Register for an upcoming Data Science and Innovation information session (when available) or view a recording of our recent webinar.
MDSI events
Upcoming events
Data Science and Innovation Postgrad Insight Discovery
Discover more about the first program of its kind in Australia where data science, creativity and innovation are integrated.
Join us online and let the exploration journey begin.
2 April 2024, from 6:00 pm to 6:45 pm
Unlock new career opportunities or build in-demand skills, discover all the insights you need in the growing area of data science and innovation
Find out more about our Data Science and Innovation Postgrad Insight Discovery event
Online information sessions
Learn how you can gain valuable, in-demand skills in analysing, visualising and communicating data to drive business outcomes and generate data-driven solutions at UTS, Australia's #1 young university. Our online information sessions are presented by our expert academics and often feature current students and graduates who share their experiences of studying the course.
MDSI course information webinar
Want more information about the course structure?
Tune in to this recent webinar, as our academics discuss the MDSI in more detail. Find out where this degree could lead you.
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Madeleine McWilliam: Good evening, everyone, and welcome to the data science and innovation showcase. I'm Maddie Mcwilliam from the domestic recruitment team before we start the session. I would like to acknowledge the first nations, people of Australia, including elders, both past and present, as well as emerging leaders.
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Madeleine McWilliam: I extend this respect to the traditional custodians of the land from which we are hosting this event. Today the Gadigal people of the Eora nation. We acknowledge their enduring connection to this land, which has always been and always will be, aboriginal land.
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Madeleine McWilliam: as we allow a bit more time for other attendees to join us tonight, I'd just like to run through some tips that I have for you, so you can have the best experience. We've switched off your cameras and microphones just to maximize the webinar experience tonight. But we are very open and welcome to your questions, as you can see down below. We have a QA box, so please feel free to put any questions you might have, and we'll endeavor to answer all of these by the end of the session.
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Madeleine McWilliam: We'll just give it one more moment. See if anyone's going to join.
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Madeleine McWilliam: Well, welcome everyone who have joined us tonight for the data science and innovation showcase. I'm madting Mcwilliam from the domestic recruitment team to night. You're gonna gain insights into career options and development through discussions with teaching academics and industry experts within the learning design industry. You're gonna be inspired by personal stories from alumni and students. And you're gonna discover industry connections and how we tailor our program to meet industry standards.
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We'll also learn about program structure and study options, including different kinds of delivery modes. But to kick things off, I'd love to introduce our guests we have tonight, we joined by associate. Professor Tony Huang is he's currently the director of data, Science and innovation courses at UTS
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Madeleine McWilliam: and an executive committee member of UTS Visualization Institute. He is a data visualization researcher with expertise in visual analytics and human computer data interaction. He designs visualizations, use interfaces and interaction methods to provide data values with human intelligence for effective data exploration, communication
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and decision making.
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Madeleine McWilliam: We're also joined by Jared Wong tonight. He is the enterprise marketing data manager Thomson Reuters. He uses analytics to discover trends and insights in marketing and business data, and additionally, he serves as a casual academic in the master of data, science and innovation program sharing his industry insights.
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Madeleine McWilliam: We're also joined tonight by Resnet, who is a distinguished alumna of the Master of data, science and innovation program at UTS with a proven track record. She currently serves as a data scientist
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Madeleine McWilliam: at Duff, a financial technology startup. And so with all the introductions complete. I think we should kick into the exciting world of data science. Thank you. Everyone for joining me tonight. If we could start with just a brief introduction. And, Jared, would you like to start us off tonight?
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Jared Wong: So I did my Phd. In image processing undergraduate in mechatronics engineering. And now I'm in marketing analytics.
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Jared Wong: It's an interesting twist and all throughout the journey I learned different things that I can incorporate. In my day to day I left Academia to join the corporate world because of stability. I needed to start a family and I wanted to stay connected to dollar signs and the university to casual teaching. I will certainly go back one day to share my true wisdom. Close to retirement age. I have 2 beautiful daughters 6 and a
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Jared Wong: half, 6 months old, and 3 and a half years old. So I'm not getting enough sleep. So that's it.
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Madeleine McWilliam: And, Tony, would you like to tell us a little bit about your journey into data? Science.
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Tony Huang: Bethany? Hello, everyone.
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Tony Huang: so I'm 21. I'm from Utah school mentioned. So I'm a data visualization researcher. So I work with data, visualize data to get a sense of them. And also I teach data visualization subjects and also coordinates coordinate data, science internship
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Tony Huang: subjects at a TD. School.
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Tony Huang: So my research is mainly on
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frame and data, interaction and data visualization.
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Madeleine McWilliam: And Resme. How about yourself?
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Reasmey Tith: Everyone. My name's Res. I started my daughter journey with the Ndsi in twenty-nineteenth so I decided to have a bit of a career change. I formally did.
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Reasmey Tith: forensic science before that. So that was the beginning of my journey into data. Science. I started work in data science in 2020. So I went full in, you know, decided to do the Masters side to find work and actually started in the banking a retail banking industry.
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Reasmey Tith: So since then, since I've graduated the Mds, I I also moved into Startup. So that was my actually former position at at dough. So doing, some data, analytics and data science there. And now, I've actually moved to the
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Reasmey Tith: Ato. So I'm working now for government as a data scientist.
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Madeleine McWilliam: Great to hear what a broad kind of career field there are for data scientists. Tony, can you let us know what is data, science and innovation program. And who is it for? And maybe what makes the data science and innovation program at UTS stand out from other similar data science programs.
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Tony Huang: Yeah, that's a very good question. So the judges, master of data, science and innovation is designed for everyone who has a passionate qualification and one or 2 study data. Science
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Tony Huang: doesn't matter. You have already. I just want to advance your career in the field.
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Tony Huang: It is also for some one who does not have that analogy and want to change their career paths into the Palaszcan's field.
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Tony Huang: So there are a number of data science courses on the market. What make our
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Tony Huang: Both are different
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Tony Huang: that are different. That are a number of prospects that we, we make our system, for example.
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Tony Huang: Our course is the only transdisciplinary science course in OS China that has creativity and innovation
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Tony Huang: crown Poland.
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Tony Huang: We also focus more on not only on technical components, but also focus on teaching our students to have non technical, which we call software skills, such as ethical thinking, creative and
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Tony Huang: systematic thinking when dealing with data problems.
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Tony Huang: we also have a strong connection with the industry professionals and the industry planners we invite, we work with
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Tony Huang: our partners and working professionals to develop our subjects. We also offer internships, so that a student who can experience the dwell of the data science projects
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Tony Huang: in the workplace of our partners.
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Tony Huang: So all this will have. Have our students told them, practical, not only their their practical but also practical knowledge and skills. So when they graduate, they are industry ready.
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Tony Huang: So this is just a very brief point. I will talk about this in more detail later.
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Madeleine McWilliam: Thank you, Tony Jared. I'd love to hear a bit more about what's currently happening in the field of data science. So what do you think are the most significant challenges and trends you've observed happening in the Industry
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Jared Wong: Yup. To start this discussion, we really need to look at it from 2 perspective, whether you're looking at from the angle of startups or multinational, a large corporations so startups you would have some challenges with regards to whether you want to build versus buy a balancing budget and
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Jared Wong: and also speed to market. With large corporations. You want to be able to maintain platform and democratize. Ai to
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Jared Wong: A broad range of end users. So a lot of the challenge are tied to speed to market versus generating long term value. In data, science. You need to be relevant. So you need to start
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Jared Wong: sharing some proof of concepts. But proof of concepts tend to be.
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Jared Wong: Hmm.
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Jared Wong: be a place where people get over promised into the idea. You you see a lot of hype on social media. Certainly, when you go and try those data science products, or AI solutions you. You will learn that there are some limitations. So
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Jared Wong: the key thing is to be customer focused and driven by what the customer needs are and develop efficiency boosts for companies. So a company like Thompson Reuters, we position ourselves in terms of the reliable data sets that we have collected over a long period of time a lot of and
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Jared Wong: solutions have this limitation of not having enough granularity in their data to come to decisions. So you want to use content
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Jared Wong: that are validated and and reach and generated over time for any applications in data science. So more and more, we are seeing the value of data that organizations have. And
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Jared Wong: think of
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Jared Wong: these AI solutions like Jen, the popular keyword. Now, Jen AI. As a driver or co-pilot to a vehicle, so
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Jared Wong: you can have the best vehicle like you can have a super car, but if you have a new beyond the wheel you can hit and break records. So over time he may and now more and more organizations are focusing on upscaling their workforce in AI and Gene AI and developing tools to
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Jared Wong: do enable these workforce members to have efficiency boost in their day to day cause at the end of the day. It's about growing your organizations revenue streams. And how do you go about doing that you need to innovate. So you need create an environment where you have like a hackaton you need to create environments where you have training programs to live.
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Jared Wong: your workforce understanding of AI and data science.
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Jared Wong: for example, if you want to sell things better, the marketing and salesforce can do that because
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Jared Wong: they understand the features that are added into those products. Your employees can leverage automation and data science to build workflows that
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Jared Wong: that are automated and reduced. The hours and the time and cost savings can be better directed to other things.
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Madeleine McWilliam: Certainly an exciting field to be a part of Resney. Can you tell us a little bit about your experience and your journey at UTS? Maybe. What have you enjoyed the most about the Dsi program? And what was your biggest takeaway?
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Reasmey Tith: Yeah. So I'm going to grab a little bit from Tony's previous response and a little bit from Jared. Because it's actually a combination of all the above.
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Reasmey Tith: So the experience that I had during the nds I was fantastic, and it really set me up well to do well in industry and go out into the field even without a technical background, even without specifically a statistical background. Of all these things that you might think you might need in order to do data, science, as a career. So with the industry partnerships, with the close relationship that we have
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Reasmey Tith: to what's actually happening in the real world. Learning the things that you need, learning, the skills that you need from day one was absolutely one of the biggest takeaways that I had. The thing that I enjoyed the most actually was the community and the peers that I had going through the masters. So it's
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Reasmey Tith: pretty much process. The relationships the the friends you make and the amount of skilled people that you get to meet during the Mds. I that will help you throughout the whole journey, and make you just that
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Reasmey Tith: even
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Reasmey Tith: more confident or more experienced person by the end of the degree
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Reasmey Tith: as well as a
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Reasmey Tith: having that opportunity to go out and work in industry. I've actually seen everything that Darren mentioned in his answer. All of those different challenges are absolutely is what is being faced in industry. And I've also, you know, worked in a little bit of each of those different sort of environments.
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Reasmey Tith: I guess the additional ones I could add to it is that now that I've moved to a a government position.
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Reasmey Tith: There's quite a a fine balance to to meet. It's not necessarily
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Reasmey Tith: driving revenue in a company. It's trying to do the best by. You know the people of Australia, you know. How do we come up with solutions? How do we use our data in the best way possible. In order to improve the lives.
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Reasmey Tith: everyone there, sir.
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Reasmey Tith: Yeah. A lot of challenges to face. But it's an exciting time for data science.
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Madeleine McWilliam: I'd love to hear from all of you on this one. For people who are considering getting into data science, and
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Madeleine McWilliam: maybe what are some of the emerging career opportunities out there? Even tonight we've heard everything from startup to marketing to government. So maybe all of you could shed a bit of light on current jobs available or current or future jobs that you could see in the field. Maybe. Add, Tony, do you want to start us off on this one.
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Tony Huang: Yes. So with, once you finish the the master of data science, there is a range of precision. You can go for that include the data scientists.
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Tony Huang: machine learning engineers, engineer analytics. So all this actually emerging positions. Also, you can do some traditional position. For example, business analytics that is teaching data analyst. So anything that's you need to data will be relevant for your future career.
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Tony Huang: So that's that's in other words, that means the the qualification will open the door for you under the range of options.
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Tony Huang: For you. Yeah, III well, if that's specific, the positions to to our
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Jared Wong: other panels. Yeah, I can add a bit more, I think, an emerging one is prompt engineering. So how to design your queries in order to get the best answer. Generative AI solutions? You also have people that monitoring models that you create statistical models for drift.
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Jared Wong: So over time, your models become biased to a certain trend or it's not updated with new and fresh relevant data. So you need to be monitoring it and also updating your models accordingly retraining them. Additionally, you have this regulatory compliance gap that is to be filled. So if you come from a legal background, you've you would have known that blockchain was the
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Jared Wong: the hype before generative AI and
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Jared Wong: doctrines hype created legal and text professionals that are experts in regulatory compliance in those space. So you will see the same thing happening with Jenny I as well. And you also can be a learning designer or developer, like I said,
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Jared Wong: companies are trying to obscure their workforce. If you know about the the skill sets are necessary you can help create those courses for your team members to learn. I'll also follow up on Tony's point on data engineering. it's actually growing as fast as data science per se, because there is so much data being generated.
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Jared Wong: sometimes you need to find patents out of it, and you need to be able to compress that data but retain the patents within the data. So the data engineers help us create segments, subsets that we can use to run our data science modeling.
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Jared Wong: Lastly, a space that I'm also very
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Jared Wong: active in is in data visualization. We are seeing more and more people
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Jared Wong: more and more people being data savvy enough to create visualizations. But
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Jared Wong: when you have many people speaking many languages. And having different sets of tools you come to a point where you have disparity in terms of data. So you're not sure who is right.
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Jared Wong: And you you need to have a single source of truth. And we're seeing more and more teams working together to get towards that single source of truth. So it's big group that data signs where you work together a lot of people and have specializations in 1 one space.
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Reasmey Tith: Yeah, I absolutely agree with that. Before, I think you know, maybe 5, 10 years ago, people used to think you know, you needed this data science Unicorn, who knew every single part of the you know, data science process from
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Reasmey Tith: data mining all the way through to, you know delivery and product, I think less and less. That is the case. I think businesses are realizing that there are very specific skills that need to service each of those different components in the data pipeline and they're very important roles.
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Reasmey Tith: So I would think you know, in terms of opportunities. There's so many more opportunities that don't necessarily have the title data scientists in them. I think. Yeah, it would do you well to go out and research some of those data engineering positions, or even Ml, so like Jared said, prompt engineering does all of these new emerging sorts of fields that happening that don't necessarily have the traditional title of data scientists.
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Madeleine McWilliam: Hmm! And what do you think it is that people could be doing to prepare themselves for these opportunities, the skills and competencies. Maybe you think that are the most important for data science professionals to to have resume. Do you want to continue? I'd love to hear from you.
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Reasmey Tith: Yeah, sure. So yeah, I realize as I'm going through my career, that it's less and less important to have you know, the most up to date will be the most technically proficient person out there.
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Reasmey Tith: A really important part of the role is to be able to speak to people, relate to people understand what the problem at hand is, and really narrow down the scope in order to deliver the outcomes that people want. So
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Reasmey Tith: the ability to understand the technicalities of the process, the limitations of the process. And communicate that well to your stakeholders, to business, to to whatever audience. That you might be, you know, communicating to that's such a valuable tool for you to have. And that's what I learned a lot during the Mds. I when I went through as well. And going into industry, is that you know you don't have to necessarily be the best coder or the best
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Reasmey Tith: in in the class. But those other software skills are just as important as those technical skills as well.
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Madeleine McWilliam: Jared, do you have anything to add to that I completely agree. Like
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Jared Wong: we. I recommend like scheme reading a lot and understanding how those new techniques work. And when you have time, and when you have interest, then you try and learn how to use those tools. So
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Jared Wong: you can take small steps and then make sure you keep the momentum going, because there's certainly setbacks. And bear in mind,
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Jared Wong: being a scientist is about experimentation. So making sure you go through the motion of having a hypothesis preparing your expect experimental setup. Have your assumptions, and then you know, present and communicate your results, and go back and forth again. Certainly, learning how to collaborate is important using digital tools. So
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Jared Wong: you like, I mentioned earlier, you need to start to divide and conquer, because if you want to go fast you can go alone. You can. Certainly build a solution. But if you want to go far you need to go as a group. So build your network like Res May mentioned earlier. You, you people progress in their career, and those next to you can be future colleagues. They can be future employers or leaders. In in the workplace.
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Madeleine McWilliam: And, Tony, anything you'd like to add, with all your experience.
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Tony Huang: Yeah, III would talk about it. This is from the ending point
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Tony Huang: So before you, as I said, our course, offer a bunch of subjective electives for our students to choose from.
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Tony Huang: to be prepared. I encourage our students to think about. What your strengths what you are good at, and also what you want to be. In your career.
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Tony Huang: So different people have different skills. And they are good.
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Tony Huang: different points. So, for example, some people may be put out at the engineering, and then you could study more engineering subjects under. Some people may be good at. For example, business analytics.
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Tony Huang: you have a strong business background. You may continue on on this, choose more business, relevant subjects.
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Tony Huang: So once you know what your suggestions. And what do you want to be during the course you can choose? Specific the subjects so to form your own patterns, and then in pass.
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Tony Huang: And also remember, we also teach, we not only teach technical skills, but we also teach EU non technical and or soft skills.
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Tony Huang: for example, to China to have
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Tony Huang: better communication skills how to think to deal with data problems from ethic
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Tony Huang: and famous and centered perspective.
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Tony Huang: Yeah.
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Madeleine McWilliam: well, all fantastic pieces of advice and insight there, and really exciting to hear about the broader landscape of data. Science trends and industry right now. But I think it'd be great time to get into the specifics of the course that we have on offer. Dr. Tony Form, could you please share some of the specific highlights and features of the data science and innovation program. And maybe you could give us a bit of an idea how they align with current industry trends.
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Tony Huang: Sure. Iii actually prepare slides. So I will share my screen.
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Tony Huang: So Hi, everyone, I'll give you a brief overview and highlight some key features of Mtsi.
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Tony Huang: First, as you may have already known nowadays, data is available everywhere in almost every display.
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Tony Huang: When a data scientist is required by almost every industry to process social data for business purpose.
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Tony Huang: ranging from finance.
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Tony Huang: healthcare, manufacturing, tiny communications.
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Tony Huang: energy retail from them. I feel. therefore, there is a very high demand for data centers in the market for data, analytics and decision-making purpose.
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Tony Huang: And and that is also why data scientists are among the top paying positions in industry.
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Tony Huang: This course, is there for
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Tony Huang: designed specifically for data, science and data. Science is a multi-disciplinary field
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Tony Huang: requiring knowledge and skills from different planes.
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Tony Huang: for example, mass and strategyx.
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Tony Huang: computer science. business, intelligence and visualization.
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Tony Huang: Further, in order for data centers to process the data drawing inside from the data and make decisions properly.
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Tony Huang: they were also needed to have a good knowledge of data, ethics.
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Tony Huang: security and privacy issues.
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Tony Huang: There are a number of data science courses on the market. So why should you study data science at the UTS.
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Tony Huang: what make Mdsi different?
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Tony Huang: Mdsi is the only transdisciplinary data science program in Australia that has creativity on the innovation component.
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Tony Huang: This means that we will not only teach you technical data science skills, but also trying to have relevant surface skills
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Tony Huang: through a range of in class, many activities and assessment tasks.
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Tony Huang: As a result, you will be able to approach better problems in creative ways.
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Tony Huang: both from that perspective
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Tony Huang: and also from ethics and Hyung Center Perspective.
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Tony Huang: Our course subjects are specifically developed for data science and is regularly regularly updated to keep up with the changing needs of students
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Tony Huang: funded the job market. Not only is that
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Tony Huang: the course subjects are developed, but in collaboration with our industry partners and delivered by current working professionals
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Tony Huang: and our quality academic staff members
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Tony Huang: slow out the naming process. You will have lots of opportunities to work on railroad projects with the actual dialysis.
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Tony Huang: So when you graduate, you are industry ready.
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Tony Huang: Also, we understand that our students come from very different backgrounds with different linear objectives.
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Tony Huang: So we have a flexible and comprehensive course structure with 2 year one and a half year and a one year, full-time course durations
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Tony Huang: providing flexibility for you to ship your own parasite path.
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Tony Huang: For example, if you are new to data science. You may take a Mdsi as a password into the data science industry by starting with the fundamental subjects first.
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Tony Huang: But if you already have some data, science, loneliness or work experience.
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Tony Huang: You may also take the Mdsi as the next step to develop more advanced and specialized data science skills.
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Tony Huang: industry, partnership and engagement are a core part of the Mdsi program
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Tony Huang: that is to prepare students to address complex railroad issues through the industry partnership program.
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Tony Huang: We develop courses with inpUTS from our partners. We invited working professionals to teach and give guest lectures and students have opportunities to work on railroad projects
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Tony Huang: through our partnership program.
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Tony Huang: You can build your professional network and the connections with our partners and connect with your fellow students. Many of our students are also professionals.
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Tony Huang: You could also have intangible opportunities to work at the office of our partners.
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Tony Huang: Such yeah, as a national heart foundation and the orangey energy
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Tony Huang: Mdsi has been designed for working professionals. Classes are usually held after 5 30 Pm. In weekdays and during the day on Saturdays.
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Tony Huang: Yeah, Mdsi is delivered via a blended mode, which means that our classes hold on campus face to face.
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Tony Huang: where students get the opportunity to network. And and then from our academics.
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Tony Huang: students are also expected to use online content outside of the class, to study, either individually or in groups.
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Tony Huang: Most of our subject content and assessment tasks are project-based with real data.
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Tony Huang: This means that a student will get a trained to effectively apply those often isolated notity points
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Tony Huang: cause. We have all the problems they also have. Worker integrated and learning opportunities through internships and work placement.
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Tony Huang: Or, thank you, assessment has been used for Mdsi. This means that the assessment is based on student performance in applying what they have learned in class into assessment tasks.
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Tony Huang: and there are no exams for in-house hospital science subjects.
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Tony Huang: but for subjects that are offered by other faculties is, it is possible you may so now need to sit in exams.
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Tony Huang: We also provide additional support for those who need help. This include your pass program you pass program is designed for pure Nanny.
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Tony Huang: It is run by senior students, poor student, happy students that are also included in the Microsoft teams side
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Tony Huang: and also slack channels for networking theory and jobi information.
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Tony Huang: This is the typical courses jacket for the 2 year program. Students are required to complete 96 credit points to complete the course.
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Tony Huang: This includes 44 credit point core subjects and 52 credit point electives. The core subjects are set of subjects for you to name core data, science and skills
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Tony Huang: build railroad projects its students and develop a human-centered perspective. And the ethical thinking on big data
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Tony Huang: relatives can be chosen from in-house data, science offshore subjects, and also from course faculty subjects.
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Tony Huang: As I mentioned before, we have very flexible and comprehensive cost structure.
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Tony Huang: with a total of 36
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Tony Huang: subject choice to meet individual needs of our students.
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Tony Huang: With this flexible cost structure.
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Tony Huang: students will be able to develop their own data, science interests and expertise.
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Tony Huang: Yamagsa has 3 admission points which are for 2 year, one and a half year under one year, durations
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Tony Huang: depending on your qualifications and works to children, you may be eligible for one of them.
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Tony Huang: For the two-year course students are required to complete 96 credit point. To complete the course.
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Tony Huang: This includes 44 quality point core subjects, and 52 quad electives
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Tony Huang: accordingly, for one and a half year course student as required to complete 72 quarter points
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Tony Huang: where for the one year course, students are required to complete only 48 credit points. To complete the course
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Tony Huang: for more details. Please check our handbook online
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Tony Huang: for admission criteria generally genuinely. You will have a bachelor degree from any discipline with a Gpa. Of underneath 4 out of 7
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Tony Huang: you may be eligible for the 2 year course
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Tony Huang: to be eligible for one and a half year. Entry. Your qualification should be from and relevant fields, or you have other needs 2 year relevant worksheet fields
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Tony Huang: to be eligible for the one year course, you will need to to have other needs, a personal level degree in a relevant field
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Tony Huang: with a Gpu of, and is the 4 out of 7.
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Tony Huang: You are also required to have A minimum of 2 year, full time or equivalent part-time work to the buildings
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Tony Huang: in IT. And the data analytics field within the past
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Tony Huang: 5 years.
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Tony Huang: In addition to the 3 admission point to our Mds cycles.
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Tony Huang: We also offer 6 months graduate certificate and a one year graduate diploma in data, science and innovation
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Tony Huang: and microfinance. Our range of flexible learning options in data, science and innovation allows you to focus on developing the specific skills you need
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Tony Huang: when you need a gym. we have 2 credible 2 micro credential courses.
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Tony Huang: Apply the data science for innovation and advance the data science for innovation. These are 2 credit point online insure courses and can be completed in 6 weeks.
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Tony Huang: The credit point you receive from the micro credentials can be recognized. If you're continuing on doing the master lab course.
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Tony Huang: you may find more information about them from our yeah UTSa open website, open UTS.
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Tony Huang: Daughter Edu. Daughter Edu.
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Tony Huang: I think this all from me. I will hand over to Madi.
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Madeleine McWilliam: Thank you, Tony. So much for that. Before we kick into our question and answer section of to night. I'd just love to give a reminder to you all here that we are preparing for our next intake for autumn. 2024 classes will commence on February nineteenth.
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For those who'd like to apply domestic students you have until the 20 eighth of January. International students who are outside of Australia. Applications must be in by the thirtieth of November 2023, and international students who are currently in Australia. It's the fifteenth of December 2023. We do have financial support options at UTS.
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Madeleine McWilliam: There is all of this information is available online. If you'd like to go out and find some more details you can also schedule a one on one session with us here at the recruitment team to discuss your options in particular, to recognition of prior learning as well. And we'll jump into all of those details in a moment. For right now, I think let's get into our QA. Section for tonight.
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There's been lots of questions in the chat box, so please keep sending them through and we will endeavor to answer all of your questions just to kick us off resume. I would love to know what kind of industry, experiences, and opportunities you encountered while you were studying in the program. And how did they enhance your learning.
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Reasmey Tith: Yeah, I'll be happy to answer that. I guess I feel like I was one of the lucky ones I actually
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Reasmey Tith: found work outside of the Mdsr.
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Reasmey Tith: the kind of first interaction with industry that you do have as you're doing. The course is through the Ilab Capstone project. So that's where you're working closely. With a few particular companies that might have a data problem. That they work with UTS and the students to to allow people to to work on these projects. So
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Reasmey Tith: I was able to work with a small consultancy company, and also Nbn. Co. In my allied projects. And a lot of my peers actually went on to find work outside of those with those same people through the Ilo project
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Reasmey Tith: I found my opportunity a little bit later on, through doing the big data engineering subject. So the lecture for that subject was actually advertising for a job at their company. I applied for it, and I was lucky enough to to land that role.
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Reasmey Tith: I was also able to work with another lecturer from the Mds. I. Who is also an alumni. So they were part of the one of the very first rounds of the Mds. I then went out to industry and came back to do some some teaching. So I'm kind of following in steps a little bit where I've gone out. Gotten to work with them. And I've actually come full circle. And I'm helping out with some of the subjects in the MB site. Currently.
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Reasmey Tith: So there's, you know, a piece of opportunity to to make relationships and to network
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Reasmey Tith: to find some of these jobs that that are out there. on top of, you know, the tradition kind of going on going on Linkedin to find work.
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Reasmey Tith: So yeah, the opportunities are out there.
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Madeleine McWilliam: And Tony, you mentioned before that there are classes available after 5 30 pm. And on Saturdays, could you tell me a little bit more about how flexible the teaching and learning is in the program, and what's the recommended amount of time a student should be dedicating to their studies each week.
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Tony Huang: Sure, so as I mentioned. So our teaching is blended mode. So which means we have all classes on campus. We also have online content for students to study themself
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Tony Huang: or in Goroz.
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Tony Huang: So that means we do not have classes every week.
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Tony Huang: We have sessions on campus.
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Tony Huang: But we also have online materials for students from
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Tony Huang: in terms of
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Tony Huang: i
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Tony Huang: sorry. What was the second question.
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Tony Huang: Just how much time should students be dedicating to their studies each week. How could they balance work and studies? Usually. We require, you have about 12 to 15 h.
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Tony Huang: Plus objects per week.
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Tony Huang: But it's very depends on individuals. So if you're for specific subject, if you do not have much background, and perhaps you need more time.
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Tony Huang: But if you already have some knowledge and skills for subjects. Probably you need less time on the subject.
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Tony Huang: So in January is just 12 or 15 h per week per subject. But it already depends on individuals and your time, availability.
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Tony Huang: funer subject.
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Madeleine McWilliam: And those classes, Tony, are they delivered online or face to face as well, could you tell me a bit more about the delivery mode
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Tony Huang: the the course is the uncomfortable course so our class are delivered face to face
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Tony Huang: on campus.
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Tony Huang: But we do. we do have subjects or lectures
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Tony Huang: record. Their sessions are made available. So for students who occasionally can't attend the class.
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Tony Huang: We also have lectures who will connect to offer
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Tony Huang: life online options for their students.
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Tony Huang: So it really depends on individual subjects and lectures
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Tony Huang: in general our course is on campus, so we always teach our class face to face
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Tony Huang: on campus.
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Madeleine McWilliam: Thank you, Tony. And this one's for Jared, and resume. So people who are considering studying data science wonder if they have the right skills or background for the course? I know you've both touched on this a little bit tonight. But what advice would you give to individuals with varying backgrounds? Or maybe they're creative people who are interested in pursuing this type of program. Maybe, Jared, do you want to kick us off?
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Jared Wong: Yeah, I can certainly kick us off.
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Jared Wong: if you have a growth mindset. If you love to innovate. You wanna drive change. Are you willing to embrace change? I think.
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Jared Wong: this cost is for you, because I allows you to exercise those creativity and innovate. If you understand business gaps, you understand strategy. This costs will teach you how to learn the tools and then learn how to execute. Learn how to communicate, learn how to create products and productionize earlier rest may mention
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Jared Wong: up
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Jared Wong: how she got a lot of opportunities. I think it's just Testament to the soft skills that you will gain. But you also develop that mentality of being proactive being innovative. So I'll give an example for myself as well. I
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Jared Wong: see a lot of data scientists going to
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Jared Wong: snowflake conferences, Aws conferences, Google Cloud Conferences. These are the providers of data science tools. But when you go to those conferences is more like a marketing event where they're trying to sell you something what I did that is different is go to an event where I focus on customer experience because I'm developing a data visualization tool kit for all marketers around the world.
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Jared Wong: So I go to customer experience events to learn how to improve the customer experience of using my tool set getting those fresh perspective from
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Jared Wong: your peers in different industries. When you join the course that leads to innovation. So yeah. I also think that the teachers are great in the causing that they give you feedback and
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Jared Wong: you can use those feedback to work towards the direction you would like to. To focus on.
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Reasmey Tith: Yeah, absolutely. I agree with all those points, Jared. And just a couple of things to kind of extend on them.
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Reasmey Tith: is, you know, don't undervalue the amount of experience or background that you have already when you're bringing into the course. So the media of the course is that we have a lot of different backgrounds, different experiences. And you know what you know?
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Reasmey Tith: Maybe new to the industry. Maybe this problem hasn't been solved yet, but your particular background, you know, brings a a new perspective for a new answer. To the problem.
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Reasmey Tith: So yeah, you don't have to necessarily learn
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Reasmey Tith: something new in order to bring something to the table. Your current experience.
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Reasmey Tith: Whatever industry that you're in can can bring that for you as well.
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Reasmey Tith: The second thing, I think that is, a really good skill set is to always remember to be a lifelong winner. So you don't have to know everything. That's, you know, a certain point in time, in order to do the job. You'll always be learning as you're going along.
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Reasmey Tith: Just have that mindset of here.
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Reasmey Tith: Yes, I've learned this particular skill, or I've achieved this particular thing. I know this particular coding language now. But how can I learn some more? Or where can I go to for a little bit more information, or what new tools are out there that I haven't seen before.
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Madeleine McWilliam: Fantastic advice so for Jared and Tony as contributing academics to the course. Could you share some of the practical learning opportunities or innovative projects that students have enjoyed the most in the program?
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Tony Huang: Yeah, maybe I can against that on this. So there's a number of practical learning opportunities offered in our course, for example, internship or opportunities.
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Tony Huang: We have many industry partners who would make offer internship to our students.
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Tony Huang: and the student can apply for those opportunities
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Tony Huang: and also for our savages.
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Tony Huang: Well, every subject's assessment task is actually project based
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Tony Huang: that could those approaches could be offered by our academics
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Tony Huang: and also by industry partners and a student who work on this project, not only to be trained
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Tony Huang: for the subject to their learning, but also train them to have comprehensive and systematic, go
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Tony Huang: thinking.
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and to work on the whole data science project cycle.
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Tony Huang: And also we have a I live that offer capsule projects for our student working
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Tony Huang: in groups. Tackle on using drill the data set tackle on. there would us as problems and provide solutions and presence that resulted to the stakeholders.
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Tony Huang: So there's a. So there's a number of opportunities as long as our students are winning to learn. And there there is no limitations.
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Jared Wong: I think Tony covered a lot of the things I would like to cover about. Add to the answer. I think in relation to I labs.
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Jared Wong: we have a broad range of projects. So if you think government, you think corporations, you think startups? They all have different kinds of data for you to work on. And when students get together in group they are not bounded by
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Jared Wong: just one objective they allow to explore and come back with solutions that sometimes, impress upon the partners that we invite to to work with us as well. So
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Jared Wong: students can just
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Jared Wong: do their own path when when they work on these data projects.
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Madeleine McWilliam: And I've got a question here from the audience tonight. Maybe, Tony, you could answer this one for us. The student is asking, Am I required to have prior it? Coding knowledge also? Is there an age limit for learning this course and making a career change.
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Tony Huang: Yeah, there is no new ageing limitations for entry to our course to our program.
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Tony Huang: No, there is no requirement that you should have via it, or strategic lowity.
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Tony Huang: So we, our course is designed for everyone who have qualification. and we will have fundamental subjects
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Tony Huang: for those who catch up with the it skills and adolescence skills.
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Madeleine McWilliam: Fantastic. And I just have one last question for all of you tonight kind of a very topical subject on artificial intelligence. So as AI continues to play a pivotal role in data, science, how do you all foresee AI advancements shaping the future of data science as a discipline, or maybe the impact it might have on future job roles maybe resume. Do you wanna kick us off on this one?
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Reasmey Tith: Yeah, sure. So yeah, AI isn't
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Reasmey Tith: quite a popular buzz term. I think I think it always has been and there's, you know,
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Reasmey Tith: a little talk and a lot of fear around. You know what I mean, what it means to the drug market.
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Reasmey Tith: But in reality I don't think that's okay. So that's not the case anytime soon. You always need a a human at the center,
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Reasmey Tith: the the technology and at the center, the model the algorithm that is producing. You know, these decisions for you. Because without that you know, there's heaps of room for error.
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Reasmey Tith: With chat Gpt, you know, that's kind of more, the most widespread sort of exposure of
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Reasmey Tith: one part of what AI is.
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Reasmey Tith: And even though it's quite popular, and there's been a lot of uptake industry actually hasn't implemented
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Reasmey Tith: that technology at the same rate that has been exposed. Because there's a lot of limitations to it. In terms of you know, security. How
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Reasmey Tith: can you massage, or, you know, control the output of chat DVT. In order to serve your specific purpose?
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Reasmey Tith: A. And who owns the decisions or who owns the answers that that are coming out of the model?
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Reasmey Tith: There's a lot of different questions that we're we're yet to answer in in the space. So it's kind of an exciting time. Having some AI being exposed more publicly. But also there's a lot of issues to to go through. So
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Reasmey Tith: I wouldn't be too worried about
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Reasmey Tith: too soon.
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Jared Wong: Yeah, I'll agree with that, Lee, I think. If you look at the most advanced solution, chat, Gp, and
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Jared Wong: Microsoft is pushing out co-pilot. So the reason is called copilot is because you still need another person to drive things, and that's you. It. The differentiation in the future with AI is on those who know how to use AI and those that don't. So
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Jared Wong: in in this course, you learn how to get started really quickly. I think a lot of the barriers to adoption is people not knowing where to begin or hit a roadblock and don't know how to progress from that. So
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Jared Wong: Once you get past that, then you need to think
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Jared Wong: from the perspective of not having anything developed tied to a key personnel. So
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Jared Wong: you, we want to build
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Jared Wong: the workforce to be able to think with continuity and skill in mind. So if you're building an AI solution you don't want that
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Jared Wong: AI solution to be the only one that serves that purpose because of all the aforementioned problems, drift risk with regulatory
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Jared Wong: changes and compliance. You want to be able to continue innovating and find other AI solutions and innovate and and grow from then make iterative improvements as well. So
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Jared Wong: gone are the days. Where in your job? You're doing one thing, and you want to secure your job because it's not possible anymore. You, you need to start, have to have that learning mindset that was mentioned earlier. Keep growing your knowledge on AI and stay relevant with its growth.
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Madeleine McWilliam: Tony, do you have anything to add?
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Tony Huang: Oh, just a few points, I think, the the other 2 panel members.
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Tony Huang: have been well set on the impact of all. AI. So II think AI is powerful and also very helpful. It will have fundamental impact on how we work with technologies.
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Tony Huang: At the same time, as mentioned, we should not only just depend. Rely on AI
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Tony Huang: we should. That is always clearman.
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Tony Huang: involvement to be helpful we should ensure we use AI technology in ethical ways.
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Not a misconduct. Misuse it.
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Tony Huang: And for decision purpose. And we also needed to understand, how AI make decisions so that we can
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Tony Huang: and we can make decisions in informal, informative, different ways.
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Madeleine McWilliam: Well, on on that note, I think that brings us to the end of our session tonight. I would like to express my gratitude for our guest speakers for sharing their insights into the latest trends in global data science, as well as for highlighting the opportunities provided by our distinctive data science and innovation program at UTS. We really look forward to welcoming the future that awaits our graduates. For the audience tonight
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Madeleine McWilliam: our team is available to answer your post graduate study questions and we can schedule a one on one consultation with you to further chat about your learning options. So please feel free to contact us at innovation@uts.edu.au, and we look forward to supporting your learning journey at UTS as well, and for those interested in applying applications are now open
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for the February nineteenth, 2024 intake. You can submit your application via the UTS student portal, and we appreciate your participation tonight, and we hope to welcome you at UTS very soon. Thank you so much. Everyone. Thank you.