Future You podcast transcript

Big data biology (with Cardiff University)

Dan Mason, Editorial manager
July, 2022

In this episode of Future You, Cardiff University's Veronica Grieneisen talks us through an exciting new postgraduate course for this year - the MSc Big Data Biology, which aims to train a new generation of scientists with big data skills


In order of first appearance:

  • Dan Mason - editorial manager, Prospects
  • Veronica Grieneisen - course leader for MSc Big Data Biology, Cardiff University


Dan Mason: A new generation of scientists is needed to combine their knowledge and understanding of biology with skills in analysing, manipulating, and interpreting big data. Find out about the new postgraduate course that will help you become one of them in this episode of Future You.

Hello, and welcome to Future You the podcast from graduate careers experts Prospects, we're here to help you achieve your career goals. My name is Dan Mason, and in this episode we're talking about an exciting new postgraduate course offered for the first time this year by Cardiff University. Course leader Veronica Grieneisen tells us all about the Masters degree in Big Data Biology. We hear a lot about big data and the demand from employers for skills in this field. But the combination of big data with biology is less common. So it's fascinating to hear Veronica explain the course, what it involves, who it's for, and the types of careers you might go on to have. If that's piqued your interest, have a listen and then for more information, there's a link to the course details in the episode description. Here's Veronica.

Veronica Grieneisen: Yes, so my name is Veronica Grieneisen. I'm a Reader in Systems Biology at Cardiff University, and also the programme director of the Big Data Biology MSc that we're launching now this year.

Dan Mason: Fantastic. Well, thank you very much for joining us Veronica. And it's that MSc Big Data Biology that we're here to talk about. I think it'd be good to start just with some definitions of what you actually mean by big data biology.

Veronica Grieneisen: Yes, indeed. So well, big data biology is a fairly new and rapidly rising area of biology, where we basically seek to find patterns in large and what we call multimodal datasets, using informatics and statistics, but coupled with that biological reasoning. So when we talk about big data, we refer to datasets that are too large and complex to be analysed by traditional means, and the patterns that we aim to uncover will be key to infer biological mechanisms, say to make predictions about how we can interfere or ultimately how we can understand better living systems. So that's, that's how we will talk about yeah, big data biology and its general aims.

Dan Mason: OK, so could you tell us a little bit about why you're launching this course now, and what what makes it so unique?

Veronica Grieneisen: Yeah, so the time we feel is absolutely ripe for this type of course. So technology, the last years, it really has been advancing very rapidly in the area of collecting data. So it could be on the genomic or the molecular scale, but also up to, for example, satellite imaging of ecosystems. And all these levels are interacting with one another in very complex and non-linear ways. And so we have to look at this data and try to find hidden signatures of how we could combine these datasets to reveal some new mechanism or some new understanding. But to do that biology, as a field, it really needs to have a new type of biologist that can exploit the richness of this data and make meaning of it. And that also means that new skills are required and new approaches. And we will be teaching and developing this during the course. And so that's why this this course is so timely, it's really necessary for things like drug discovery, or, you know, trying to solve food security issues, or new ways of dealing with climate change. So it's really to empower the new, you know, the biologist of the future, so to say.

Dan Mason: OK, so it's you talk there about a new type of biologist.

Veronica Grieneisen: Yeah.

Dan Mason: So who is this course for? Who is it designed for in a sort of general sense? What sort of people listening to this might you be be looking for to take up this course? Both in terms of what your formal entry requirements might be, but also in a more general sense?

Veronica Grieneisen: Exactly. So when we talk about statistics and informatics and using computers, many people will think oh, that's not for me, but actually, what we really are seeking for is, you know, having people to have a deep sense of curiosity and a willingness to try new methods, because we will be teaching absolutely everything along the way. So one doesn't have to have a prior experience with advanced informatics at all. So we're looking at life science students, biologists, chemists, people who are interested in plant science, biomedical students. But we also very much welcome STEM orientated students, so that would be coming from maths, physics, computational scientists and engineers. And we will make sure that these students from different backgrounds will be interacting with one another and learning together how to work in interdisciplinary teams. So the only requirement really is to have a good enough level of English, right, and to be motivated and have this, you know, undergraduate in one of these scientific areas.

Dan Mason: OK, so this interdisciplinary nature of it is probably I imagine, it's quite an important element of the course then.

Veronica Grieneisen: Very important. And it also will reflect very much how a real research environment but also maybe a work environment will be in the in the future, there will be people with different skillsets and perspectives coming together. So we will be nurturing that during the course.

Dan Mason: Fantastic. So let's get into a bit more detail then about the course itself. If you can tell us a bit about the format of the course, the structure, the content areas that students can specialise in that kind of thing.

Veronica Grieneisen: So I think that the most important thing to emphasise is that we will be, our main objective, and the way we teach is that the student will learn by doing. So yes, we will be giving lots of theory and lectures. But we will be giving a lot of workshop and hands-on application sessions. And that will be throughout the whole course. The course itself is divided roughly in three stages, the first one we will be teaching and practicing some important analytical skills. And that will be enforced with workshop time. So the students here will learn computational techniques, how to deal with datasets, it could be imaging or genomics. And that will give the foundation for the rest of the course. The second part of the course is diving deeper into the biological analysis of the patterns. So this will be through bioinformatics, as well as something we call systems modeling. So we will be using computers to actually simulate reality to simulate the systems that we're studying. That could be for example, cancer cell growth, or swarming organisms or ecological systems. And at this phase, the students will have their first taste of real data. So there will be a client which will present some datasets, and the students will be able to explore and see what, you know, what they're able to get out of that. So the last phase of the course will consolidate the learning that has been established throughout. And this will happen through a research project, where there will be state of the art science projects in the area that the student has decided to specialise in, leading to a dissertation.

Dan Mason: Ok, and so is the dissertation, the the bulk of the assessment then or is there assessment throughout the course?

Veronica Grieneisen: There will be assessment throughout the course. What's very special is our assessments are, they will reflect the real working environment that the student might go into in the future. So instead of just having traditional exams, there will be, for example, a video podcast that the student would have to do, or a bioinformatics pipeline that you will deliver. So there will be real actual deliverables which will reflect some, some big data analysis or communication side of research

Dan Mason: OK, so you mentioned there that one of the key parts of this is the use of real data. As well as that are there opportunities for students to undertake placements, work placements as part of this course?

Veronica Grieneisen: Definitely. So towards the middle of the course, there will already be a module called case studies. And here, the student will be presented data through a client. So that would be someone who has a big dataset, and needs help interpreting it. And so the student will already then you know, have a chance to sink their teeth into into a dataset, it could be imaging, it could be epigenetic, it could be about disease, so that the student will also have some liberty to choose. And after that, there's still the research project, which leads to the dissertation. So if you're going to follow this course, and you don't know exactly what it is that you want to specialise in, you'd have different opportunities to try out different fields. So that's a beautiful aspect of of doing big data biology.

Dan Mason: Absolutely. And so now probably one of the biggest questions for any students listening to this who might be interested in this course. Where's it going to get them? So what are the skills that they're going to come out of this course with? And what are the career options that you expect students who study this course to have in front of them?

Veronica Grieneisen: Yeah. So because we're always, we're throughout the whole course we're going to be teaching by doing you will really leave the course knowing that you have skills, you can actually, you know, start something up yourself, you understand how to run a big data analysis. So that will allow the student to be incorporated in any type of data analysis industry. So, obviously, the course is focused on biological data, what one has to realise is the biological data is, by itself vastly complex. So, if you're able to handle biological big data, you know, it will be very easy to jump to things like economics, or sociology, politics, and so forth. But most importantly, the student will leave with a deeper understanding of life sciences. And so they would also be able to pursue their academic research career, if they would like that. And it's not established what direction they want to go into. So if they would like to go into plant science, that would be perfectly fine. If they want to go into biomedicine that's also something we would encourage. So that type of differentiation isn't done yet. And the last thing I just wanted to emphasise is that computational biologists and bioinformaticians, they are highly required and sought after in the academic job market. So there's going to be plenty of opportunity for these students when they get out.

Dan Mason: Yeah, these are skills that are definitely only getting more and more in demand aren't they?

Veronica Grieneisen: Exactly.

Dan Mason: So let's talk, go a little bit beyond this course. Could you talk a bit about what makes Cardiff University and the city itself a great place to go and study?

Veronica Grieneisen: Yes, definitely. So one of the amazing things about Cardiff University is that it's in the heart of the of the city, many universities are placed outside on the outskirts. So this means that you can really, you know, you can have a day of work and still go out with your colleagues to the pub or to the park, it's a lovely place to be and to, you know, continue having meaningful interactions with people. The other nice thing about the biosciences, School of Biosciences here in Cardiff University, is that you have great research happening here, but not only on one topic, but on all the different scales of biology. So from genetic up to the ecological scale. So a very friendly community. So that means it's really the right environment for interdisciplinary research. So as a big data biologist, you will find a lot of scope for discussions, interactions and and to seek your own collaborations. Besides Cardiff University, there are different research hubs around the city and all of them have big data biology sets, and all of them would be willing to interact. So it's a very, it's a very rich centre here in all these science aspects, but also cultural aspects.

Dan Mason: Yeah and you've given us a great insight there into what what might attract students to this course. If anyone listening has listened to this and thought this is something they'd be interested in, where should they go for more information, information about how to apply, information about fees, all that kind of thing?

Veronica Grieneisen: Yeah, so we have, all of that would be found on the web page of the School of Biosciences. So if you just enter there Cardiff University, you will see that there is the Masters programme link. And there you'll see the Masters programmes which are being offered. So this would be Big Data Biology MSc, and all of the information about the fellowships and the funding opportunities, application dates would be given there. I would also like to say that if any of the listeners here would like to address a question personally, you can always send it to, to myself. So I'm Veronica Grieneisen. And, actually, the  last name is a bit of a jumble. So that's g, r, i, e, n, e, i, s, e, n and you'd be able to find my email and also send any questions you have about the science and the future opportunities.

Dan Mason: Excellent. Is there anything else about the course that you'd like to emphasise or re emphasise?

Veronica Grieneisen: Well I would just like to emphasise that it's the teaching staff and the scientists which are part of it, it's really like a dream team. So everybody is extremely motivated. And we don't see this as just something which is top down, like we're here to dump information on students, we really see this as a process that we would also be learning together with the students and, and developing together new ways of looking at patterns and big data. It's a very novel cutting edge field. So it's exciting for all of us. And yeah, and I hope that whoever's listening here can one day be part of this, this excitement that we have here.

Dan Mason: Yeah. And as well as being exciting for you to launch this new course, I imagine it is going to be quite exciting to be the first, you know, some of the first students to take it as well.

Veronica Grieneisen: Definitely, because we are very open to feedback and we will be talking a lot with the students to know what is it that they would like to see more of. Or what is it that they feel would be more interesting to pursue so we can start with the help of the students, you know, customizing it a bit.

Dan Mason: Fantastic. Well, Veronica, thanks very much for your time.

Veronica Grieneisen: Thank you so much Dan.

Dan Mason: Thanks very much to Veronica. And just to reminder again that the link to the course details can be found in the episode description if you want to find out more. Don't forget to search for other postgraduate courses head to Prospects.ac.uk, where you can also find plenty more advice on making the most of further study. To hear more episodes of Future You find us on Spotify, Apple Podcasts, or wherever you listen to podcasts,  or at Prospects.ac.uk/podcasts. Finally, you can also get in touch with comments, feedback or suggestions. Just email podcast@prospects.ac.uk. That's it for this episode, thanks very much for listening and we'll see you soon.

Transcript ends

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This transcript was produced using a combination of automated software and human transcribers, and may contain errors. The audio version is definitive and should be checked before quoting.

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