Discover Bachelor of Data Science

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Speaker 1: Hello. My name is James Harland. I'm the Associate Dean of Student Experience in The School of Science and the program manager for our brand new bachelor of data science. This exciting new program, combines computing and mathematics and is centered around the manipulation, analysis, and communication of data. While everyone now has lots of access to data, people with the ability to understand and interpret it are in demand across a variety of disciplines, which lead to stimulating and rewarding careers. It is my privilege to be part of the education community here at RMIT and to tell you a little bit about this program today. 

Before I start, I'd like to acknowledge the people of the Woiwurrung and the Boonwurrung language groups of the Eastern Kulin nations on whose unceded lands we conduct the business of the university. RMIT respectful acknowledges their ancestors and elders, past, present, and emerging. And while we conduct that work remotely, I want to pay my respect to the wider unceded lands of this nation. 

Data science is the combination of computing, mathematics, and application. So you'll see that very much in the structure of what we do in this program. To be able to manipulate data, the first thing you have to be able to do is get a hold of it. That's why the computing is there and part of that is also about information technology. What do you have to know about how to get the data, if you like. Once you've got it, you might have to change it, alter it, process it in some way and that's where the computing comes. So you have to a little bit of programming, but you don't have to be a master computer scientist. In order to interpret and evaluate the data, you do have to know a bit about statistics and mathematical concepts. 

So you need to be able to infer. What does this data mean? What can I conclude from it? So about a third of your time will be doing some mathematics and statistics. The other third of your time will be applying your knowledge in a work integrated learning context. So there's a fair bit of project work, case studies, and other material. That means you have to, even the second year of the program, start applying what you know in a very focused way. So we like to think this is a unique combination of mathematics, computing, and application. 

What is data science? As we've mentioned, there're combinations of computing, of information technology, of statistical inference, as well as just general business intelligence or understanding what is going on here. So you don't have to be a master of any of these fields, but you do have to be comfortable talking the language of business. What does this data mean? What might it predict? How might it work? You have to be a little bit savvy about the technology. What does it mean to be presented in a certain form or what are the limitations of certain technologies for getting certain amounts of data? But the two main areas are the computing, so you can manipulate it as you wish and then interpret it. And that's very much related to the structure of the program. About eight courses of mathematics and statistics, about eight courses of computing, and the other eight are project work and case studies. So truly about foundations, about application, and the combination of the two. So in your first year, you'll study pretty much a fixed set of subjects. We do require a certain level of mathematics so it'll make sure everybody's on the same level playing field at the end of their first year to do that. 

So when it comes to second and third year, in the second year you can branch out more into picking areas and start doing some of the case studies and project work. And then third year you spend around half your time doing that applied project work. So first year is foundational and the second and third years are very much applying those concepts in realistic work integrated contexts, 

Why data science? Well, data really is everywhere. If you want to interpret the latest climate change data or data about bush fires or about economics or about pandemics or about financial analysis and stock markets or anything else you start with the data. And what does the data mean? Well, that's also part of your role. So a data scientist is not a computing type. They need to know about computing, but don't have to know everything. Similarly about statistics. You don't have to be a master statistician, but you have to know enough to be able to make a literate argument and case for why, say, the prime minister might have a certain policy towards energy prices or on climate change or on bushfire management. 

So supplying that data so people can make informed decisions, that's the role of the data scientist. Now, given that data is everywhere, everyone needs a data scientist. Just about every company, every organization will need a data scientist to tell us what does this mean? So what can I tell from this information? To me, it just looks like a sea of numbers. To the data scientist, they can extract the patterns, provide predictions, make the data into information that can be used to make informed decisions. So pretty soon, everyone's going to have a data scientist on their team to tell us, what is the data telling us? What does it mean? So what should I do? That is the role that you will play once you are a graduate data scientist. 

Because we require some level of mathematics, we do ask that everybody entering this program has done some mathematics at high school. Here in Victoria, that might be further maths or math methods or specialist maths or equivalent if you're from somewhere else. What that means is in the first year, if you've done sufficiently advanced maths, you'll get one free elective. But if you haven't, we'll use that same time to enhance your math skills so that everybody's on a level playing field after the end of first year. So from that respect, as long as you've done some mathematics, you'll be eligible to enter this big killer program. Now, a lot of people ask about eight hour requirements. At the moment, because it's brand new, we can't tell you what the eight hour is or will be. An eight hour, of course, is based on the previous year's entry to say, what was the lowest score someone needed under the BCE system to enter this particular program? We can't tell you what that is now, but I can tell you that we do want this program to be as flexible and accessible as possible. 

So as long as you've met the mass requirement, we'd like to think you can meet the requirements of the program. There's no need for interview or portfolio because it's basically about, can you handle the maths and the computing? 

Work integrated learning is something that's very big at RMIT. It's an important part of all of our programs. And as I said earlier, around a third of what you are doing will be project work or case studies. So we very much wanted to get our industry partners involved in this so that you can get an idea while you're in the classroom in your second or third year about what it's like to work in this industry. What sort of problems do people solve? What sort of data do they use? What sort of issues do they face daily? It's one thing to say, there's lots and lots of data we can manipulate. It's another one to say, here is the data. What do you have to do? It might involve cleaning the data. Not everything is uniform. [inaudible] what we call data wrangling, changing it for one form to another, perhaps formatting, perhaps different kinds of processing so you can understand it a little better. But those sort of perspectives only come from people working in the industry and our work integrated learning concepts will bring that into the classroom so that by the time you graduate, you will know exactly what you're expected to do and you'll be ready to work from day one, rather than having some period of transition. So our work integrated learning is a fundamental plank of this program. 

Well, just like a lot of technical fields like I.T. Or mathematics or computing, there's no real geographical constraint on where a data scientists can work. In fact, you don't even have to move anywhere. The data will come to you. So in that sense, being a data scientist is to be global. There are no jurisdictional issues like legal issues in different states or different processes in different parts of the world to worry about because, at the end of the day, data is data. So there's a lot of global opportunities wherever you may be and wherever you want to work in data. Everybody has data. Everybody needs data. So while we don't have any specific requirements for you to move overseas or travel during this program, we do believe that people who graduate from this will have a global outlook simply because data is everywhere and everywhere there's data, there's a need for a data scientist. 

As we've mentioned, there's a lot of different places data can be used. Certainly, we hear a lot of people talking about issues like food security in the world. Will the world have have enough food to feed everybody. Well, guess what. That'll require things like data about the rates of production, the rates of consumption, population growth, and other things. So if you want to go into an area, like will we be able to feed ourselves with our planet, there's a need for a data scientist there. When it comes to health issues, there's an awful lot of information we have about health. There's a big and growing field called health informatics and a growing one about the data science of health, or how does data drive better health outcomes? What is the most effective vaccine? What's the most effective treatment for any particular disease? That's a data science problem. So the career outcomes, if you've got data and you care about a particular large problem, like climate change or pandemics or any other issue you care to name, there is data issue in there and hence, an opportunity for a data scientist. So the career outcomes you're going to get are being that sort of analyst, if you like, that provides decision makers with important information. And that occurs all over the place in all kinds of different contexts. 

In terms of important dates, the main ones are the same as any other program when it comes to entering via Vtech or other such means. So there's no special dates for this program. It's the standard ones you would need to enter. That's about all from me today so thank you for taking the time to hear about the bachelor of data science. For more about this program, please see the RMIT website or context study at RMIT. Have a great day. And watch out for low-flying chocolate frogs. 

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