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 about the courses 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 from 2022 focusing on our Masters courses in Data Science, Human and Social Data Science and Artificial Intelligence and Robotic Systems.
- Webinar transcript
Hi everyone, I can see people are joining the webinar. We're just going to wait a minute or two before we do introductions. Just bear with us while people get into the webinar. As you're joining, it'd be really nice if you could type in the chat box - let us know that you can see and hear us.
Okay. So do type in the chat box and let us know that's all working. And it's also really nice to know where people are listening from. So if you're in the UK, maybe just down the road from the campus in Brighton, or you could be anywhere in the world. So it's lovely to know. So do type in and also do type in if you've applied for a course with us or if you're just in the process of checking out the University of Sussex as a potential option or if you've already applied, do type that in. No one's typing yet - I'm going to assume that people can see and hear us. We'll just give it another 30 seconds or so and then we'll do the instructions. Oh, Lorraine's in Zimbabwe, applied and accepted. Fantastic. Anthony can see and hear.
That's good because we won't be talking to ourselves for an hour. Okay. I think we'll start the session now. I'll do a brief introduction of who I am and I'll hand over to main speaker today, Dr. Julie Weeds. My name is Ben Osborne and I'm the Postgraduate Manager at the University of Sussex, so I help organise lots of events such as this, specific subject sessions, and also lots of general post-grad themed sessions as well, both online and on campus.
And today's session is a specific subject area session. So we're gonna be talking about data science and AI. So a very exciting and timely topic. And we're joined by Dr. Julie Weeds, who's going to introduce herself shortly, and we've got a couple of students who are currently on Data Science or Artificial Intelligence courses, Rachael and Swati. And how today's session will work is I'll hand over to Julie for introductions, and then Julie will take us through her presentation, and then we'll come back to our current students who are going to talk about their experiences, and then we'll do questions.
So if you do have questions, please do type them in the Q&A as we're going through and we'll come back to them at the end. So don't worry if your questions don't get answered during the presentation, we'll definitely come back to them at the end, we'll leave some time for that. So do type them as we're going. But anyway, that's enough from me. I'm going to hand over to Julie now. So, Julie, over to you.
Thanks, Ben. Hi, everybody. Welcome today. Just to say a little bit about the team here at Sussex in the data science and artificial intelligence courses who you might be discussing with either today or generally in your application. So there's me.
I'm Dr. Julie Weeds. I Co-convene the Human and Social Data Science MSc and the Data Science MSs, along with Dr. Omar Lakkis, who isn't here today, but he's the other convener who you may also have correspondence with. And we co-convene, because Omar is in the Math and Physical Sciences school and I'm in the Engineering and Informatics school, and these are degrees which bridge both of the schools so it makes sense to have conveners from both of those schools. We also have on the team Professor Ian Mackie, who's the convener for Artificial Intelligence and Adaptive Systems MSc. Today, the students that we've got with us to talk to you later about their experiences are Swati, we've got Rachael, and I believe we've got Mohammed has just joined us as well. We'll be going to them at the end of the session and and they will talk to you a little bit about their experiences on the course.
Ben's introduced himself as well as for postgraduate recruitment, and there's also Dilys who's been involved in the marketing of this event. So that's us. As Ben's already said, it's always good to know about the audience, it's nice to know who we're talking to. So if you haven't already, please do tell us a little bit about yourself in the chat - and that can include the postgraduate degree that you have applied for, or are applying for; a little bit about maybe where you're from or what your background is. I can see that lots of people have already been putting things in the chat, and we've got lots of international people here today. I can see people from Zimbabwe, India, Iran, Bangladesh, Thailand and Argentina. So lots of people from all over the world are here today. So thank you very much for joining us.
What I'm going to talk to you a little bit about, first of all, is data science and AI, and then I'll talk to you about the degrees on offer at Sussex. Some of this you may already know if you've already applied and been accepted. But I think, again, it may help to go through some of the differences between the different degrees and obviously answer any questions that you have at the end.
So first of all, what is data science? So I've got a few quotes on the screen here. Essentially, it's the science of big data where we've got either lots of data or messy data that we want to try to extract some insights from. So that's data science, but what does that really translate to? What do data scientists actually do?
Well, a data scientist typically would try to extract meaning from and interpret data. This involves lots of other things and therefore skills that need to be learned and developed whilst you're studying. So you'll need to be able to collect, clean, and munge data. That means get it into a form that you can actually work with. You need to be to explore that data, see what's there.
And that quite often is iterative in terms of having to explore the data. Maybe you need to go back and collect clean and munge again. You then need to find patterns, build models and algorithms, designing experiments and ultimately making decisions and communicating with other team members, engineers and leadership. So on the Data Science masters, these will be the kind of skills that you will be developing.
And those skills lie at the intersection of mathematics, computer science and the domain. So this is where we see data science, sitting right in the middle of those three areas. And as I said earlier, the course at Sussex is organised by both the Mathematics department and the Computer Science department. So it's a collaboration between those two departments at the University.
And then we also bring in the domain expertise, and that's particularly relevant when we come to talk about Human and Social Data Science. Just to say as well that the domain can be anything these days. Maybe in the early days, data science was typically associated with physics and life sciences, where there'd be lots of data in particle physics or lots of data in genetics.
But now big data is absolutely everywhere. I first did this slide when I first did this this talk back in 2019 with a slide saying 'What happens in an internet minute?' How much data is generated every minute on the internet. That was back in 2019. So I thought, well actually it's 2023 now.
Time has moved on. Let's see what's happening now in an internet minute. How has that changed? And so I've got another slide here. Produced from a different source, so slightly different, but we can see that there's even more things going on in 2023 every minutes on the internet. When we look at things like the number of emails sent, the number of text messages sent, those have all actually increased.
Although we now do probably see other sources of data on the internet in terms of people visiting ChatGBT, that certainly wasn't something that we were talking about last year and certainly over the past few years. The number of things such as Zoom meetings have increased during the pandemic and certainly Zoom meetings and other kind of online meetings are still very much a thing.
But in general, there's a lot of data out there that's in a lot of different forms, much of it textual, much of it unstructured, as well as the typical structured data one might typically work with on a say statistics course. So that is what we're thinking about when we're thinking about data science. We also offer the MSc in Artificial Intelligence.
So I'm going to say a little bit about, what is Artificial Intelligence. Again, there are many, many different definitions. Ones that I like I've put here, which kind of go with the Sussex AI philosophy of artificial intelligence. It's the ability to be able to reason, discover meaning, generalize or learn from past experience. Or we might really think about the fact it's a computer that can perform, a robot that can perform tasks which are commonly associated with intelligent beings such as humans. This does mean that AI is a lot more than machine learning.
A lot of people, especially at the moment, as soon as you say artificial intelligence, they think of machine learning. That's what's very big in the media and has been for for some years. Machine learning very much falls within artificial intelligence. And you will learn about machine learning on any of our degrees. But there are other things which may be overlapped with machine learning, such as NLP, but are not just machine learning.
And there are other aspects of Artificial Intelligence which go much beyond machine learning as well. And they are things which, again, you can study here at Sussex. The AI degree does allow you to customize what kind of modules you do beyond the machine learning. And I'll talk a little bit about those differences shortly.
So that's my kind of general, what's data science, what's AI, what do we actually offer at Sussex, what have you been applying for? Just so that you're all aware of the different options because it is actually possible to essentially change between some of these degrees. So we do have our MSc in Artificial Intelligence and Adaptive Systems which can be studied with or without an industrial placement.
You can study full time or part time. We also have the Data Science MScs which can be studied with or without an industrial placement, full time or part time. And then we also have the Human and Social Data Science degree, which can be studied full time or part time. Of course, the best place to go for current information on these degrees is to go to our website and check the different options available and the expectations on those degrees. So what is the MSc in Artificial Intelligence Adaptive Systems?
This is a degree which has in some form been taught at Sussex since the 1990s. It's been going a long time in various different guises, and started as I think two degrees, one in Intelligent Systems and one in Evolutionary and Adaptive Systems. And over the years both of these have kind of evolved and combined together to what we now offer, which is the MSc in Artificial Intelligence and Adaptive Systems.
Sussex has a very long history of research in this area and teaching in this area. Students on this degree tend to follow a course of study in one of these kind of strands, and there are modules in your options that are available from these kind of different areas of artificial intelligence.
So see here that there is the kind of data science / machine learning / natural language processing strand of AI, which is possibly the one which most people associate with artificial intelligence at the moment. But there's also options and we have a strong tradition at Sussex in the areas of computational biology and consciousness science within robotics and autonomous systems, and also within the artificial intelligence and cognitive modelling area of artificial intelligence. Most students coming on to this do have a background in a scientific or technical subjects, but not all of them. So again, if you don't have a background in that and you want to apply, then certainly contact the admissions team or Ian directly to discuss your background. Maybe you've already done this and have applied and been successful in applying to the course. Our MSc in Data Science was first delivered in 2016, so we've been going for six years coming into our seventh year.
Now typically students have come to this course with backgrounds in mathematics, physics, computer science and life science. And we have modules with options within some of those areas so people can continue to develop the domain expertise in the life sciences or within, say, physics, computer science or maths; but combine that with their data science learning in the core modules which go across the course.
And since 2020, so we're now going into our fourth year, we've been delivering this degree in Human and Social Data Science, which is aimed at students with backgrounds in business, humanities, social sciences, politics; and we have students pretty much from any background you can think of where there may be some kind of interesting data that people want to explore and analyse.
And more than we were teaching these students the kind of data skills that they need to be able to work in data science in these areas. We currently have strands and module options in digital media and innovation and policy. We are hoping to add very soon, although it's not advertised yet, but we are looking at being able to add modules on corruption and governance from our politics departments.
So if that's something which interests you, it may be something which becomes possible in the very near future. I mentioned that there's a possibility of an industrial placement year. This is something which can be taken with either of the MScs in Artificial Intelligence and Adaptive Systems or Data Science. The way it works if you're doing an industrial placement year is that in year one you typically do all of your taught modules including your dissertation, and then in year two is your industrial placement. There is some flexibility n terms of when your placement starts. Some students may start their placement in June of the first year, do a year's placement, and then come back and do their dissertation. Or students may start their placement later in September having completed their dissertation.
So the dissertation is actually usually assessed in year two, but is certainly something which you would start the process of doing in year one, whether you're doing an industrial placement or not.
That industrial placement that you do in year two is assessed. It's a kind of pass / fail assessment; you need to give a presentation and write a report about what you have learned during your industrial placement. It would be interesting potentially to know if you are thinking about doing an industrial placement, then that's something else which you can put into the chat, or whether you've applied for the industrial placement option.
We like to point out now, and I will say this again once you start next year, it is your responsibility to secure the placement. We do not give placements to students. We can't do that because it's a company's choice as to whether they take a particular student on a placement or not. We will help you in terms of thinking about where to apply for placements, but we do not assign students to placements.
So it's the same in terms of kind of graduate recruitment. You will need to be proactive and start the process quite early on in year one in terms of looking for and applying for placements, but it is a very beneficial thing to do in terms of getting that kind of extra experience before you have actually graduated from university.
The other thing to say about the industrial placement year is it is very easy to switch between the two degrees. So if you apply for the MSc Data Science and then halfway through the first year you get the opportunity to take a placement with a company, you can switch on to the placement year version of the course. Similarly, if you have applied for the placement year and then during the first year you decide that you don't want to do the placement or you're unable to secure a placement in time for your second year, you would be straightforwardly transferred on to the single year of degree and you would then be able to graduate with your MSc in Data Science. So it's not that you would then fail the degree because you didn't do the placement. You would just transfer onto the option without the placement year. Now, I have said that we're going to come back to questions later, but I have seen one in the chat which I think is very relevant to what I'm saying at the moment.
So I will mention that now the placement year version of the course is slightly more expensive than the non placement year option. I think it's 20% on top of the the normal fees that you would be paying for the one year course and therefore if you transfer onto the course, you would then have to pay the additional fee for that version of the course.
And Ben's just popped a link in the chat where you can find out information about fees for the different different courses. Okay, so that industrial placement years. Let's think a little bit about how the MSc actually works. First of all, I'm going to think about this for the full time perspective and then I will say how that might vary if you were part time.
So essentially the Sussex year's split into two semesters and then a kind of summer period. During the autumn and the spring semester is when you do all of your taught modules, and then in the summer - and that goes from say June through to the end of August - you will be working on your dissertation project. In the autumn,
typically on our MScs, in each semester students do four modules. On Artificial Intelligence and Adaptive Systems in the autumn there's one core module which all students on this course do, and then they select three options from the list of available options. On Data Science and Human and Social Data Science, each of those courses actually have three core modules which all students do, which has some overlap between those two degrees.
I will talk more about what they are shortly, but they're not exactly the same. And then students pick one option, which is usually tied to a strand. The strand is something which is not enforced, but most students come in with an interest, say, in physics, or within life sciences.
And therefore we have organised the options into, not blocks as such, but into these strands. We think, well, if you are a life sciences person, these are the kind of options which would be good to choose from. But again, there is flexibility and you can pick options from other strands to customise your degree, but it would normally not make sense to sort of pick one option in kind of particle physics and one option in life sciences or something like that in the in the spring term.
Normally it makes more sense to kind of group your options according to what you are interested in, what your background is and what you want to do once you've graduated. So anyway, we have these three core modules and one option in the autumn, then in the spring, there are two core modules and two options for the AI and Adaptive Systems course. Data Science and hHuman and Social Data Science actually have five modules in the spring, three core modules and two strand specific options.
And this is because on these courses students start working on their dissertation project essentially in the spring developing a research proposal so they get some credit towards that in the spring. And then the dissertation project itself in the summer is worth less credits than it is on the AI and Adaptive Systems course. I think for all of the modules, it's 180 credits that you get to be able to graduate from the degree.
And so typically it's 60 credits in each of these periods, but in these courses you get 75 credits here and then 45 credits for the dissertation project. Okay, so that's kind of how the year will work. Just to sat that all of the five MScs can be studied part time, which makes - if you're not doing the industrial placement year - this into a two year programme where you accumulate approximately half the module credits each year for the taught courses and then your dissertation is carried out over the two consecutive summers.
There is some flexibility again here, depending on what your part time restrictions are, how exactly you do this. That would be something which you would talk to your course convener about. But this would be what we would normally expect. If you do the industrial placement year, this could make the program into three or four years.
We would typically expect you to do the taught modules over two years and probably the industrial year itself would need to be done as one full time year. But again, it might be that the particular company that you want to work on placement with is quite happy for you to work part time and therefore it would become a two year part time placement as well.
So it could become a four year program. But again, that would depend on the placement company whether they could accept you as a part time placements student. Okay, I see some more questions coming in which look good. I'm going to try and come back to those I think at the end. So I'll press on with what I'm going to say and then we will come back to some of those questions at the end. Let's just move on to a little bit about the modules that you will be studying. I've talked a little bit about core modules and option modules, the core modules are things that everybody on the course will study. And actually what you'll find is that some of them are core across all three of the courses. So you will see in some of these modules students from all of the three different courses that we're talking to you today. This includes in the autumn term the fact that all students do mathematics and computational methods for complex systems although in some cases this may be replaced by data analysis techniques for data science students if they have a very strong background in math or physics.
But we would normally recommend that all students take the mathematics and computational methods of complex systems module in order to ensure that you have the sufficient mathematical and computational background to be able to go on and do modules such as machine learning in the spring term. Other core modules for Data Science and Human and Social Data Science include data science research methods.
The Data Scientists do algorithmic data science and Human and Social Data Science do systems for information management. All these modules provide computer science background that's needed to do data science, particularly across these two modules. And then the data science research methods is starting to look at datasets and being able to work in the Python programing language using libraries to be able to work with these datasets and start to carry out exploratory data analysis and modeling of those types of those datasets.
Then in the spring there's machine learning for all of the courses, and then artificial intelligence and adaptive systems is also a core module that all students have to do in adaptive systems. And then the data science and human social data science students look at why topics in data science, which includes things like ethics in data science, and they also write their research proposal for their dissertation.
I mentioned Python: all the programing across all of these courses is typically in the Python programing language. We don't expect you to necessarily know how to program before you come, but some of the modules that you will be studying in that first year will build on your Python programming. So they won't expect you to be a proficient Python programmer, but they will expect you to be learning to program in Python if you haven't already.
We do have an optional module for programing the Python, which will be done in the autumn term, I think on any of the courses. So if you haven't done any programing in Python before, this is potentially good module to do. And we do also offer a free online course in programing in Python in September, which again, if you haven't done any programing before, is a really good thing to do.
Just so you can start a little bit of programing before you actually arrive for your course at the end of September, beginning of October. This we will let you all know about once you register on the course. We can register you on the virtual learning environment and you can start working on the material and the exercises that are on there.
And that's usually from around the beginning of September. We did this for the last year, and I think this just sort of helps particularly for some of our Human and Social Data Science students who were coming in and finding it hard to get up to speed in Python at quite the speed that they wanted to during that first term, even if they were studying programing through Python.
So that's why we would encourage anybody that doesn't have much experience in Python programing to do that pre-sessional extra training. Okay, so there're questions coming in - besides Python, PyTorch may well come up on certain modules. I'm not 100% sure. Certainly something which would get I think at least mentioned in machine learning and natural language processing courses.
SQL certainly comes up in algorithmic data science and also in systems for information management. So both of those modules do cover some SQL as well.
So yes, those two examples that you've put there, typically we focus on that kind of as a programing language, the single programing language of Python. So there's no expectations necessarily learn other programing languages. But I think that is one of the optional modules the second term does use R, so again, that might be something which you could learn as well, particularly if you have already competent Python programing, you're looking to extend the kind of range of programing languages.
And I also know that there is in the autumn term, there is a programing in C++ module which again, if you can already program in Python, this might be an option that you'd want to take to learn another language as well. I wouldn't personally recommend that for somebody who hasn't done much programing before because it is hard to learn two programing languages at the same time.
So if you are coming on to the course with no programming experience or very early programming experience, I would not take those options. I'd take the options to really build up the Python programing. But if you have got programing experience there are options, as I said, to do other kind of programming languages as well.
And so talking about the options, I think I can see some questions coming in about options as well. These are the kind of options that are typically offered on data science. Occasionally we add extra ones to these. So it may be that there will be other ones available in the autumn, but these are the ones that definitely will be there and the kind of ones that we would suggest.
I've kind of organized them here, according to your background that you have, things which you might find interesting to do, but obviously you might - even with a computer science background, you might say, well, I've done natural language processing before and I don't want to do that. Actually, I'd quite fancy programing C++, that's fine too. In terms of modules that are not officially offered on the course, it is possible to take modules from other courses via something called variation of study. We'll talk more to you about this at induction, so if there is something you really, really want to study that's not officially offered on the degree, you could do 30 credits of variation of study, which is essentially 15 credits per term, but there's no guarantee that you will be able to do the particular module that you choose in the sense that it might not fit in your timetables.
It might clash with one of your core modules, so it might not be possible, and it would have to be assessed to the point of view of whether it actually fits with your timetable and whether the course convener and the module conveners think that this is an appropriate module for you to be taking. And it may be because you've already done some of these other options, it may be because something is really important for the kind of work you want to do. And we would say yes, that sounds really great that you do that module. So we try to be as accommodating as possible and listen to what you want to do and make your degree. But we can't accommodate everything in terms of the timetable so even sometimes some of the options that are offered on the course, sometimes two options won't actually fit together.
So students do have to take other options to their first choices, or maybe an option in some cases gets full up in terms of the number of students on it. So we can't guarantee even some of the the actual options on the course we certainly can't guarantee the options which are not officially offered on the course, but we make every effort to to make these ones available to our students and find ways of making those possible.
And yeah, we look for ways of making other things possible as well if you've got good reason to do other modules. Some of the other option modules, again, students doing Human and Social Data Science take options from the Media, Arts and Humanities school or from the Business school. And these are typically the modules that we offer to those students.
So modules in policy making, artificial intelligence and policy, industrial innovation policy from the business school. And then we have various options on the digital media strand, including digital journalism, developments in digital media, media law and ethics and so on. Again, if there are good reasons why you kind of think there's another module from one of those schools or elsewhere that you would really like to study, then the best thing to do is talk to your course convener during induction or even before induction in September and we will look at trying to make that happen.
Okay. So time is ticking, isn't it? So I'm almost towards the end of what I've got to say here. We try to partner with industry as much as possible to give you the kind of experience that you need for going on beyond academia and working in industry after graduation.
And so we do have the industrial placement year, which our first first year of that was two years ago now.
Possibilities of maybe taking internships over the summer where you defer your dissertation. We offer mentoring and there are industry networking events. We put on hackathons and talks and that's all via this research centre we have in Sussex called DISCUS, lots more about that at the start of next year so I won't talk too much about that now.
Some of the industrial partners that we've worked with in the past I've listed here, and some of the partners where we've had students go to do placements or go on and work at after graduation. I did just mention DISCUS so that's the kind of the organisation within Sussex in which we try to bring together students, researchers from across the University with an interest in data science.
So lots of these events that I mentioned are actually organised by DISCUS and one of the things which I think is particularly of interest to MSc students is that we run mentoring from industrial partners and alumni. We've been doing this the last three years, and approximately each year we've made around 30 one-to-one matches between our students and industry mentors.
For this mentoring scheme we do prioritise our scholarship students. I'll talk about this on the next few slides. But it is open to other students as well. It's something you have to apply for and we can't necessarily find mentors for every single student so there is a kind of slightly competitive process but we try our best to find as many mentors as we can for students who would like to have a industry mentor.
And yeah, we have many mentors working in a range of different companies. Many of them are alumni for the course who've gone on to work at some of these companies, and then are sort of coming back to give back to the the new generation that are coming through. I do want to mention that we do we are offering again, scholarships from the UK Office for Students for students in minority groups, and this is seen as a key priority by the government to increase the amount of diversity in entrants to this field.
I do so need to say about that, that as of this year, this is strictly only available for applicants who are classified as Home. I've seen that. I know we've got a lot of international students on the on this webinar. We can only give these scholarships to home students. But if you are a Home student we do have 25 scholarships of £10,000 each to award. That says 2022 but should say 2023 - that's this year. All of you in the sense that you are applying for one of our conversion courses in AI and Data Science would be eligible unless you've done an AI or data science degree before.
The other eligibility requirement is that you are from an underrepresented group. The three priority groups for these scholarships are female, black and registered disabled. Although there are some other minority groups which we can also consider if we don't have enough students applying for these underrepresented groups. There's more information about that on our website.
The deadline to apply is the 1st of August. I believe that our admissions team have been emailing out to people who have applied for the course who might be eligible, to let them know about it. It's a really simple application process. We just kind of need to know that you're eligible and you have to write a brief statement about your motivation and what the scholarship will mean, which we will use to kind of break ties in terms of deciding who gets the scholarships if we have more than 25 people applying.
But I do have to say that this year is strictly only Home students. In the past we have been able to award to international students if we did not have enough Home students applying this year the rules have changed. But there are other scholarships that are available to both Home students and international students. So it's certainly worth checking out the course web page to find out what other scholarships you could apply for and putting in your application for.
So that is scholarships. How are we doing for time? I think I'm probably running out of time here. Okay, we definitely want to get to our student panel so what I'm going to do is probably just skip through the slides where I've got some past students and I'm not going to talk about those.
I'll hand over to our current students to talk about their experiences. So I want to stop sharing my screen and then I'm going to ask Swati, Rachael and Mohammed to introduce themselves, say a little bit about the degree you're on, where you came from, how it's going, and maybe what you would have really liked to have known this time last year, which you didn't know in terms of what it would be like to be studying this degree at Sussex.
So I'm, going to stop sharing and hand over to you. Let's, let's start with Swati.
Hi everyone, good afternoon. So my name is Swati and I'm really happy to share my experiences here. So little about myself. I'm currently pursuing a one year full time degree masters in Data Science. So I have had a couple years of experience working in IT as a developer, but I got exposure to machine learning and I really found myself interested in the domain of data science.
So while I was researching Sussex, I applied to Sussex and I got a scholarship of £3,000 offered to all the Indian students. And there were also a couple of other scholarships, but I applied a little bit late for that. So I would really recommend students to apply for scholarships on time. So, Sussex has quite good and large campus and it was like very supportive.
Thanks to the students. Whatyou might need in day to day life, which is a really great library and the scholarships and everything else is like as professors are like quite supportive. You know, one thing I would really recommend students is to research about the optional modules beforehand and especially about the Python programing if they could, you know, gain a little bit experience in that so that it would really help them in a whole journey of just like one year.
And it has been a great journey throughout this year.
Thank you, Swati. Okay, I'm going to pass over to Rachael to introduce yourself, please.
Hi, I'm Rachael. And I'm currently on the Human and Social Data Science course full time. And so my background is psychology mostly. So I did an undergraduate in psychology and I graduated in 2017, and then I did five years working in mental health and support and then fancied a change so found Sussex and the Human and Social Data Science degree.
Which was right up my street. And yeah, it's been really good. I didn't have any programing experience at all beforehand. And over the summer I did the pre-sessional introduction to Python course that Julie was talking about and that was really helpful because it made me feel a bit more comfortable going into the modules where you are programming from the beginning and I had a bit of a foundation to build on and I wasn't all new and panicked.
And one thing I wish I'd known is probably for me, I don't have the strongest math background and I was sat in this webinar last year and the person on the panel said, oh, go back over your basic mathematics and I thought, oh, I started googling data science mathematics but that was not what I needed because you get taught all of the data science mathematics on the course, but going back over just your foundations, your linear algebra, just the real basics actually helps loads.
So the first math module one semester. But yeah, that was well-supported as well. So yeah.
Thank you. Brilliant. Thank you, Rachael. Very helpful advice. And over to Mohammed, would you like to introduce yourself, please?
Hello, this is Mohammed and I came from Syria on a scholarship to Sussex University. And my scholarship was dedicated for those students who was coming from the Middle East.
I have a background in engineering, and then I joined the United Nations for like five years before coming here to Sussex. And, and now I'm working as senior data analyst in Sussex University in the Student Experience Department. So I would say that despite like having these hours, long hours of writing or just like searching on some data science aspects, it's also very good to keep an eye on the vacancies around Brighton.
There are like a lot of vacancies, job vacancies and they require some kind of skills. So alongside with the taking your modules it's so important as well to adapt yourself into the job market skills starting as basic, as simple as data management through Excel data visualization, like, you know, try to apply the things that you learned into real time situations.
Like, for example, now it's so easy to just think about some problem that is around you and you just like how you figure out how data science would help in solving these problems. I have a lot of friends who are with me on this course of data science. They have like experience in each are financing accounting and they use data science solutions to to get there.
They are like their skills to the next step. So this is something I'd really like to recommend for those who are coming through to data science next year. I would also say that I enjoy the optional modules a lot. I had the opportunity to learn a lot about image processing. Also the web applications, so it is also giving you some push towards some other domains rather than like staying as a as a like that analysis or data science.
So this is a really important thing to take into consideration and to select your optional modules carefully. So as you like to, to push yourself into some new skills.
Thank you. Brilliant. Thank you, Mohammed. Okay. I realise that we're close to the end. I will stay and answer as many questions as I can over the next sort of 10 to 15 minutes.
Obviously, if people do need to leave, then please do leave. You've got to be elsewhere. And I believe the recording will be available afterwards, as will the slides that I shared earlier so if you do need to leave, I don't feel bad about that. But let me see if I can answer some of those questions.
Now, some of them, I think have already been answered, so I'm going to go through and see what I can answer. So Nowsheen said that she's interested to know about the alumni profiles, especially about jobs they're doing after graduating from the MSc Human and Social Data Science, also the networking opportunities. Of the profiles I had on my slides that I didn't really go through, there was one for the Human and Social Data Science course a couple of years ago.
Emily, she was actually here in this webinar, I think last year as one of our recently graduated students now working, she actually works at IBM. Let me just bring that up on the screen now. So yeah, she previously studied psychology and she did her dissertation on psychological distress modelling and predicting mental health outcomes and is working as an Associate Technology Engineer Data and AI at IBM. Many others of our Human and Social Data Science students go on and work within government organisations, local authorities. I believe, Rachael, you were saying that you're applying to the ONS so that's certainly something which our Human and Social Data Science students would be thinking about. But I think there is a very wide variety of sort of different areas that people will kind of go on and take that into potentially into kind of journalism.
Again, I think it depends on what your background is before coming in as well.
Networking opportunities. This year we will have two. We try to write one at the beginning of the year and the end of the year, networking opportunities where we get students together with industry partners, both local and potentially some national ones as well, come together for a kind of networking event where the companies present on what it's like to work with and then there's networking opportunities to to speak to to people from those companies and there may well be other events as well organised throughout the year in terms of kind of hack events and things like that, which would also involve industry partners as well as students I think that kind of answers that question there. A good question about masters projects - it's a mix, I would say, in terms of whether you come up with your masters project or whether it's kind of suggested by a potential supervisor. It is definitely your responsibility, I would say. But, you know, there are suggested projects and lists of kind of projects that students have done previously, projects which supervisors are suggesting.
But then as well, many students come even before they start and they've kind of got an idea exactly what they want to do for their project. And the best thing there is to try to identify a supervisor kind of in that area and try to develop that as a project idea together. So I think there's a hybrid between the sort of the two to kind of options that you've suggested there.
And you're also asking about exploring large language models. Yes, I teach advanced natural language processing and we do explore large language models on advanced natural language processing, which is an optional module in the spring term for both the Data Science course and the Artificial Intelligence and Adaptive Systems course.
If you don't get a placement, then you would transfer to the one year version and you would need to complete your dissertation in the summer and then you would complete within one year so I hope that answers that question.
How do you choose your optional modules? You choose them at the beginning of the course during induction. I would actually suggest choosing them as soon as possible. I think one of our students suggested this as well. Do your research early, think about what you would like to study. There will be some information coming out over the summer about what option modules are available, but once you're registered, you can actually log on to the Sussex Direct system and actually fill in a full kind of online form saying you select your options.
It does, I think, even kind of show you how many spaces are left on the module. So the longer you leave it, there is a possibility that the option modules that you want will be full up. So it is worth doing that as soon as possible. After you have registered many of those modules. If we see that they are oversubscribed, we can start to kind of work out ways to offer extra kind of places on those modules, but it's not always possible on all modules.
So again, that is something which the sooner we know what optional modules people want to do, the easier it is to kind of sort out any kind of problems with too many students for a particular class. How many you actually choose? Depends exactly on the course as to how many are core and how many are optional. But usually between one and three on each module.
And yes, you would normally do that at the beginning of the autumn term for the whole year, but you can change your mind so up to the end of the second week in the term of the the module has been studied, you can actually switch to another module provided there is space in that module. So again, thinking about your spring term modules, it may be that during the autumn term you kind of change your mind about what you want to do the spring based on what you've learned in the autumn term.
And it's very common for students to sort of request changes and that's absolutely fine. But again, we need to make sure there's enough enough spaces for students to be able to to do those modules. Background in a specific area such as computer science, can I still focus my studies in another area, such as physics? Is an interesting question, it's possible, but we would need to look at carefully with the module conveners, whether you have the sufficient kind of prerequisites to be able to do those kind of modules.
So if you wanted to do a module on particle physics technology and you hadn't got the kind of required physics background to be able to do that kind of level 7 masters level module, then it may not be a sensible option for you to do, but it's certainly something that we could look at and consider as to whether you have the kind of the prerequisites, even if you haven't actually worked in that area before.
How can we get the complete modules in a credit unit? How many modules do the Data Science MScs have? I think again, the best thing to do is to look at look at the website in terms of what the modules offered are, how many credits they are many of the most modules are worth one credit, some modules in maybe the other school, say the kind of business school in media, arts and humanities have modules which are worth 30 credits.
So occasionally you might find a kind of double credit module that you want to do. But that's yeah, I would say look at the of the website and again getting contacts with myself or course convener if that's something which does it make sense or you wants to kind of ask about that that's absolutely fine to email us info about research internship opportunities at Sussex for students of Master in Data Science that varies.
So there certainly sometimes are possibilities that we do have, certainly through DISCUS. There are opportunities that sometimes come up throughout the year to work on of a small paid research project within the university. Definitely we have some that have happened each year for the last few years not a great number of opportunities, but there certainly are opportunities and three, five possibly opportunities so it's possible.
And again, you know, I would certainly say in those cases, if you are interested in those kind of things, it's worth you once you've arrived talking to the module conveners in the kind of areas that really interest you, talking to DISCUS, going to the DISCUS events as well, where other opportunities will be kind of you'll be made aware of these kind of other opportunities as well.
They would be the kind of the best ways of kind of finding out about these opportunities. Along with our virtual learning environment, a site where students are kind of wanting industrial placement they kind of on there and we kind of put other kind of opportunities on there. Again, we have a kind of discord channel was well aware of opportunities that come up of advertising.
So there's lots of different places where some of these opportunities are advertised, but it does vary from year to year, and there are never any guarantees that something will be available. And obviously they're, you know, potentially competitive in terms of which students would be able to to take these opportunities. Yes you can take optional modules outside of your course, provided they fit on your timetable and provided the module conveners, the course conveners agree.
We know we try to be agreeable, but, you know, there are sometimes reasons why we think something's not sensible and we would advise you against it.
Practical projects are close to the industry need? I mean, I'm not I'm not aware of exactly what you did in your previous degree. I understood how practical that was. I think many of the modules do try to kind of, you know, they are practical in the sense that they are looking at real data but they're not necessarily, you know, very closely aligned with the current industry project.
So if that's what you're looking for, maybe it's not the best option for you. So I don't know what you've done previously. Again, that might be something to kind of have a chat about with myself. Or one of the other conveners with separately. Whether you think this will complement what you've already done or whether it's same as what you've already done.
So yeah, I think that maybe that's a conversation we can take offline. If you would like to see, is there a directory of masters projects so we know and can see what level of work is expected? We certainly do. Past masters projects are available, not necessarily all of them but we certainly do make available some of the previous masters projects.
So you can see certainly a list of names of the projects. I think that's generally provided or can be provided. And then yes, certainly some dissertations are provided to give you an idea of the I suppose the level and format that's expected. Options for changing the module we choose initially? Yes, you can, but there are timelines for that.
You can't change module. You know, more than two weeks into term because then you'll have missed too much. Most of the modules get going really, really quickly. So within one or two weeks you'll be doing things, learning things. If you missed that part of the module, you know, it will be really hard to catch up. So we do occasionally think we let people switch modules up to three weeks, but generally, you know, the recommendation is don't switch after the first two weeks of a module. Criteria for enrolling for placement year? Essentially that you can obtain or secure a placement. So if you're not on the placement course and you come, we can still help you try and find a placement. And if you find a placement you can switch on to that degree. There are some criteria in terms of what constitutes a valid placement. It needs to be a graduate level position in data science or AI for at least nine months of the year at full time but you know, they're quite minimal in terms of the kind of expectations in terms of, you know, what constitutes a valid placement. Any of our students would be able to enroll on that should they want to.
You could select them, I believe pretty much from when you've registered, I think, which happens sort of August September time. How big are seminar groups usually? That varies a lot from module to module, lecture groups can be anything up to sort of, you know, a few hundred. Seminar groups, lab groups, tend to be smaller. I don't know if Swati, Rachael and Mohamed want to comment on sizes of seminar groups have been in this year I think they do very very quite a lot.
Yeah they do. At the labs, it's a lab full of people. So I don't know how many computers are in one of the labs, but the big big labs at the front of Chichester, I think they take yeah, I take about 100 students. That's usually this should be a number of teaching assistants in there with that many. Yeah. Definitely.
And then some of my other modules, I did the policy strand and all seminar groups around 15. So yeah, it's a massive variation yeah. I think we would normally say that seminar agreements between 15 and 30 might be covered by a single T.A. if there are more than 30 in a seminar group or lab group or something like that, then there would be multiple tutors or teaching assistants in the way to, to work with the students Any other experiences on that?
Mohammed or Swati?
So the generally the practical sessions are divided in groups so that if you need any help, these are there and it's like 15 to 20 students only in a group. It's quite compatible and you could easily get into this with the Professor or TAs.
Yeah. So yeah, I would recommend that very much in terms of like, you know, making use of those kind of access to the kind of the tutors and the TAs and asking questions as much as possible in those sessions. What are the materials available for Data Science students to study?
Not sure I quite understand the question, but all the teaching materials are accessible via our virtual learning environment called Canvas. So all the lecture slides go on there. I think all lectures are recorded or they're meant to be recorded. Are they recordings go onto campus as well. Lab questions go into campus assignments are generally submitted via campus, unless there's some reason why they can't be submitted online.
So that's the kind of electronic points of contact for all of the materials. Reading lists are also provided on Canvas. So all of that material will become available to you once you're enrolled on the course. Some of that material will be provided right from the beginning. Others of it will be kind of updated throughout the term as you are studying. Generally it may well be you as you go through the material has been updated on a kind of weekly basis you'll get the materials and for that current week. At which building do we have to study?
I'm not sure I quite understand that question.
So the building. Yeah, I mean you'll be in various if you're talking about where you'll be on campus it's on the Sussex campus and there are various different kind of labs and lecture theatres across the campus and there will be, you'll be in lots of different ones at different times. You won't get provided with a laptop. You see that question pop up.
But we do provide computing facilities for you. We have high performance computers in our computer labs, which you'll be able to work on. Those labs are all accessible 24 hours a day so you can go in at any time, day or night that you wish to study okay. I think we've managed to deal with the questions, so that seems good.
There's one more Julie, just quickly, I think you mentioned it in the presentation, but someone's just asked again if someone's got an offer for the single year can they switch to the industrial placement?
Yes, you can. But what I would really recommend you do, there's one caveat to this in general, it works out best if you've got your offer for the single year, you can you find a placement and then you switch to the industrial placement.
Yeah, because getting a placement is not kind of a given. It's not necessarily straightforward. So it is much better, actually, and I would say this to everybody, to come on the one year course and then switch to the two year course once you have found a placement. And we can do that really, really quickly.
You know, we have students even in July and August, who suddenly get a placement and want to switch to the two year course. And that's you know, we have made that happen in the past. I've I do realise this is slightly more complicated for international students in terms of sorting out visas. Visas can be extended. So I would still recommend that as the route to go for rather than switching to a two year program now and getting a of the two year visa, I'd come on the one year visa and then extend the visa.
Should you be successful in securing a place. That would be my recommendation. And should you find a place where we can switch you and it is straightforward and we can help you extend your visa. And that again is generally fairly straightforward and is a better option than being the other way around.
It's fine if you are here on a two year visa, the two year course, you can switch on to the one year course and that's fine too. But then, you know, you've kind of, I suppose in that sense overspent on your visa for two years in that sense. So I would recommend that. There's another question that's just come in: can we get more information about some internship opportunities for AI and Adaptive Systems, how would we set our module options to best suit for future employment? Some internship opportunities very a lot, it depends on so many different things. There may be opportunities within the university, with local company, you may find your own that you know there are opportunities throughout the year. We will kind of be able to provide you with links to things and suggestions for places that you could look locally and beyond for such internships.
Many of them are available via LinkedIn. That's actually a really good place to start looking for placements, internships and employment but then also, you know, there will be opportunities within the year where we will kind of have these kind of networking events, which again will hopefully lead to internship and other opportunities as well.
I mean the I suppose is most jobs currently the kind of machine learning, image processing, natural language processing kind of area of AI. That's what everybody's talking about. So they're, you know, if you want to kind of, you know, guarantee being snapped up on graduation that may well be what you want to kind of focus your studies on but you know, there are job opportunities for students, whatever kind of options they have taken.
All the students do the core, the machine learning things, which definitely make you very employable. Any of our students here, are any of you AI students? I think you're all Data Science. Which degree you are Mohammad?
I'm on Data Science, yeah. But I mean, I was able to take some some option modules in image processing, so I had to know how to use MATLAB also.
I was also able to get some firsthand experience, in web services and web applications. And I believe that the project that you will take by the end of module will help you a lot to to like to free yourself up and making your web services web applications because it like it focuses a bit on the back door developing and this is the software the software domain is just like very big here in the UK.
So you can you can give yourself a first push for that.
Okay. Another question is coming. I am going to have to go in a minute. So any other questions that come in after this one, I'm going to have to say no. But if you do have more questions, please do kind of let us know and we can try and sort of answer them after the session. Modules that you take one semester - usually it's four.
And you can see information about these modules on the website and there's some information also on the slides that we'll share afterwards and you'll be able to select these modules once you're registered on the course. And yes, so that's kind of how you it's kind of go about choosing and the thesis. Again, you know, it's good to start thinking about that early in terms of what you want to do to start talking to people as early as possible.
But, you know, again, that's something which, you know, gets finalised around Easter and then you start working on that in sort of, you know, over the kind of you write your proposal around that time. And then you go on and work on it in the kind of over the summer period. Okay. I am going to have to go.
So I'm going to thank again our students Swati, Rachael and Mohammed for joining us today. And thank you all for joining us. And I hope the sessions been helpful. If you do have questions, then Ben may well be able to help you answer those kind of practical questions, particularly about applying if you've got more questions about the courses then I as course convener, or the other course convenors I mentioned earlier, Omar Lakkis and in Ian Mackie, would also be able to kind of answer those questions for you.
Thanks Julie. I've put my email address in there so any questions do forward them on to me and I can send them on to Julie or answer them myself. Thanks, Julie. And thanks everyone. I think we'll end that here.
Lots of great questions. Yeah. Thanks everyone. And we'll get those recordings and slides to you at some point soon so you can watch it back and some of your questions may be answered there as well.
- 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