter from the university signed by the conveners. So it is formally signed by me in the case of data science, where there are several links and hints on this. I see that Rob is also adding something.
Is there any possibility of coming with your spouse or family, etc.? Be careful because this is the United Kingdom. It's a country with a home office and it has visa rules. So go to the website, but at least they are quite clear. They are quite clear. So they give you most details that you need to know.
And I will go now back to sharing the screen to conclude my presentation. I still have say 5 minutes maximum. Just to say that if you come to Sussex you will be sort of embedded in a research environment where many people are working, using data science as a tool, or their main research is data science. We have a research program which is called DISCUS on which I will tell you a couple of words and then research and data science is present in the Department of Informatics, of course, Mathematics, Physics and Astronomy, but also in the Sussex Humanities Lab.
DISCUS is the Data-Intensive Science Centre at the University of Sussex, and they offer several helpful elements and tools for MSc students. For instance, they collect interdisciplinary MSc projects for your final dissertation. As mentioned by Julie, there is the Industry and Peer Mentoring programme, and a Student Challenge set of events, and also informal data science seminars for PhD students
and Masters students. Final words on your career. Of course, there are two kinds of data scientists: developers of new ideas which we learn normally who might be developers and creators. And then there is a set of users of existing methods. So my advice to you is to become developers and creators. It might be more difficult, but if you become just a user of existing methods, there is a very fast obsolescence in data science.
And in five, ten years you may face difficulties and even out of the job market if you are only users. As mentioned by Julie, we have contacts with several companies. This does not, however, guarantee for those of you who are interested in an industrial placement here that you will be getting the industrial place in here because the industrial placement is like applying for a job.
Essentially you have to prepare your CV, you have to submit your CV, the company has to come back and invite you for an interview, have they have to like you at the interview before offering you the placement. So this is the reason why the placement is not guaranteed. And in the past, there were several questions about professional accreditation in the United Kingdom.
Currently, there is no professional accreditation for data scientists and data science, but it is forthcoming, as you can see. The Royal Statistical Society is leading efforts to create the profession of data scientists, which will have specific requirements. And finally, as I was telling you before, you can always go in and check our webpage.
You can contact me and people in admissions, etc. And so thank you and goodbye. Actually, this is the end of the presentation and I can stop sharing the screen. And we have 3 minutes for further comments.
And just to answer a quick question that's on the chat, English language requirements from Ghana - we have those requirements listed on our website for all countries, for English language.
So even if you're from the UK, we still need to see evidence that you can you can use English to the correct level. So you can find that on our website. For Ghana we accept the results from WASSCE so you will be able to use your school requirements for your master's application.
Deferrals are usually possible. I recently joined Sussex, so I may not be up to speed with the exact requirements of whether it's possible to defer. And if so, for how many times. So do check the details on your offer letter once you've received that and we'll. let you know. Last recommended date for deposit payments?
Well, if you need to have a CAS then you need to provide that for us as soon as possible so we can get your CAS because we won't release that until we've had the deposit. So that's worth bearing in mind, you need to plan in time for your visa application, which can take a long time depending on when. And the global pandemic obviously delayed things. Just running through the list of questions. Thank you for the extra link there. The scholarship requirements and the amounts are vastly different depending on the awards. We have scores of them, so I'm not fully up to speed with exactly how much each one is worth. But generally speaking, if you're awarded more, if you're successful for more than one Sussex scholarship, we will usually give you the higher value awards, not the lower.
However, all scholarships are, as you would expect, competitive, so it's probably quite unlikely. You may be successful in getting an external award, for example, achieving a scholarship that could be combined with Sussex Awards, but normally not to more than the value of the tuition fees. How long is the application process? We like to think it's pretty swift. That will vary.
Swift will vary depending on what time of year it is and how many applications we have. The earlier you apply, the faster you get a decision, generally speaking, because the longer you leave it, the more applications we have to deal with.
Scholarship requirements are all listed online. It does vary from from award to award. There are some scholarships which are only for certain nationalities. And then I'm just going to get the link back to the main session as well - so I'm going a paste on the chat box so that you can follow the link.
I've just pasted that. It'll take you back to the main Zoom session. And so we are just finished here. So that is us. Thank you very much for listening. I certainly learned a lot. I'm not sure I'm going to be joining data science anytime soon. So it's a good thing I've already got employment with the university. Thank you Enrico for that presentation, it was really interesting. Nice to see all of you from so many different places. We hope that you will join us at Sussex.
- Webinar transcript
Brilliant. Nice to meet everyone.
So my name is Christopher Buckley, I'm a lecturer in Neural Computation at the University of Sussex and I teach on the MSc programme.
So what I want to do today is kind of give you an overview of some of the techniques in artificial intelligence that we teach here.
And I'll point to some of the things that I teach on the course as we go through.
But generally, I want to give you a kind of flavour or taster of the kind of ideas that you're going to come up against if you come and do the MSc here.
So just a bit about me. So as I said, I'm a lecturer here, but originally I was a physicist back in the day. Then I went into neuroscience and started getting into AI. I lived in Japan for a while, doing kind of the interface between AI and artificial intelligence.
I came to Sussex to to teach these themes here, so I'm very passionate about what I do.
So there are talks called paradigms in artificial intelligence, so it's possibly kind of a bird's eye view the types of techniques you can expect to learn here.
So first of all, what is artificial intelligence? I guess it's very simple.
It's built machines that think and problem-solve like humans and animals - important, right.
I think typically most people tend to think that AI is about building human level machines, but maybe we should think about animals and come back to that towards the end. And there's two kinds of real aspects of it.
One, a common aspect is it's an engineering endeavour, right?
So it's to design algorithms by studying natural systems. We take how the brain works, perhaps, or how animals work, and we try to use that to inspire algorithms to do smart things in technology.
But there actually is another side to AI, which is to use our understanding of machines, our understanding of algorithms, to shed light on the nature of intelligence in biological systems.
So this is kind of a quid pro quo between science and engineering. Equally, we go from the science and the biology to new algorithms,
we also take the framework and the theory of new algorithms to try and reflect back on what we should expect in biological systems and the kind of interspace I live at.
I kind of live between neuroscientists, on the one hand, looking at the brain and the biology and measuring from neurons, and developing AI algorithms, on the other hand.
Cool. So I'm going to try and give you this kind of overview of AI. I think typically people monolithically see AI is one subject, all one type of thing.
But actually there's kind of three core distinctions within AI, which kind of emerged historically, but still exist in modern pursuits of AI.
So I'm going to give you a guide to those. One is introspection, using introspection to design AI algorithms; the idea of connectionism, so looking at the brain, looking at biology to build AI algorithms, and the last one is the appreciation of embodiment and the appreciation that intelligence emerges from the interactions of brain, bodies and environment.
It's not just a property of the brain. So I'll go through those three different areas of AI.
The first is introspection.
This is the oldest approach to AI So this is back in the 1950s when people thought, how are we going to build intelligent systems?
Maybe the best way to do that is to introspect, to look inwards on ourselves, work out how we think, and then use those ideas to construct AI algorithms and artifices.
Right. So this has led to something called a kind of symbolic AI.
So the idea that we represent the world in terms of symbols, and we manipulate symbols or representations of the world.
So the idea is that I look at the world around me, I see chairs, I see apples, I see whiteboards and so on.
And when I process the world, what I'm doing is manipulating those symbols.
You know, how do I get to my apple? I move towards the table and take it off the table.
And so it is kind of symbolic representation. Right. And you'll learn about those at Sussex, and it's very useful in various areas of biology and various areas of data science and database driven systems.
So this is one particular use of it. It's kind of cataloguing the biological pathways in biological systems.
So we have these labelled pathways and there's a very complex interaction of these kind of representational parts of the system, right?
You'll learn about this in something called Intelligent Systems Techniques if you come here, so how to deal with databases in a smart way and so on.
This is another example of this type of approach, and it's something again, you'll learn about here called Natural Language Engineering.
So it's the idea of taking text and then doing somehow some kind of intelligent processing of the text.
So what we're going to do is work out what kind of emotions are represented within the text. So it's a very symbolic thing, and extracting meaning from this symbolic level of text is a really valuable thing.
So this is one of my favourite examples. This is from a few years ago now, but it's developed by the laboratory here at Sussex.
So what you'll see is I'm going to turn the volume down slightly so it doesn't completely dominate the feed. How do I do that?
I'll just go off this quickly, and. Right.
Yes. So what we have here is A generic talent show competition, right? So we have five contenders trying to win the public vote for a talent.
This is one particular singer in this talent competition. What we have on the left is a dynamic, real time tracking of the online sentiment about the singer in the Twitter feed.
So what this algorithm is doing is extracting all the data from all the tweets across the Twittersphere, and in real time, analysing the sentiments of those tweets.
So are the tweets say good things, or are the tweets saying bad things about this person? And what you're seeing here, this yellow line, is the degree of positive sentiment, so what you've seen is a peak.
So she did quite well at the beginning, and positive sentiment was all over the Twittersphere.
And then she messed up her lines at this peak. And you see now all the negative remarks creeping get into Twitter.
So basically, what we're doing here is in real time tracking the progress of this individual by having intelligent algorithms that can analyse whether a tweet is positive or negative.
And they've designed an algorithm to do that. But you can think about the kind of advantages and the possible uses of this algorithm, perhaps in politics, perhaps you can, in real time, track the sentiment of the public about government policy.
So this is a very interesting thing, a component of natural language processing.
OK, so that's the symbolic notion of AI. So when we think of intelligence as representing the language in terms of symbols or text or language, right?
But what happened actually in the late 60s, 70s and really kicked off in the 1980s?
Well, there's a movement away from this notion of introspection being the dominant paradigm in AI.
So people said we shouldn't be looking at how, you know, abstract thought works if we want to build intelligent systems.
What we should be looking at is biology. Right?
Not introspecting in their own minds, but actually going in and looking at the wetware that's underlying AI algorithms.
And this gave rise to something called the connectionist movement.
And basically, what these people said is: actually, if we look at the brain, we can't see symbol representation very clearly.
What we can see is a network of cells firing electrical signals to each other in these vast ensembles within the system.
And then people thought, right, let's use that paradigm to build intelligent systems.
What we're going to do is train these so-called neural networks to implement impressive feats, right?
So most people here, I think, will be aware of connectionism, which has led to things in neural networks, deep learning and so on.
Right. So this is the kinds of systems you're going to build. They don't look like representational systems, It's very hard to see where the information is represented in these systems/ There's only representation really at the inputs or the output and within them, there is no sense of kind of representing, or symbolic representation of information, right?
And this has really dominated now. What happened actually was we had an initial surge in connectionism in the 1960s and 70s, and then there was a crash because of some fallacies that were told about connectionism by people who were more interested in symbolic AI.
And then it slowly developed again back into the 90s and 2000s, but didn't really make much progress.
In fact, it was kind of shunned in conferences, even like NeuRIPS, Neural Information Processing System Conference, didn't really like anything to do with neural networks for quite a while.
And then what we've seen, and I think most people will be aware of this, is a massive resurgence of neural networks in 2012, 2014 and so on now, and we're seeing the domination of so-called deep learning in these systems.
And basically what happened is, you know, basically neural networks only really work when scaled up to large, large systems.
When you do small neural networks, they're actually not very powerful.
And so what happened, before 2010, the kind of processing power that was out there on our desktop machines and even in our clusters to do, you know, interesting things in neural networks wasn't there. And then what happened, when people found that we could scale up to very large and deep neural networks, and wait long periods of time in training, that we could do really, really interesting things. So the thing that's rescued neural networks is not a theoretical development.
It's just the kind of ability to get cheap compute and run these things in large scale.
Now they really dominate AI. So the reason they dominate is something called the backpropagation algorithm.
So basically, they dominate because they're very easy and very efficient to train.
So this is the general idea of what a neural network does.
It takes some kind of high dimensional input at the beginning, perhaps an image, in this case we're looking at handwritten images in the case of this figure 7, and then it maps through a set of nodes, which are supposed to reflect these little neurons in the brain to a classification of the input.
So here it's supposed to give an output, but by firing one of the particular nodes, it indicates that there's a 7 being shown to the system, right?
And how do you train that type of system? It's a very simple idea actually. What you do is, you initially start off with a random neural network, you put in the figure 7, and it outputs something random and not good.
And then what then you do is you take the difference between what you wanted the system to output and what the system actually outputted, construct something called an error and then use that error to inform the system how these nodes are connected back through the network. And this is called backpropagation.
And it's the success of this algorithm, the success of this algorithm on large machines that's really driven the deep learning revolution, and you'll learn about this in your machine learning module here at Sussex.
And so this is all very interesting, but actually there was another kind of big paradigm shift in AI which happened in the late 90s and early 2000s.
People started to get upset that most of the ways that we think about intelligence, and people who think about intelligence and AI, was in a very static way, right? Typically, people were thinking about intelligence as something like chess playing.
So you sit there very cerebral, you're very devoid, divorced from the environment.
You look at the input. You can think about the input for as long as you want, and then produce an output.
There's no real time in that system. And what people started to notice is actually most of intelligence is not like that, right?
Chess is a very specialist thing, the only people that do that are humans and we don't do it all the time.
And most of what we do is this kind of dynamic interaction with the environment.
That's the kind of core intelligence, right? So you think about the intelligence in this cat that's doing the washing up on the right hand side.
This is a very different notion of intelligence. This kind of smooth control, the smooth combination of sensory input, motor output in this very controlled environment is actually really, really difficult to build, right?
So we're very good at chess playing computers.
In fact, we can train a chess playing AI now on our laptops, but we're very, very bad at actually solving this problem with smooth, real time control. And this is kind of a real edge of AI; embodied, embedded and dynamic AI, right?
So this is moving away from the traditional idea of the brain computer, to more the idea of the brain as a control system, and it's more in line with robotics than it is with machine learning and pattern recognition and so on.
So what happened really in the 90s and early 2000s is that when people really got serious about building robots, they found that the old paradigms, symbolic AI and an even traditional connectionism weren't good enough to do what they wanted to do.
So this kind of embodied embedded AI now forms its own subject in things like reinforcement learning, control theory and so on.
So here's some really good examples of it. This is from Boston Dynamics.
If anyone's seen it, there're probably cooler videos out there at the moment.
We could compare and contrast the intelligence in a system like this to the intelligence you see in a chess game computer, or Go-playing computer, or even Atari-playing computer, right. So a very different notion of intelligence.
So there's a split within the AI community, you know, people who study this type of thing, and people study kind of more pattern recognition classification systems.
So kind of almost a different language of those two different communities.
And what it's also driven is a kind of a difference in what we think is important in trying to account for natural intelligence, right? So, you know, the deep learning community tend to think that if we can understand how deep learning neural networks classify and generate perhaps images, that would be a good insight into how the brain works.
But the embodied AI people say, I know most of what the brain is doing.
99 percent of what the brain is doing is just dynamic engagement with the environment, which we really should be thinking about those types of system.
OK. But that's a really good example of where actually we're not very good at it.
So this is a more representative example. The example you saw before was a highly groomed promo video from Boston Dynamics. This is from the DARPA robotics challenge. So this is more typical of robots that work in normal environments. They're really terrible.
We're really still bad at both the theory and the technology behind building these systems. It's still cutting-edge to build decent systems.
So these robots look very silly. So, you know, people have started to try and apply deep learning to robots, so you can now try to combine these kind of can connectionist ideas with these robotics.
But actually, there are real key problems in doing that because you need so much data to train these systems.
So typically, people have tried to overcome this by building massively parallel robotics systems and so on, but it hasn't been as successful as you can think because of various technical challenges.
So an alternative way, and some of my colleagues at Sussex, what they think of doing is, instead of actually just having this very kind of raw and, you know, learning driven approach to building intelligent systems, what we should actually be doing is mimicking the intelligence we see in the biological world, right?
So let's get rid of an obsession with human level intelligence, playing chess, but let's see if we can get the intelligence of a bee or an ant, right.
We can't even achieve that in most of our kind of intelligent artefacts that we have out there.
For example, this video is an example of bee navigation in complex environments.
So basically, we can't even recapitulate that kind of degree of robustness and processing that bees are able to do and navigate over large areas of land in their systems.
And what people, my colleagues, have tried to do is copy the way that bees and ants work in insilico artefacts to develop a kind of robust navigation systems for drones and quadcopters and various things, right?
So this is the type of stuff they're doing, they kind of copy the nervous system and the techniques that these systems work.
And then they try to build them into mobile systems, wheel systems, flying systems and so on.
And you learn about those types of things on the module of intelligence in animals and machines here at Sussex.
So why? I mean, I think even if you've all chosen AI, I can say that you've made the right choice for career prospects.
It is definitely the subject to be in in the moment, right?
There's a huge rise in the interest in AI and the amount of resources have been invested into AI.
So first of all, we're in a data rich world, right?
So there's 50 million Wikipedia pages, 800 million users on Facebook.
If we want to start the process and make sense of data like that, then we're going to need automatic and intelligent algorithms to help us do that.
So there's a real drive and need to do it because of the big data we're immersed in these days.
And you can see the interest in AI just in terms of the number of papers being published in the last decade.
You can see this kind of massive increase, almost an exponential increase in the number of papers being published.
So there's huge, huge interest, there's a booming ML job market. There's a real lack of people with the expertise in this area.
Data science in itself has become one of the largest tech industries in the UK now.
So it's a really nice place to be, in artificial intelligence.
You've already made the right choice and you can come with us and study how to do it and choose one of your paradigm - robotics, data science or, you know, symbolic AI, whatever you want to do.
Thank you very much. I'm happy to take questions. I know that was quite rushed.
I've had to do the talk at a slightly last minute, so I apologise if any of that was unclear, but I'm very happy to take questions from anyone who would like to ask them.
Fantastic, thank you very much, Chris. I know your raced through it, but very comprehensively so.
So we've got to go a few more minutes, so we're going to head back to the other room at 1.30pm.
So if people want to ask some questions now or contribute to the conversations... So I've got a question about statistics.
I don't know if that's something you have on the top of your head, Chris.
Yeah. Is there any statistic to show how many Sussex graduates could find related jobs early after graduation?
So I don't know that statistic, but I do know is the employment rate for graduates from computer science is extremely high.
So we're in something like the 99 percential range for people getting graduate jobs when they leave education in Sussex.
I don't know what proportion of those have gone into into AI jobs.
Yeah, it's very, very high. Maybe 99% is an exaggeration,
It's like 97%. It's somewhere up in the 90s. So, I mean, that's just computer science. The ability to programme now is in huge demand.
There aren't enough people who can programme to fill jobs.
And then within that realm of programming, AI is emerging as well as even hotter, right?
So being able to do machine learning and programming is such a valuable skill now you'll almost fail not to get a job in this area.
Every company wants to invest in data science, and so it's a really, real growth industry.
So I don't know the exact statistics, but you definitely see something on the web, on the informatics websites, actually.
So for AI it's actually 94%.
I think I actually that that figure has changed. I think I've seen a presentation by Ian and that's higher now. And also that's that figure doesn't account for people taking gap year. So it's people in graduate level education two years after they've graduated. But some people go travelling. And so yes, we're pretty sure it gets close to 99% in reality.
Very good point.
What is a prerequisite level of knowledge for this course? So this is MSc, so we expect someone to be numerate. So we try to make this course as self encapsulated as possible.
So you have a course which will teach you the maths that you need for artificial intelligence, computer science in general.
We'll also have introductions to programming, right? So we don't actually expect you to programme when you start the the MSc.
I would say that being able to programme has a massive dividend.
And it's much harder, obviously, if you don't programme, but you can do it.
So for example, I actually did this MSc and I'm a good case to do this right.
So I did it quite a long time ago now, 10 or 12 years ago or something, and I couldn't programme when I started the MSc.
And so I did find quite a hard learning curve in there in the first term, but it was it was overcomeable and then I began to love it.
Once you can programme, so many things open up. If you can programme, then you can always push yourself more and more and more.
Right. So all the people who teach at Sussex are research led, so they're either working in the cutting edge, machine learning AI research or data science research or some parts of computer science, so you can really push yourself to whatever degree you want.
So we really try to cater for people who don't come in with any programming to people who come in as really good programmers at the start, So what language you use apart from Python? So it depends really on which areas you go into.
So if you go into actually data science, machine learning, so on, then Python really is the go to language.
But if you go into something a little bit more robotics, where time is more important in whatever you're building - so if you're building that real time control systems, that's going to be C++.
I would say that AI is so dominated by Python now that most of what you're going to do here, if you do the AI and Adaptive Systems course, will be in Python. Most of the modules, though, are kind of a language agnostic.
So there is coursework to build something and they the will let you do it in whatever language you want.
I certainly let people do that on my modules. And certainly, if you file your dissertation, you can treat any language that you want.
And, Chris, they say they want to see us, so if I don't know if you can share your video.
It says unable to start video.
Let's see if I can make that happen.
Do Sussex have specialised facilities that partner with MSc students when doing their dissertation?
For example, if creating a predicted model for the thought processing, are there collaboration opportunities?
Absolutely. Yes, absolutely. So, you know you're in the right place.
I like that. I like that tone, that question. So we pride ourselves on being an interdisciplinary university.
So we really encourage collaborations across traditional discipline boundaries, right?
So for example, I sit down and do research between life sciences and informatics.
I do computer science, but I also work with biologists. And so there's lots of collaboration with psychology.
There's lots of collaboration with philosophy if you want to, and there is some collaboration with the humanities too.
So there is. And during your dissertation, particularly, you'll be encouraged to to explore those types of collaboration.
And also, many of the modules are influenced by lots of lots of different disciplines.
And you might be taught by someone in biology and then in informatics.
So I know Intelligence in Animals and Machines is half taught by biologists and half taught by people here in informatics.
So you will get a lot of cross-disciplinary stuff going on.
Chris, you should have [camera] rights now.
There we go. Hey, you were missing my pi t-shirt! That's the uniform of an AI lecturer.
How does this differ from data science, from a job market readiness perspective, i.e. what roles do you see graduates doing?
So we teach data science, right? But there is an MSc - wait, so OK. So (Artificial Intelligence and Adaptive Systems) is more a broad sweep across AI.
So the data science MSc is very, very much focused on intelligent use of data, NLP, databases, analysing market data, classification systems.
But here in AIAS, I think that's the right acronym, it's changeed from my days, actually.
So I can't remember. But it's it's more focused on the broader suite.
So we do robotics, we do adaptive systems, we'll do things like control systems, we do things like reinforcement learning, you know, the control of objects and robots in the environment.
So it's a broader set of skills. We also have a lot more biology in what we do, right?
So there's a much stronger interface with neuroscience and biology than you would see in data science.
So data science is really about using machine learning as a tool to do smart analysis.
So it's a really fantastic subject, but we're more kind of 'broad stroke' looking at AI.
We do do data science, though, so you'll learn NLP and you'll learn data science techniques and you'll learn machine learning and AI connectedness.
We also do reinforcement learning of a much more broader sweep of things.
Is there an opportunity to work with researchers as an assistant during the course? Interesting.
During your teaching, that's unlikely. I think that we do have people who can do TAs as teaching but you probably won't be when you're on the Masters. There are sometimes opportunities in the summer to do kind of projects that we share half in industry and half within the university, which are paid.
But I would say that it's very hard to do that as you're learning the MSc.
Is robotics more AI than embedded systems and mechatronics?
So the difference between robotics and, I think we have a mechatronics MSc, is that we're kind of focused on algorithms, so we don't really build robots, right? So I don't - I'm really bad with my hands.
I've not done electronics. We talk about the theory of algorithms, the theory of systems we build.
Most of what we do is built in software and we're not really going down to electronics and building the mechatronics things, which is a whole new subject in itself.
Can you share any success stories from past students? Yeah, well lots of people have set up companies. Quite a few people have gone into neuroscience and become very senior in neuroscience, so I can't remember any names.
So one of my favourite success stories is that someone who did this MSc is now very senior in ecosystems engineering.
So this is the study of climate change and so on. He learnt modelling and data analysis here, and the ability to programme, and now he's a very senior academic in ecosystems and AI, climate science and so on.
So there is a lot of diversity because we can teach you a broad range of modelling and AI and intelligent systems.
You can really be very diverse in what you apply those things to.
Question: I'm quite interested in electrical electronics, as an engineering graduate I think learning AI is the right path. I'm quite interested. Yeah, I think it is.
In fact, most of the people who were the developers of AI, you know, 30 years ago, came from electronics. And that goes to neuroscience as well.
Electronics and electrical engineers were significant in development of AI and cognitive science.
Is an MSc in AI the same as computer science specialised in data science and artificial intelligence?
As I said, data science is more focused on this kind of processing of data.
It's the more kind of input output paradigm, how to extract the information I want from the data, how do I analyse tweets, you know, given a dataset, how do intelligent things with those datasets? Whereas AI is more focused on around the picture.
So we do control, we do something called reinforcement learning, how to make a robot learn how to walk, or how do you make a quadcopter learn how to control itself and so on.
Much more broader sweep of AI systems. While, as I say, we also do the main techniques of data science within AI as well.
What makes this course unique to other AI MScs?
I mean, I guess we have we have a very, very strong history of interdisciplinarity, as I mentioned before.
So we are deeply immersed within other schools. So you'll have a much richer idea set coming from you if you study here. So many AI degrees are, you know, you'll get the textbook approach to AI.
There'll be a nod to some kind of biological inspiration, but here we do take it very, very seriously.
We take the cross-fertilisation, particularly between biology and AI, very seriously, and we have a quite strong reputation for doing that.
We also have a very leading philosopher in artificial intelligence called Andy Clark, if anyone's heard of him, but he's written some seminal books, and he was in The New Yorker a few months ago talking about the kind of theories of artificial intelligence,
so it's definitely quite a hot place to do it in my opinion.
My offer is in artificial intelligence and adaptive systems and I kind of have some questions, please. Will the industrial placement take place outside of the school?
I think I might leave that one for Ian. I'm not sure about the industrial placement on an MSc.
So you going to speak to Ian later I believe. Is that right? So, yeah.
Yeah, industrial placements are generally good, I don't know how they fit in with the MSc, so I'll leave that to a colleague of mine later.
What are some types of placements that students have done? Again, I'll leave that one to a colleague.
I can talk about placements in the context of undergraduate, not in the context of MSc.
I don't know much about that. I'll refer you to a colleague that you'll meet later.
Can you share the email of your professor friend who is doing the modelling with AI?
Professor friend - who's that? I've got lots of professor friends.
That's what one of the side effects of working in academia is.
We can all be searched online. We've all got web pages and so on.
Any difference between artificial intelligence and cybersecurity and artificial intelligence and adaptive systems?
Yeah, I'm guessing there is. So - oh! Hi Ian.
So I mean, we don't focus on cybersecurity here. And AI in cybersecurity is a big topic in itself, so I'm guessing there are differences.
But I don't know the specific differences. I'm not the biggest expert in cybersecurity, but if Ian's here, he'll definitely be able to answer that!
Is he around? Maybe struggling with his audio, too?
Yeah, yeah. I mean, let's just carry on. Okay. How much mathematical knowledge is needed for the course?
So we kind of expect you to be numerous. Right?
So, you know, the people who come from degrees which have got a very scientific flavour are going to be better equipped to do the MSc.
But saying that, we try to cater for a very wide audience.
So we, you know, as I said at the beginning, our MScs are self encapsulated.
We teach you everything you need to know to do AI. so we'll teach you the maths, we'll teach you the programming, we'll teach you the theory in AI.
So in theory, you can come with very little knowledge.
Except for, you know, being bright and getting a good degree. You can learn everything while you're here.
So as I said before I couldn't programme before I did this MSc and I was able to learn it on the go.
I know that people come with very little maths and you can pick up lots of mathematics when you're here, So you don't have to have any mathematical knowledge but it always helps.
I'll let that one question to be answered by Tosin.
Yeah, I think we've got just one more minute. If there's one that we can take in 20 seconds, go for it! Please now rejoin the main session.
Great. I see Prabang, I saw your post - says I look like will.i.am.
Maybe I am. Will.i.am has fallen on hard times.
That's what's happened. Well, thanks everybody for joining in.
Nice to meet you all. Thank you so much.
Brilliant. Can we just give Chris some emojis of appreciation for his time!
Bye. Take care. All right. See you in the lobby. I'll wait for everybody to exit.
You should see the chat. The link in the chat to go back to the main lobby.
- Webinar transcript
OK. So I think that's probably everyone now.
And I just want you to take the opportunity to say hello, So I'm Isobel and I work here in the international office and I help students join us from overseas.
So predominantly I work with students who were joining us from Central and South Asia.
But I can also help with various different queries and things like that.
But enough from me. I'm going to hand back over to Julie now.
Thank you. OK, thank you, Isobel. Hello, everybody, again.
You've got me again for this session as human and social data science potential students.
Just to say that I'm convenor for the human social data science course.
So I would be a point of contact for you on the course and would certainly be talking to you in induction and be talking to you at a time of setting up projects, etc. Luke is saying he hasn't got audio. Can other people hear me?
Luke, I think it's just you.
OK, so the point of this session is just to talk a little bit more about what it would be like to be doing the human and social data science course.
And we set this up as a taster lecture, so what I thought I would do is talk to you a little bit about one module in particular which is a module that actually students on any of the three degrees might take.
But it is one that is on offer to human and social data science.
I'm sure this might give you a bit of a flavour of what it would be like to be on this course and what kind of skills you might want to work on in advance or what skills you will be learning once you get here.
So a module that I teach in the first term is applied natural language processing, so that's what I'm going to be talking to you a little bit about this morning.
It may be that you come and do this degree and don't do this module at all.
So that's, you know, this is not a core module to human and social data science.
However, it is an option and it is a quite popular option. A lot of students do choose this option, and students may want to know more about it to be able to make an informed choice as to whether it's the right option for them. And also, whether you do this module or not,
I think it will introduce you to the kind of skills you will need, even if you don't do this module.
Because one of the things that we'll be talking about over the next half an hour is Python programming, which is something we do in this module, and is something that you will be doing on at least three of your core modules, I think, in the first term.
And it also in machine learning in the second term, and you'll also be doing that on your dissertation projects.
So learning to programme in Python is something which you will be doing a lot of next year.
And again, as human and social data scientists coming in, we don't expect you to have done any programming at all beforehand, although we are, as of this year - and you will hear more about this should you accept your offers - offering an optional pre-sessional course in Python programming to help you hit the ground running.
But you will be learning to programme in Python through the different core modules and optional modules that you will be taking.
OK, so I said I was going to talk about applied natural language processing.
Let me just share my screen and let me run my slides.
OK. So as I said, applied natural language processing may well be an option that you might choose to do in the autumn, so some of these slides are taken actually from the first lecture that I would give as part of that module.
I've adapted them slightly for today's purposes, but it's quite similar to what you might find yourself hearing in a lecture in the first week in the autumn.
OK, so first thing that we would need to think about on this module is: what actually is applied natural language processing?
So I invite you to kind of try to break that phrase down a bit more.
What actually is natural language? Well, natural languages are languages which were invented by humans in order to communicate with humans.
So they include English. We're talking in natural language now, but obviously they also include other languages French, German, Chinese, Arabic, all of the languages that exist in the world, which humans have invented in order to communicate with other humans.
When we're talking about natural language processing, what we're doing is we're trying to get computers to handle natural language inputs and outputs.
Maybe only one of those. It might not be doing both inputs and outputs.
But certainly, we are thinking about that automatic processing of natural language, which might be in the analysis of the natural language, so it might be trying to... mine text for information, or it may be that we're trying to do some natural language generation as well.
When we're thinking about applying natural language processing on this module, what we're really doing is we're thinking of it in quite an applied way.
We're thinking about using computer tools and applications to do interesting things with natural language.
So it's not necessarily about the... we need to know something about the underlying techniques to understand how to use them, but it's more like driving the car rather than kind of building the car ourselves.
If you want to learn more about building the car of natural language processing, then you would want to take the advanced natural language processing module in the second term.
But here, what we're doing is we're thinking about: what can we get computers to do in this area?
How can we use computers to process text and be able to analyse it in order to gain insights from large amounts of text data?
Maybe we've got millions of tweets that we've collected on a certain hashtag, and we want to know what people think about the there a certain situation, maybe a sort of political situation in the world, or maybe it's a product a company's just released.
So that would be one application.
But I'd like you to think a little bit, as you should hopefully already be doing, what other applications of natural language processing are there?
And if we were actually in a lecture, I would probably invite you to talk to each other about it.
Or certainly, I would ask you to kind of put some ideas in the chat if we were doing this online, but hopefully we won't be in a hybrid or online situation in the autumn and we'll be in a lecture theatre or a seminar room, and we could actually just have a chat about this. What applications of natural language processing can you think of? I'm just going to go to Luca's question because he's just asked whether we can take the advance module as part of this programme.
We don't usually encourage our human and social social data science students to take the advanced natural language processing module.
That is a good point, partly because it is more mathematical and more programming orientated than this first kind of introductory module.
But it would be possible, if you and I, as the course convenor/module convenor, felt that you had the appropriate skills to be able to take that module, it could be swapped in to your degree.
That would be possible if you wanted to do that.
Or it might be if you've got those skills, you actually think, Well, actually, I want to do data science rather than human and social data science.
So that's something that we can talk about. before you apply or even once you've applied.
OK, so hopefully you've been thinking about what applications natural language processing there are.
And here are quite a few that I thought of.
So natural language processing applications include: being able to retrieve and rank documents on the web relevant to the query, providing answers to questions that people ask automatically to be able to understand the question, be able to retrieve the correct information and give an answer to those questions.
Translating documents or speech from one natural language to another counts as natural language processing.
We might want to simplify our documents for a child or non-native speaker.
We might want to summarise documents because we want to take something which is pages long and reduce it to a summary that's much shorter to read.
One that I started, which is, I think, probably very relevant to human and data social data science is opinion monitoring.
So trying to find products or companies with good or bad reviews.
There's user content moderation. So trying to identify when somebody says something which they shouldn't on social media or chat, trying to automatically anonymise things or filter out that inappropriate content automatically.
Automatic recommendation, trying to match up products and jobs and people.
So maybe doing some targeted advertising. Then there's another one which many people think of, I think we're all getting used to chat bots and virtual assistants such as Siri and Alexa. They are applications of natural language processing.
And there are many more which you may have thought about. OK, so why would you study applied NLP?
Well, I've crossed this one out because this one probably doesn't apply to you as human and social data scientist.
This applies more to the kind of artificial intelligence people. So I've crossed that one out.
But what really does probably apply to you is that, lots of the data that we want to process comes as text.
We want to be able to extract information and usable insights through text and answer questions such as: what the people think about... Donald Trump? Or Vladimir Putin? Or to come out of the political domain, what do people think about the new Apple MacBook? Whatever we might want to be able to extract. Identify the opinions that people have based on what is being said, potentially on the internet, on social media, and be able to analyse those opinions.
It's also an important part of policy for companies or policy for government in terms of knowing what people are saying.
What do people think? What do people think about their new policy? This will also drive potentially how the policy itself or potentially how that policy is managed and advertised.
OK, so what will you actually do in applied NLP?
Well, you would learn about some of the problems in NLP and the potential solutions. Because NLP's hard, let's say that, you know, in terms of perfect natural language processing, it's still a research area in terms of improving it.
So you'll learn about some of those, why it's difficult and what we can do at the moment.
A lot of the time you'll be deploying off the shelf NLP technology via the Python programming language, using libraries of methods and routines.
You'll be working with pre-existing technology and applying it to realistic sized datasets to try to extract insights from. You will be.
and this is why I think this is relevant to everybody, here learning the Python programming language and developing appreciation of the challenges of NLP.
What would I expect you to know already coming onto the module? I don't assume any knowledge of Python.
I will teach you the Python you need to know to apply NLP. However, I'm going to say this, and I will say this again at the beginning of next term. We do expect, you know, over the first couple of weeks that you will pick up what you need in terms of basic Python programming to be able to use Python within your different modules to be able to actually then learn more about natural language processing itself.
So while we're not assuming knowledge of Python, it wouldn't hurt for you to start building that experience with programming, particularly in Python before you come.
So if you've got any time over the summer, one of the best things you could do - people always ask about, What can I read?
I say you don't need to read anything but go do a basic Python programming course, and that will be really good preparation.
We're not going to expect that you have necessarily done that, but it will really, definitely help you.
We certainly doesn't assume any knowledge of machine learning. You won't be learning about machine learning as a module until the second term.
But a lot of natural language processing methods do use machine learning.
So we will be using machine learning, but we'll be using it very much in the kind of using it kind of way.
Applying it. But we will obviously be explaining some of those machine learning techniques.
So that would be your kind of first introduction, potentially, to machine learning techniques, which you then learn more about in more detail in the second term.
It doesn't assume any knowledge of linguistics beyond basic familiarity with the English language, vocabulary and grammar, so most of my examples would be from the English language. We occasionally have some foreign language examples in there as well, depending what we're doing. But it generally just assumes you're familiar with the English language.
OK. The other thing that I really therefore wanted to talk to you about today was Python, because we will be, on this module, applying NLP through Python programming.
So I wanted to do a little bit of an introduction to Python to actually get you going even now before you are here.
So. You're going to be starting to programme in Python.
What is all this about, if you haven't done any programming at all before? Well, Python code, you can think of that as a set of instructions that you want the computer to execute.
Often many times. This is why we programme: because basically we're lazy. We don't want to do things over and over again.
We just want to hit run and have the computer do it over and over again, so it will be much quicker.
We can do a lot more if we can code it using our programming knowledge that we're going to be building up and get the computer to do these things automatically.
The first thing we need to think about when we're thinking about coding or programming is how do we interact with that code? Because we need to be able to edit it.
We need to be to change those instructions. We need to be able to give it inputs. If we want to run the code, what are we running it on? Are we running it on a set of documents that we want to process? Or are we running it on a set of web URLs that we want to process in some way? We also want to see the results.
It may be that there's some analysis that we're doing on a set of documents, and we want to know how many of a set of tweets are positive about a certain product, and we want to therefore see the results.
So what we use is what is referred to in computer science as an integrated development environment. Or abbreviated to IDE. And you will see this when people talk about, what IDE are you using?
This is your environment that you use to help manage your code.
What we will be using on this module and across, I think all of the modules the do coding in this degree, will something called Python notebooks.
And we can access these. One of the nice things - There're many reasons, and we'll talk more about this probably next year, why we use notebooks - one of the advantages of a notebook environment, other than the fact that it allows us to do all of these things together in terms of editing, making notes, giving inputs and seeing the results, is that we can access our notebooks online via a service, a cloud service called Google CoLab or through dedicated software running on our own machines.
And one that which we tend to use and which is installed in our lab machines, and which, if you want to install it on your own machine at home is one that we would recommend, which you can install for free, is Anaconda.
Now what I've got for the rest of the session, we've got about 20 minutes left, is a notebook.
And actually what I'm going to do, is I'm going to put that link into the chat. What you should be able to do is go to this link and you will actually be able to see this notebook and atually you can interact with it yourself and actually you can use this as a kind of first programming tutorial.
And you can work through it after the session as well in your own time.
So hopefully you can all access that link.
I tested out my partner yesterday. He never does any Python programming, and he could access it on his own machine.
So whatever machine you're working on, it should open up.
And what you should see is something which looks a little bit like this.
I think I've changed it in terms of I know I changed the title. It doesn't say NLE 2021 Lab 1 anymore.
It says something like a taster, but in general, it will look something like this.
And what you see is that there is a mix of different things in the notebook.
There are what we call text cells and then there are what we call code cells.
So and I've sort of highlighted them on this copy of the first bit of the notebook.
So we can add text cells by clicking here and then we can just type text. Things that we want to write down.
So this text here was just typed into a text cell.
And then we have these code cells, which again, we can add by pressing plus code and we get something here, which is then a runnable code cell.
So and here we've got the first line of Python programming code, which is the function call print.
And what we're going to try to do is ask the computer to print this bit of text: Hello World. What happens is then we would run that code cell, to see what happens.
And I can bring this up actually on my machine and do it live in a second.
It's probably easier than actually looking at here. So wait a minute. Let's just switch.
I've got it over here. So what we've got is this one here. And so when I'm here, there're different ways in which I can run a code. So you can actually just click the play cell there, but actually when you get used to this, you kind of go for this shift + enter, which is the kind of keyboard shortcut. It's so much easier than always finding the little play button at the side.
But either of those will work. If I run that - actually, first of all I'm going to clear all my outputs from when I ran it earlier, when you load these things up, you kind of give you your last outputs.
OK, so if I click on that, what you see is the output from running that piece of code. And you can see that the computer thought about it a little bit and realised that in order to complete this instruction, it needs to put in the output here, this piece of text 'Hello World'.
It's under edit clear all inputs. Oh, thank you. That's what I was looking for.
Yeah, it's different between Google CoLab and between Anaconda. On Anaconda, it's on the kind of runtime menu. On Google CoLab it's here, yes. If I did clear all outputs, we can see all of those outputs are gone, and now we have to actually run them to be able to see what actually happens. And what you should have, if you've gone to that link, you probably need to make a copy of it in order to be able to edit it in any way.
And therefore you would need to save a copy somewhere so that you can actually edit the file yourself.
But yes, we can run these cells, and what we find is we get the output of the different cells.
I can just flick back to my slides. I'll come back to that in a moment, but if I bring my slides back up.
So yes, that's what we see. We've got our code cells, we've got our outputs.
And then, yes, this is actually what's changed slightly. This is CoLab, but they must have changed it because it used to have 'restart runtime' there.
And then you still need to clear the cells for what you would do. Slightly different on Anaconda; it's on something called the kernel menu.
That's a useful if you want to turn something off and on again and start again, restarting the runtime was a useful thing to know where that is.
OK. I've noticed there's a question coming in, I'm going to try to get to that question at the end of the session before we go back to the main session.
With any other questions that come in like that.
But I just want to kind of go through a little bit more on the Python programming as a kind of introductory session.
OK, so that was our notebook functionality.
The first thing that we learn about with our first inductory, Python notebook, and again, this is something which you would do in that first week of term, but again, we would expect you to kind of make good progress with it.
This is all things you're going to learn quite quickly.
So the first thing that you're going to need to be thinking about are other different kind of data types, and to know that any piece of data has an associated data type which tells the computer how to interpret the value. Because actually in the computer this is a stored as what we call 'bits'. Ones and zeros.
So it has a, allocation in memory. There're ones and zeroes there, and we need to tell the computer how to interpret that.
And so data has what we call a type. The basic data types that we deal with are integers, so these are whole numbers; floating point numbers / floats, which are decimal numbers; we have strings which get abbreviated to str, so it's something like 'my name is Julie' is a string; and then we also have this type called boolean, which are true and false, so this is just two possible values. And again, it's another kind of important basic data type that we have.
We then have to differentiate between variables and values.
And this, again, is something which is an important distinction to make because quite often what we want to do is, we have a value and we want to remember it.
We want to be able to use it later. So what do we do? We put it in a box, we put it in a variable.
We store it there for later and then we can use it again so many times later.
Each variable has to have a name. Some way to identify it, so we know where something is. It's basically a kind of basic filing system on the computer. Variable names can be absolutely anything you like.
But it does really help if you name your variables was in a way which helps you know what's in the box, what's in the variable, so it helps you to organise your code. So here I've got a variable called 'student name', and what I'm doing here is using the equals symbol to say, store the string 'Adam' in the variable called 'student name', and then that will be stored there for later.
And then I can do operations on this variable that are appropriate to the thing in the box.
So I might do string operations on it. What kind of operations might we want to do?
Well, we might want to print 'Hello'- whatever that is stored in that 'student name'. If I go back to the piece of the code that I've got over here, I've got some other bits here going through the different types, which again, you can kind of have a look at in your own time.
Some basic operations. So here we're talking about the fact that we could join strings together using the + symbol.
So we're using that within what I had on the slide as well.
So here, we are joining together two string values 'Hello' and 'World' to make a single string.
'Hello world'. We've got operations that we can do on integers and floats. Kind of fairly obvious mathematical ones.
And again, you start to kind of see the kind of notation that we might use here.
We use the * for times, and the / for divide.
A kind of important one that you'll need to get to used to seeing, we're not really talking about it now, is this double = to test if two things are the same.
So that's telling me, yes, 5x4 is the same as 2x10.
I've even got some exercises for you in there. The one that I was talking about here is the fact that we've got 'student name' there.
You can print it out. I don't want to do all of that now.
A bit that I wanted you to think about is, what do you think the output would be for this piece of code?
So when we had 'print' and then we had 'Hello'+'Student name', what's going to happen there?
And well, if you don't know, you can press run on that cell.
But what you should see is 'Hello Adam'. And it's whatever we've stored in 'student name'. If I add in another cell here - let's move that up because it's before - and I change what student name is, so say actually student name is now saying Julie.
Now, if I run that same piece of code, it will say 'Hello Julie'.
I can change it again. Whatever is in the box is what I print 'Hello' to. So when we come back to my slides, here, we should see that when we've done that, we get the 'Hello Adam' because the student name was Adam at the time.
In the kind of first lecture we would have, and as you're learning Python, you're going to learn about a lot more complex data structures that we don't have time to talk all about in the next ten minutes.
I was going to just do a very brief introduction to lists. Just as a kind of harder data structure that you'd be learning about, quite, very early on on the course, and this is how we group together our basic data types to make some kind of collection of data types.
So within that notebook, if you keep working through it, it takes you through some basic list functionality.
First of all, what is a list? It's an ordered collection of other data types, which can vary in length, and we use them a lot in natural language processing because we have a lot of sequences of textual objects, characters, words, sentences, paragraphs, page documents. There's always an order. And so a list makes a sense. If I want a sentence, I can think of it as a list of words.
If I want a document, I can think of it as a list of sentences, as if I want a whole document collection, well, that's a list of documents.
And quite often, we want to do the same thing to everything in the collection, and that's what's where lists come in really useful.
So here I've just got some code going through how we would set up a list. And see, lists of things which come in square brackets.
So if I want the list of numbers 2, 3, 5, 7, 11, I separate them by commas with square brackets at the start and the end to say, this is a list. I'm then storing this list in a variable I've called 'Prines'. But remember. I've called it 'Primes' because I wanted to remember that they're prime numbers, but I could've called it anything. I could have called it 'monkeys', I could have called it 'cheese', I could have called it absolutely anything I wanted, but to help me with my programming, I've called it 'Primes' to help me remember that's what I'm storing in there. And we can store anything in a list. I've got numbers here, we can have strings, we could have a mixture of numbers and strings. That would all be fine in Python.
It's important to note that the order in a list matters. We do have other data types where order doesn't matter, such as sets.
You'll learn more about those next year. But here, a list which is 2, 3, 5, 7, 11 is different to a list which is 3, 7, 11, 5, 2.
What kind of things might you do with lists? We're going to talk a little bit about indexing and iteration.
So first of all, list indexing. We want to get an item out of a list at a particular position.
So that is what we refer to as the the list index. A thing to remember in computer science is that we always start there, right?
So the first thing in your list is not in position one. It's in position zero.
It's just one of those things that computer scientists do, they start counting at zero, so the first item in the list is index zero.
So if I want the first item in my list of primes, I would say 'primes', and then to index into it I use the square brackets again, say primes
And if I run that piece of code in the notebook, I would get back 2. Because that is what zero is pointing at in my list. If I want one of the others, if I want the second item, I would say primes.
If I want the third item, I say primes, etc. all the way until the end. And you can have a little play with that in the notebook.
Also, some other interesting things we could do with list indexing; we could actually index from the other end of the list as well. So if I might not know how long the list is or I might not care how the list is, I just want the last thing on the list, I can just say primes[-1]. That gives me the last thing. I can count backwards through the list as well, so I can have the third-from-end thing, that's primes[-3].
So that would give me five when I run that bit of code there. So that's indexing. My last thing I wanted to mention to hopefully not have blown your heads too much in terms of introducing you to Python coding, is something which starts to make coding become more useful.
It's this idea of being able to iterate over a list. We take each thing in a list, do the same thing to it. So I don't have to give an instruction every time, I can just say to the computer, for everything that's in this list, do this operation.
And that's what a 'for' loop, which is the most simple kind of list iteration there is, Will do for you. Here what I'm saying is I have a list, and this would be any list variable I could put here, so this is the one that I want to iterate over. Do the same thing to everything in it.
So what I'm saying is 'for prime in primes'. See this again could be called anything.
'Item in primes' might be a good way of referring to that.
I'm going to take each one of those items and then I'm going to go through this looping process where the first time, prime is the first thing, that's index 0.
Then it's at index 1, then it's at index 2. I don't have to tell it to increase that index point.
I can just tell Python I want to do the same thing for everything in the list.
And the thing that I want to do goes in the body of the loop. What I want to do is print.
I want to print that number out. I know it's a number.
We don't actually. It might be anything. It could be another string. And then we're going to print 'is a prime' as well.
It's a very, very simple iteration. What we can see is that these two lines of code allow me to, however long that list, this list could have hundreds of numbers in it, would print a line for every single item in the list, which starts with the item in the list and then 'is a prime'. As we do more programming you'll learn more interesting things that you might actually want to do to those items in the list.
OK. That was my kind of mini introduction to Python and to applied natural language processes. I'll put a few words at the end of that mini introductory lecture there as well. You might want to sort of look at and think, did I pick up on that as a kind of keyword that might be useful in programming? And think about what they mean.
If you've got any questions for me, we've got about three minutes before we have to go back and join the other session for the the general Q&A.
I can see there's one question on the Q&A. This is an interesting question coming in. It says what is the background of students that are normally on this stream and what type of roles would they go into?
So typically we actually, again, we have a wide range of students.
What we imagine is that most of the students coming into this will have a background in business or in humanities and social sciences.
So that's the typical background.
But actually, we do have students who actually convert the other way as well, who kind of actually have a background in computer science.
But they're really interested in the human and social side of data science, and they want to work in essentially in business or in policy, and therefore they want to learn more about innovation and policy.
And so they take this degree as well. Typically our students might go on and work, and again, we will talk to Emily, who's one of our alumni of this course shortly, and we've got another student coming in.
I mean, typically you would imagine that you might go on to work in policy, in government, or in a company that's dealing with kind of human and social aspects. We had a number of students, they were probably a mixture of human and social data science and data science students, but that's partly because we didn't have many human and social data science students, I know whenever I talk to Brandwatch they're always really interested. They've massively supported this as a course that we deliver, saying that they find it easier to train up people in the technical aspects, but find it hard to train up people in the domain aspect.
The fact that we are taking students into this degree who have more knowledge about the kind of social science aspects of data in the real world.
And then we can guide their learning to include programming and more statistics on their course that they can carry on to do this at a company such as Brandwatch. So I know very much that, you know, Brandwatch are very keen to look at our graduates of this course.
So I hope that answers that question. How much experience you need for data science is doing with social sciences, e.g., psychology.
It's hard to say how much experience. I think everybody comes in with different experience.
You don't need a certain amount of psychology or a certain amount of business.
It is the fact that people are coming from different backgrounds, but you may well find that your background and your experience may then influence which optional modules that you choose and also what project that you choose to do and who you choose to work on your project with.
Because when you do your project, you want to obviously showcase your skills.
And if you can do a project which brings together skills that you already had before you came into the university, and also the skills you've learnt on the course, that's where, you know, the best projects come from.
So I'm not sure I've completely answered that question, but I've answered that question the best that I can. Question from Geraldine: how many people are typically on the course? This year we've had 24 or 25 students on the course, I believe, and certainly be looking to maintain that kind of number next year, maybe a few more, depending who applies.
But yeah, I would be thinking 20 to 30 students in the course.
Any other questions, because I believe we should be rejoining? OK.
Isobel's put the link back. So let's finish there.
If you have got more questions, we can put all those into the general Q&A as well.
So please keep hold of those questions and ask them in a moment. OK.
See you in a moment.
Subject and course videos
- Advanced Computer Science MSc
- Artificial Intelligence and Adaptive Systems MSc
- Computing with Digital Media MSc
- Management of Information Technology MSc