This is the transcript for the video Master of Data Science & Innovation webinar


All right, I think we’ll make a start. So, Hi, folks. And welcome to the master of data science and innovation, open week webinar and live Q and A. And thank you very much for coming. Before we get into the session proper, I just want to note that we record these sessions so that we can provide them to other prospective students later. The only thing is that we’re recording the video and the audio, which is just us as the panelists and we’ll also capture any text responses that you might give so that we can build up a catalogue of questions, which again, is just to benefit people in the future. And you can see there’s more detail on our privacy policy and stuff that’s just on the screen now. I’d also like to acknowledge the gadigal people of the eora nation upon whose ancestral lands our City campus, and indeed my home now stands and pay respect to the elders both past and present, acknowledging them as the traditional custodians of knowledge for this land.


So, in this session, we will be using zoom and using the q&a function. You’ll hear a little bit from four of us who are on the panel. So Associate Professor Tony Huang, who’s the course director, he’s going to give you a bit of an overview of the course, myself, Dr Simon Knight, I’m a senior lecturer on the course and in the faculty, and Rebecca O’Dwyer and Chris Mahoney, both of whom are current students, and they’ll be giving you a bit of a student flavour, you get a chance to ask them some questions and hear from their experience as well. To ask questions you should be able to see at the bottom on zoom, there’s a q&a button. And if you click that, you can ask us some questions. We’ll either respond to those in the chat function, or we’ll respond to them verbally. And always good to have a here’s one we made earlier, we’ve got some questions that people generally speaking, ask. And so, we’ve actually got some that will run through towards the end of the session. Feel free to ask those questions at any point in the session. We’ll come back to them at the end. So, don’t be offended If we don’t answer them immediately. We will be looking at them towards the end of the session, when we’ll do the kind of overview of those questions. So, what we’ll be doing, I’m going to hand over to Tony very shortly and we’ll have a brief overview of the masters. We’ll run through the top 10 questions, ones that we typically get asked. And then we’ll go through any questions that you might have, that we’ve not addressed through the q&a field. And as I say, feel free to ask any more questions throughout. And if you want more detail on the MDSI, then we give you in today’s brief overview, then you can check out this video on And if you search for ‘Where can a data science and innovation take your career’ then you can find a more detailed video there. You can also drop us an email and we will be happy to answer questions there as well. Okay, so I’m going to hand over to Tony now to give you a bit of an overview of the course.


Thank you, Simon. Hello everyone, I trust you have already done some research about MDSI before you came here. So, I will be very brief to give you an overview and emphasise some key features of MDSI. So, let’s go straight to the point. Why you should study MDSI at UTS. The MDSI is the only transdisciplinary data science program in Australia that has creativity and innovation components. The course curriculum and the subjects are specifically developed for data science and is regularly reviewed and updated to keep up with the changing needs of students and the job market. And not only that, the course subjects are developed in collaboration with our industry partners and delivered by current working professionals and our quality academic staff members. Throughout the learning process, you will have a lot of opportunities to work on real world projects and with actual data sets. So, when you graduate, you are industry ready. Next. So, the industry partnership and engagements are a core part of the MDSI program. So, this is another key feature of our program. The industry partnership, and industry program is to prepare students to tackle complex real-world challenges. Through the industry partnership program, we developed the course with inputs from our partners. We also invited working professionals to teach and give guest lectures. And the students have opportunities to work on real world projects. And students could also have internship and work placement opportunities to work under the office of our partners. For example, National Heart Foundation and Origin Energy. Next.


So, this is our course structure, you will need to complete 96 credit points to finish the degree and this includes 44 credit points in the core subjects and 50 credit point and electives. So, the core subjects as you can see from the left hand side of the screen, this subjects are set of subjects for you to learn Core Data science skills, build real-world project experience and develop a human-centred perspective and ethical thinking on bigger data. On the right hand side of the screen, we have electives and these electives can be selected from in house data sciences optional subjects and also from across University faculties. So, we have about 25 electives. And also, on top of that, you can choose any subjects from other faculties for 12 credit points. So, as you can see, we have a very flexible and comprehensive course structure to lead students’ individual needs with this flexible cost structure, students will be able to develop their own data science interest and expertise. Next.


So, these are the admission requirement, you will need to have at least bachelor level degree and this degree will need to be in a specific data related field such as IT, math’s and statistics, physics and economics,  finance, business, accounting and so on. If you have heard about MDSI before, we used to require students to have at least three year work experience, but I would like to stress that this requirement has been dropped. And we no longer require you to have work experience to enter the program. MDSI has been recognised as a very high quality program, and we want more people to have access to and benefit from this program. However, if you still do not meet the admission requirement, you are still encouraged to apply. And in this case, you just need to include evidence of your prior learning or any work experience to demonstrate your skills and experience with qualitative data in your application. And I think that’s all from me, Simon.


Great, thanks, Tony. So hopefully that gives you a bit of an overview of the course. What we’re going to do now is jump right into those top 10 questions. As I said before, feel free to ask other things via the q&a. And we’ll get to them after we’ve addressed these top 10 questions. And this is where I’m going to hand over to Rebecca and Chris for various other questions as well. I’m just going to stop sharing my screen now as well. Which will help me focus. Okay, so one of the key questions that we get asked, especially at the moment, is how the program is delivered, and whether it’s delivered online or face to face and how the classes are scheduled and issues like that. And so, to answer that question, I’m going to hand over to Rebecca.


Thanks very much Simon. So, my name is Rebecca O’Dwyer, I’m in my second and final year of MDSI, and I’ve been studying full time. One of the reasons that I chose UTS and MDSI was because it was a face to face course, I was very keen to do face to face learning, to interact with other students. COVID-19 has changed those plans somewhat, last semester and this semester are following New South Wales health regulations. We’ve done online learning, but I think one of the key messages to deliver is if online learning has to continue because of the COVID situation into 2021. What we’ve seen is lectures, tutorials, and labs that are all done at a scheduled time rather than being prerecorded. So, what you still have is all the students interacting together via zoom. It’s not as good as face to face but I think the faculty has done a really good job of adapting to a difficult situation So, hopefully face to face in 2021, but be prepared for online learning if we’re all still keeping 1.5 metres apart.


Thanks, Rebecca. And now so another key question that we get asked is what the assessments are like in the course. And often what people really mean by that is, are there any exams? So, I’ll hand it over to Chris for that question.


Hi guys, my name is Chris. I am studying part time. Currently, towards the end of my second year, of a four year part time degree, and I’m currently working full time in the warehousing and logistics industry. For me, the structure of the assessments are very practical and there are no exams. None, except if you are doing any electives, there may potentially be an elective in one of the other faculties. But in this particular degree for our core subjects, they’re all assessments. The assessments themselves are very authentic. They allow you to apply your learning in a real world experience. Very practical assessments. A lot of the data sets are very practical, real world datasets not made up or fictitious data that you might get in a statistical textbook. So, a lot of the data that we get to see, feel and play with are real world examples. And some of the assessments that we’ve had an opportunity to do as part of this degree – we have created vignettes, ourselves. We do a lot of technical coding assessments in both R and Python. We are given an opportunity to present to a class to external businesses. And, those presentations are a way for us to articulate and to show and tell a story about the power that we have come to know so intimately. There are of course reports and various documents which are compiled in parallel to the technical coding documents. There is group work, which I personally find very beneficial because bouncing ideas off my fellow students, hearing their experiences, thinking about, oh, well, maybe I should have done it this way. Or they have a different idea or, or together we can come up with a different way to try and solve a problem that we’re having together. The assessments are intense, and, that’s what makes them so fun. To be given a data set and to be told, go and explore this data set and build something that can be predictive of this data. I find fascinating. And its a, it’s a fantastic structure of the assessments that we do here. Thank you Simon.


Right? I’m actually going to stick with you and then throw through to Rebecca as well for the next question, which is about how much time you have to devote to study each week.


Sure. So, working full time, obviously has its constraints. Studying part time is the way that I’ve chosen to do this degree. The amount of time as a rule of thumb 10 hours approximately 10 hours is what should be a target for the amount of time to be spent on each subject. So, for me, if I’m doing two subjects per semester, that would roughly equate to be about 20 hours per week. For me, sometimes, I get really caught up and take a really deep dive into the assessments and may end up spending a little bit more time than that. There are other times maybe towards the beginning of the semester where not all the information is yet become available, you’re still exploring your way through the textbooks and whatever it might be, where you might spend a little bit less than 10 hours. So, there is a bit of give and take there. Obviously, the more you put in, the more you get out, but there is of course, the work life balance, which comes into play as well. So, rule of thumb, for me personally, I aim for about 10 hours. But of course, that can sometimes and a lot of the time go a little bit over. So that’s what I’ve chosen. Rebecca.


Yeah, from a full time perspective. So, I have tried to treat this like a full time job. I sort of tried to work during business hours. That said, I would say there are pinch points in the MDSI degree when you find that you have a lot of assessments, backing up together, which is when, as Chris has said, you will probably devote more hours. And at the start of the semester, you may devote less hours just because you don’t have the assessments due. The other key point, I think it’s worth remembering when you’re thinking about the time requirements for this is that you’re looking at 2 x 12 week semesters per year if you don’t do any subjects during the UTS summer semester. So that’s only 24 weeks out of your full year. So, while during the semesters, it can feel very intense, and you can feel like you don’t have much free time. There are actually large gaps in June and July and then over the Christmas period where there isn’t any university. So, it’s not a 365 day a year commitment.


Great, thanks both. And I will say we do try to make sure that assessment deadlines don’t clash, although there is always a bit of a balance to do. Okay, So question number four. In our most frequently asked questions is how much does the course cost and for this, I’m going to hand over to Tony and we’ll also throw a link into the chat so that people can access that there.


Thanks Simon. So, you can find out more about what your degree will cost with the link we will put in the chat. So that’s And once you land on that page, there are a number of items you need to specify. So that you can find the specific fees for your degree. So, you need to choose ‘search fees by course’. And then specify the fee type, which is a postgraduate domestic coursework. So MDSI is post graduate domestic coursework.  And the fee year is 2021 and the cohort year is 2021. So, the cohort is a year you started your program. So later if you want to find out information for 2022. The cohort year would still be 2021. And the fee year will be 2022. So, the course area is our faculty, so that’s transdisciplinary innovation. And then you need to specify the course code or name. So, if you do not know, or if you don’t have the exact course code, you can just type in data science. And then you click on search, the webpage will return fee information for you. So, you probably get the fee per credit point. If you want to calculate the draft total cost for your course you need to multiply the total credit point which is 96 by the fee per credit point. Remember each year the course fee may change. Okay, so if you want to know the total cost, this is only an estimate. And remember Commonwealth supported place are not available for MDSI. But if you are a domestic student, you may be eligible for fee help. We also put the,  if you want to find out more information about fee help, you may find a link we put it in the chat, which is, if you do have to pay a fee and you are a local student, you may be eligible for fee help which is an Australian government known scheme. And using fee help means you do not have to pay for your tuition fee up front. Every person has a different situation. So, you can go on that link to find out more information about the fee help. That’s all from me, Simon.


Wonderful thanks, Tony. So, I’m actually going to take the next question from other people talking, which is whether or not you need to know how to code or the level of statistics that you need. And one reason it makes sense for me to answer this question is typically, although not always, the subject that I teach is the first subject that people take in the Masters if they’re working part time, and they’ll take it as one of the first subjects alongside other subjects if they’re going full time. And the answer to this question is that we assume that you have a basic statistics and quantitative skills. If you’re not sure that your skills are up to level, then we’d recommend taking some sort of bridging course, there are electives that you could take at UTS, that would be a good first subject to take to then ease you into the rest of the masters. And we actually have a few free open courses and I’ll post a link to one of them in the chat shortly that do a little bit of introductory statistics as well. The course is really structured to try and guide you through learning these things. But part of that is going to be that we will try and point you to some of the best free resources available on the web alongside giving you structure within the assessments within your group work and so on, to learn these things. It’s great if you do some of that before enrolling. And again, we can point you to some of the good resources that we’ve found for this. And within the MDSI, you will be guided through using R to solve problems. So particularly in the statistics subjects, and I use a little bit of R in my subject as well. And we try and onboard you to that, ease you into it. And you’ll use Python, particularly in some of the latest subject probably in the second year. If people know one of these programming languages or another language then it’s always useful to be able to bring those to the table from the outset, as well. I think that’s all on that one. If you’re not sure that you have the pre-requisite expertise, then you can ask us questions, obviously right now, but you can also get in touch and we can, we can direct you. Okay, so our next question is really a kind of high level question and I’m going to pass it over to Chris, which is what do you learn in this course? Chris?


Thank you Simon. It This is a fantastic question. More or less challenging questions to answer, but let me have a crack. And the course is, of course two year full time, four year part time degree. It’s very, very comprehensive. And a lot of the time you learn and you apply you learn, apply, learn and apply. And some of that learning, of course, comes in class. You learn various different statistical models, etc. and have an opportunity to apply those in your assessments. Sometimes the assessment is do this. And you need to learn how to do this, or whatever that might be. And that, for me personally is a very exploratory, a way for me to explore how to do certain things. Either use online resources, do my own research, ask some colleagues, ask some friends, and find out how it’s done. Because data science itself is a very complex field. It requires a diverse range of knowledge; diverse range of skill sets to try and find a way to solve a particular problem. And the way that the MDSI is structured, allows you an opportunity to figure out how to do that, in a very guided, supportive environment. There are a number of aims throughout the course, which try and build you up to be a well-rounded data practitioner, not just a data scientist. Yes, there is machine learning. Fantastic. Yes, there is deep learning. And there is all sorts of fantastic ways to teach the computer how to learn. But there is more than that. There is data visualisation, there is exploratory data analysis, there is so much more to a data practitioner, and this degree, aims to try and build a well-rounded skill set for a data practitioner. There are of course there is essential knowledge given in core technical skills. Simon has mentioned some in R and in Python as well. Depending on the practical Capstone units that you would do, which we refer to as the iLabs, you may have an opportunity to explore other languages, for example, SQL and other languages like that. There are core technical skills, core Data science skills, creative thinking skills, communication and collaboration there are, there are a broad range of skills that this degree aims to push across. But in the same context, the very human-centred perspective, data analysis is not just here is a table of data, it’s much more than that. It’s not just here’s an equation or here is a function of how to achieve something or other, which mathematicians do. But it’s much more than that. It’s how can you elicit learning from that data? How can you give a knowledge about what the data is doing. Is that a visualisation? Is it a plot? Is it a model? is it your results? There is so much more to, to being a data science practitioner. And this master’s degree allows you many, many opportunities to do that. And some key examples, I mentioned one before, to write a vignette. And if you don’t know what a vignette is, fantastic, you’re going to learn because in one of the assessments, we were quite literally thrown in the deep end and told to go away and write your own vignette and come back as part of the assessment. And we were given 10 days only, which on face value sounds intense, but when you start to think about it, and unpack it and start to understand what it is these assessments are trying to achieve. It was a brilliant dive in the deep end. To be able to create that how to documents. There are, of course, opportunities to learn how to build neural networks for image recognition, or for caption generation, there is opportunities to create machine learning models for doing, for example, movie recommendations, think of the Netflix data set, which has become a famous data set in the data science space. There are many, many, many opportunities given throughout this degree. And to answer the question, What do you learn in this course? Well learn is such a continuum, you learn so much in this course. And I hope I’ve given you a bit of an overview as to some of the things you can learn throughout this degree. Very challenging, like I said, very intense, but it’s very exciting at the same time, so well, yeah, I love it.


Thanks, Chris. And I’ll say as well as Tony said earlier, one of the ways that the course is designed is of course, with an elective structure. And that’s partly so the people can choose which pathways they want to go down. So, you can decide that you’re going to focus on perhaps the more technical side for some people, and perhaps the more sort of business and innovation side for other people and select where your existing strengths are, and what things you want to be building on through the degree, which is you know, a fun part of working with the student cohort as well.




So, sort of related to that actually, is a question about how you can make use of that existing expertise, your backgrounds, from perhaps previous study, and a question that we get asked is whether or not you can get credit for a course, or parts of courses that you’ve done previously. And I will pass over to Tony to answer that question.


Thank you, Simon. So, whether you can get the credit for course, that you have done before, the answer is yes. So, you’ll have up to 32 credit points to be considered for elective. So, exemptions are granted only on the basis of prior postgraduate study at the University, the Australian university or the equivalent overseas Institute. So, you have up to 32 credit point to be considered for electives, only for electives, not for core subjects. To be eligible for recognition of a prior learning, the subjects being considered for prior study must have been completed within five years of commencing the course. Recognition of study completed before this period is not considered. So, yeah to summarise, so you’ll have up to 32 credit points to be considered for electives. And those subjects should be obtained, should be studied from your previous post graduate study. And should be, have completed within five years of you commencing the MDSI course. That’s all.


Great. Thank you, Tony. So, I’m actually going to take a couple of questions now. So, our eighth question is about what the career prospects for the masters are. And we’re obviously very mindful that, you know, it’s a big investment for people, people care a lot about the subject. It’s a fascinating subject. But you also want to be able to apply it into your work contexts, or use it for the next step in your career. And the Masters was really designed with that in mind, from its very inception. So, it’s been running for, I think, about six years now. And in those initial design stages, we were working with industry partners around what they perceived as the needs in a data scientist, and how we saw that we could make this data science Masters a bit different to other data science masters as well. Throughout our teaching, we have close relationships with industry partners. So, the innovation labs are project based subjects where it’s really all about you doing a big project with a partner, that’s 12 credit points subjects. And you have some sort of industry relevant output at the end. People will sometimes do those with their existing organisation. Other times we’ll partner them up with an organisation or they’ll reach out to somewhere and they’re trying to address real authentic problems. We have guest speakers in fairly regularly as well as panelists and so on. And we, we have run networking events. Obviously, we can’t run those face to face right now to try and build those relationships between our student cohort and industry partners as well. We know that lots of students have actually got jobs from these connections so particularly with the iLabs, so that’s those bigger projects, but also you know, we will send out internship opportunities, short casual work contracts and things like this. And those initial relationships that might only be a short contract will turn into something bigger sometimes. For some people, it’s also just a stepping stone within their existing organisations and it helps them to take that next step. So, some of the partners Tony actually mentioned some of them earlier, we’ve worked with Atlassian, Cancer Council, New South Wales, Hub Social, Origin Energy, Transport from New South Wales has been a fairly big one, National Heart Foundation, Lion, Rugby Australia, there’s a whole range of them and they’re from quite different sectors as well. Which I’ll talk about in a little bit more detail in a moment. And various of our alumni then go on to work for companies like that for Google, CBA, Reserve Bank Australia, the ABC, and various others. And that’s fantastic as well because we have quite a good relationship with many of our alumni. Some of our alumni in fact, teach on the program. So, they’re leading subjects with you. And others are coming in on our industry panels. They’re inviting iLabs, so current students to join them within their organisations. So, we really try and set it up to support that networking and to help you to make those relationships that will help you to take the next step.


Kind of related question is about what your background needs to be and again, Tony mentioned this before. So, if you have a background that’s got a kind of mathematical IT, analytic context to it, then you meet the criteria, and it will be processed and it should go through fairly smoothly. However, some people will ask, well, what if you have an undergrad degree that’s in an arts area, but you work in something that’s a bit more like finance, a bit more numeric, maybe IT. Do you meet the prerequisites there? And the answer there is if your qualifications not in a relevant discipline, then we want to see some evidence from your CV of prior learning and or work experience that’s going to demonstrate that you’ll join the course and you’ll be able to do it, right. We want people who are going to be able to make the most of being on the course. So that you’ve got knowledge of the basic quantitative data skills, key mathematical concepts, and some idea of, you know, programming potentially. And I’ll say one of the things I really love about teaching on the Masters is the diversity of our student cohort and the expertise that they bring to the table. That’s, it’s cool because they then go out and work in really varied areas after they’ve done the Masters, but they’re also bringing a lot to the room when we’re running our sessions. And you know, we’ve had people with pharmacy backgrounds with kind of data visualisation communications, product engineering, obviously, you’ve got IT, Accounting, those kinds of project management. There’s a real range of people. So as Tony said before, if you’re not sure there’s a process that you have to go through to be able to demonstrate you will meet those criteria. But don’t necessarily rule yourself out because you don’t have one of those mathematical or IT undergraduates, you may well still be eligible to join us.


Okay, so our final question, and it’s going to be a key one, of course, we imagine that people are looking at a range of different master’s options. And so, a key question for you is, how is this Master’s, the MDSI different to all of the other data science courses and I’m going to hand over to Rebecca.


Thank you, Simon. So, for me there were three key reasons why I wanted to choose the MDSI with UTS. The first might sound a little perverse, but one of the reasons I chose it is because it’s a two year full time degree, not a one year degree like many of the others that are on offer. I felt that data science was such a big subject that if I were to do it for only one year, I probably wasn’t going to have the skills that I felt that I would need to go out and work as a data practitioner immediately in industry. And I think as Chris highlighted when he talked about what you learn during the MDSI, it’s so broad that having that extra time really makes you a more rounded data science data scientist when you graduate. The second reason that I chose UTS and the MDSI was the strong focus on real world experience. I really didn’t want to do a degree that was heavily focused on statistics, heavily focused on textbooks. I really wanted to do a degree that was focused on making you ready to work as a data scientist in the real world. And through the innovation labs or the iLabs, as they’re called, you do genuinely work on real world data science projects. Chris and I are both doing innovation labs at the moment. we’ve both got clients that we’re reporting to every week. Chris and I were talking offline before we started, Chris was like, it’s like having a second job. He’s absolutely right. You’re in a university degree, you’re getting credit points, but you are genuinely delivering positive outcomes for a client through the work that you’re doing. And if you do it well, during the degree, you’ll be able to take those skills and leverage them post MDSI into a new career. The third reason that I chose UTS and MDSI was because you really can create your own degree. As Tony showed right at the beginning when he introduced MDSI it’s split between your core subjects, and a large chunk of elective credit points. And through those elective credit points, you can tailor the degree to be whatever you want. So as an example, I had no experience in SQL programming. I did a SQL elective. I was absolutely terrified of the command line on my MacBook every time I had to do something. So, I did a UNIX program, love the command line now. But other students do things that are completely different students do things that are associated with the MBA course and the business school at UTS, students do things associated with the science department, students do things associated with communication, you really do have the flexibility to pick whatever you need to make yourself into the data science practitioner that you want to be. So, it was the combination of those three things that led me to choose UTS and MDSI. And a couple of the things that have really come through since I started studying are the fact that I believe we’re doing a degree that’s the first of its kind in Australia, where it’s not just about the programming and it’s not just about statistics. It’s also about creativity and innovation. And the second thing is how good the cohort is. Chris and I probably a cohort of I would say 35 to 40 students, very diverse backgrounds, different ages, different nationalities, different experiences. And getting to meet those people and interact with them through group work has been great, really refreshing. I am going to be honest, and I was dreading the group work before I started MDSI. And actually, it hasn’t been that bad. So, I would say those are the key reasons why I chose UTS and MDSI. And Simon, I think what we’d like to do now is open up to questions.


So, unmute myself. Yes, that’s absolutely right. So, we’ve got a couple of questions in the q&a here, which we’ll work through and you’ll see I’ve also just put the screenshare back up so you’ve got the information for a bit more detail. So, we’ve got a question about studying part time and how much time that is each week. So, I’ll just answer that one because we addressed it briefly earlier. If you’re taking a single subject, not the iLabs, then you’re looking at about 10 to 12 hours a week for that subject. And that includes the face to face or synchronous session. So those are either on zoom at the moment, or back on campus, hopefully as soon as possible. And then just multiply that by the number of subjects you’re doing. The idea is that if you’re doing a full study load, it’s basically equivalent to having a full time job and a full study load is three subjects.


What kinds of programs do we use in the course and do we need a high power computer to run these or to work with the data sets, I can probably pass to one of our students to answer that. Go Chris.


Me personally, I have a laptop that can do a lot of very good stuff. It’s got extra memory, it’s got extra RAM and I bought it intentionally to try and you know, work towards having the machine learning capability on the laptop itself. So, from that perspective, I have purchased the hardware myself. But there are other opportunities, which you will of course learn about, about how you can utilise some technology that’s already out there to make your modelling easier. And two key examples that come to mind first and foremost, number one is a technology called Google Collab, which allows you to tap into some GPU technology on the Google environment in a very academic censored environment allows you to put your own models in the training yourself and get those results quickly and easily. So that you know, you don’t need to purchase all that high tech hardware yourself if you don’t want to. And if you just need to use some very quick technology very, you know, simple to use. Get those results quickly. You can use Google Collab and other technology which really comes to mind is AWS and through AWS, you are able to utilise from credits which you will get from your participation here in the course which will contribute towards GPU time, obviously there is pricing windows. So, the more you purchase, the quicker those credits will expire. And you also have the ability to, of course, pay your own money for a higher bandwidth. Those are options. And if your computer is something you’ve that it is, you know, not the highest tech on the market, that’s fine. A lot of the stuff that we do in some of those subjects don’t require a lot of that high tech, high powered computer software. But the data sets that we are utilising we are touching and we are feeling that we are playing with some of those data sets can be very, very large. If we’re thinking about a 2d data set, some of those columns can be 50/60 columns long and might be a 5/10 million lines long. Some of the other data sets that I’ve had a chance to play with are image data sets, which could be in folders, you know, 10/20/30 folders, and each of those could contain hundreds, thousands of images. Each of those could be kilobytes or even, you know, megabytes large. So, if you’re thinking about the size of those data sets as multi-dimensional data sets that can get very large very quickly. And some of the technologies that we use, allow batch processing, so your computer Ram is not utilised all in one go. That is possible. And you’ll learn some techniques about how to do that. But also, there are many other ways in which you can utilise some of that high tech high powered computer software to really do what you need to do. One of the key things I’ve learned very, very, very early on in this degree is learn to iterate quickly. Come up with a result quickly, learn what the root what the model result can be quickly. And then make a change and then make another change and make another change, make it better. If you plug your data in and spit it out at the end, it could take three days, 3 hours, whatever it is, to model that data. And at the end, if it’s not very good. Maybe that’s time wasted. So, understanding how to segment and how to batch that data and get those results quickly. To really put your computer technology to good use. That is a key tool, the key concept which I’ve learned to use throughout my degree, so that’s come in handy for myself. And, that’s what I’ve learned to use throughout this degree.


And for me I’m the opposite to Chris. I didn’t go out and buy anything new. I’m using a MacBook Pro, that’s like five years old. And yeah, through all the techniques Chris has talked about, I’ve been absolutely fine.


So, to summarise that the hardware that you own doesn’t matter too much, because for things where you need to, you know, high power computer, there are options available and you will learn about those options. And for most other things, just to, you know, the kind of computer that most people will hopefully have, should be fine. In terms of the programs that you’re using, we mentioned a few earlier, so R and R studio, Python, and I don’t know if you’re using an IDE for Python. We will show you how to use Tableau a little bit and you can access that for free with the student license. And then it will really depend on which subjects you’re doing and what things you choose to investigate more. What other platforms and programs you decide to spend time on.


May I just add Simon, for Python those subjects allow a lot of flexibility from the Jupiter notebook environment to Spyder to using VS code. R studio is one of the core ones we begin with. And then once you start branching out into your own degree, you can use pretty much whatever program that you want to use. So long as the assessments are submitted in the format that is specified. For example, in the deep learning class, the format that’s expected is a Jupiter notebook. So, if you do your modelling in a py file, make sure it’s converted across, etc. So that is fairly flexible in that perspective.


And the key thing is that you if you don’t know what any of those programs are, and you’re thinking, should I know these things? No, don’t worry about it too much, because you’ll be introduced to them through the course along with, you know, things like GitHub, if you need advice on working with databases, then as Rebecca said, there are courses available to do SQL, and, and so on. So, the idea is that we guide you through that. So hopefully that answered your question. If you have a follow up, then feel free to ask an additional one. And we’ve also got a question here about whether data science is a skill shortage in Australia. Obviously in the midst of The COVID-19 recession, which is impacting a large range of job sectors, I can give a really brief response to that, which is that we know our students have been in demand. And we know companies like working with our students, because they have this range of skills. And it’s not just about the sort of computational side of things, although that is important. But it’s also about can you explain why this model matters to our business? Can you decide which problems are worth exploring to our organisation? Because you can do lots of things with machine learning, but a lot of them are not worth doing. So, you need to be able to identify those things. I don’t know whether Rebecca and Chris or Tony have anything else to add on that question. It’s obviously a very important question at the moment.


Just anecdotally, I know from the LinkedIn alerts, I get on jobs with certain tags attached to them. By far, the largest number of jobs for the tag attached to data scientist in terms of what comes into my inbox, and it’s across a huge range of industries. I think most companies are just starting to work out that things can be done with their data to make their businesses better. So, one of the reasons that I decided in the middle of a relatively successful career as an equities analyst to throw it all away and do a data science degree is I see huge potential for this industry going forward.


Definitely can’t agree more. Me personally working in an industry that has a lot of senior managers who have started their careers as forklift drivers or truck drivers, talking to them about this model, or that data or this plot what it means to them. Perhaps it doesn’t always go down so well. Sometimes it’s a different language. And that’s fine, because it’s taught me how to phrase that information to a language that they understand a lot of gut feel based decisions that they have been making for many, many, many years. Should be data based decisions and not so much gut feel.


So, what is your sector I can’t remember.


I work for warehousing and logistics. So, I work for  3PL and we’ve got pickers, packers forklift drivers, truck drivers. That’s, that’s most of the industry. So, in terms of, you know, data science practitioners, few and far between. So, in my particular industry, there’s a massive shortage. But the managers wouldn’t say that there is a shortage. It wouldn’t be because of COVID-19. But simply because a lot of the people who are running the industry didn’t start off as analysts, like some people have myself included. So, there is a shortage there and how to address that shortages is a different kettle of fish altogether. If I may, one anecdote for what Simon was referring to before about machine learning and its applications within the different businesses. I attended a hackathon once where, of course you’re in various different groups. And over the weekend, we had to address a particular data set. And I won’t go into too many details, suffice to say, at the end, we had to present our findings. And of course, the MDSI students got up there and spoke about our particular model presenting what it meant to the business, how it was used, what it all means, etc. There was another particular group there a group of mathematicians, and they got up very proud. They solve the world’s problems. And I presented an equation on the board. And this equation was incredibly detailed. And they called it a magical equation. They said that this solved all problems. They said this is all you need, just this equation. all you need, just this equation. And the business was like, well, how do I use this equation? What does it mean to me? And they couldn’t answer that question. All mathematicians didn’t. They got up and they showed this fantastic this brilliant equation but didn’t really go beyond that. So, linking everything in together to present information back to present that benefit back to the business for what it means to them. That is, is what I have learned, amongst other things. That’s what I’ve learned from this degree.


Thanks, Chris. Tony did you have anything you wanted to add on this?


Yes. I agree with you all about the point that you guys about the future data science skills. Now because of COVID-19. I think, the way we live the way we work has changed to be more online. So, this so we could have schools online, where we collaborate with our colleagues online, this work generates even more people, more data. So, the need or demand for people who have data science skills can only increase because of COVID-19. So, I would say data science skills, we need more people with data science skills in Australia, and in the world.


Yeah. Great. Thanks, Tony. So that’s all of the questions that we’ve received via the q&a. So, unless anybody types very quickly, we will wrap things up there on behalf of the panel and the MDSI team, and Rebecca and Chris as well, thank you to everybody for attending. And on behalf of me, thank you to Rebecca and Chris particularly for joining us today and to Bec and Michelle who have been handling some things behind the scenes in our marketing teams which is fantastic. And we really hope to meet some of you next year. If you’d like more information, then you can see there’s some links on screen here. You can search for that video on The application address is there as well. And if you’d like to get in touch then it’s And I think we will leave things there. Thanks all for attending and hope to meet some of you next year.


Thanks, Simon. Bye


Thank you guys. Enjoy your night.