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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.
Lucas Tan: Good evening, everyone, and welcome to the Data Science and Innovation showcase. I'm Lucas from the domestic recruitment team.
Before we start the session. I would like to acknowledge the Gadigal People of the Eora Nation upon whose ancestral lands our CBRO campus now stands. I also like to pay respect to the elders, both past and present, acknowledging them as the traditional custodians of knowledge for these places.
So as we allow more time for attendees to join us online. We'll run through some tips for you to have the best experience for today.
Lucas Tan: So participants, cameras and microphone has been muted to maximize the webinar experience and please feel free to ask your questions. Throughout the session via the QA. Function at the 2 box at the box at the bottom.
Lucas Tan: and we will also endeavour to answer all of these questions at the end of the session.
Lucas Tan: so welcome. Everyone who just joined us at the data science and innovation showcase. I'm Lucas from the domestic recruitment team.
Lucas Tan: So in this session you gain insights into career options and developments throughout the discussion with our academic staff and industry experts within the learning design industry.
Lucas Tan: You'll also be inspired by personal stories from our alumni, and students. And apart from that, you'll also be able to discover our industry connections and how we tailor a program to meet industry needs as well as learning about our program structure, study options and including delivery modes.
Lucas Tan: So to kick off this session, I would like to introduce our amazing guests, for today
Lucas Tan: we have associate Professor and Tony Huang. So Tony is our current director of data, science and innovation courses at Uts
Lucas Tan: and an executive committee member of Uts Visualization Institute.
Lucas Tan: He's a data visualization researcher with expertise in visual analytics and human computer data interaction.
Lucas Tan: He designs visualization, user interface and interaction methods to combine data values with human intelligence for effective data, explore exploration, communication and decision making.
Lucas Tan: We also have Anthony here. So Anthony is currently the head of
Lucas Tan: had a data science at Lula, he has held a variety of analytics and managing directorial positions across her communications media and financial stuff during his career.
Lucas Tan: In recent years he also worked for multiple startups where he has the opportunity to be part the growth by implementing and deploying innovation into the AI based solutions. He's the author of several books on data, implementations, science, deep learning and reinforcing learning
Lucas Tan: is also the current senior lecturer of the master of data, science and innovation course by Uts and teaching multiple subjects of machine learning and deep learning.
Lucas Tan: We also have recently, tith is a distinguished alumni of the master of the science and innovation program at Uds.
Lucas Tan: So with a proven track record in the field, she currently serves as the data scientist as the Australian Taxation Office, Ato.
Lucas Tan: So now we have the introduction complete.
Lucas Tan: less stuff into an exciting world of data. Science.
Lucas Tan: So welcome everyone. Hope everyone had a wonderful day. Tony, Anthony, and ris welcome to the panel.
Lucas Tan: So just real quick. Would you mind introducing yourself so, Tony? Yeah, would you like to introduce yourself.
Associate Professor Tony Huang: Sure. Thank you, Lucas. So I'm Paul. I'm from uts a Td school. I'm the course director for Master of Data science and the innovation.
Associate Professor Tony Huang: And my research interest is the human computer interaction and the visualization.
Associate Professor Tony Huang: And I also teach visualization
Associate Professor Tony Huang: and also coordinate the internship subjects for the course as well.
Lucas Tan: Wonderful. Thank you very much for joining us, Tony. Can you let us know what data science and innovation program is, and who it is for and what basically makes the data science and innovation program at uts stand out amongst similar similar data. Science programs out there.
Associate Professor Tony Huang: Yeah, that's a very good question. So master of data, science and the innovation is for anyone who has a baton a degree to to study anything we needed through data science. So we teach
Associate Professor Tony Huang: technical data science skills. For example, machine learning, deep learning, digital engineering solicitics thinking, we also, use a wellness of china method to help our student to be
Associate Professor Tony Huang: innovative, to have a critical thinking. What make our program stand out? And there are many different ways one of the significant one is that we have innovation component as part of our
Associate Professor Tony Huang: program. So we not only teach our students technical knowledge and skills. We also teach our students some software skills, for example.
Associate Professor Tony Huang: team working
Associate Professor Tony Huang: and also communication skills. And also we share our students to have ethical thinking and critical thinking when they address data, science problems in jail would.
Associate Professor Tony Huang: I will give more details about
Associate Professor Tony Huang: the unique features of vm, tsi Natal.
Lucas Tan: Thank you very much, Tony. So let's go to Anthony. Welcome to the session. You the experts here. So would you like to introduce yourself and also question for you, after introduction in this rapidly evolving field of data, science? What most significant challenges and trends you have observed, happening in this industry so far.
Anthony So: Sure. Yeah. So yeah, I'm Anthony. I'm currently working as a full time head of data science, the company called Aula. And we're building AI services, using AI and and Ml for banks. So we try to predict their customer, turn and try to predict what the best lead to contact if they want to maximize the their services.
Anthony So: And I'm teaching as well at uts for the master of data science innovation. So I'm the senior lecturer for course, machine learning and the advanced machinery.
Anthony So: And back to a question, I think the biggest challenge is to stay up to date. So
Anthony So: things evolve very quickly. Yeah, in that field
Anthony So: there's new algorithm, new technology, new architecture. So
Anthony So: I think we need to be curious and still
Anthony So: be learning skip, learning along how the things will evolve.
Anthony So: and that will stay true for quite a while, because that technology is not mature yet. It's still
Anthony So: at the I would say the early stage giving a lot of promising result. But there's still a lot of things that needs to be invented and built.
Anthony So: Therefore, I think, yeah, having that open mindset to keep exploring. Keep trying, keep seeing what works. What doesn't work is very important.
Anthony So: then, in terms of trends we've seen in the last few months. It's a lot about large language model. So with Chp and Openai. So there's a big big focus on currently on natural language models.
Anthony So: But interestingly, I think the thing that we need to notice is that it's a very slow adoption by corporation for such technology. Right now.
Anthony So: There's a lot of hide. So there's a lot of you know. Give me Ca, tools here and there.
Anthony So: But the reality is that not many corporation has used it currently.
Anthony So: And the the reason why is becoming there are a lot of risk involved like data, privacy, security data sovereignty
Anthony So: which makes things bit more complex to handle, and therefore it's it's harder to implement them.
Anthony So: So I think that's a good segue to all master is. It's not about paying you energy for the sake of technology. It has to have a real benefit, a real impact on the business and on people.
Anthony So: and therefore be able to not only stay up to date, but as well understanding
Anthony So: how it can be used or how it cannot be used, is very important, so that can have a real impact
Anthony So: to the community.
Lucas Tan: Thank you very much. Anthony. Yeah. So in terms of like, oh, learning all of the stuff and implementing ris, you definitely will be the person that I'm asking, because, like, you are like the alumni here. So can I. Yeah, of course. First of all, introduce yourself. And then this question would be, so, can you share with us a little bit of your experience studying this course at Uts? And what did you enjoy the most in the data, Science and Innovation Program.
Reasmey Tith: Thanks, Lucas. So my name is Resna. I'm an alumni of the Mds. I started my journey in Data Science in 2,019,
Reasmey Tith: graduating in 2021 and ever since 2020, I've been working in various jobs in the industry. I'm currently a data scientist at the Ato
Reasmey Tith: And we definitely are implementing all of the things I learned during the masters in my current job.
Reasmey Tith: I'd say the biggest thing that I enjoyed out of the data science program ats was
Reasmey Tith: the ability to and the opportunity to collaborate with industry. So there's a lot of practical experience a lot of opportunities to network, collaborate and actually work on real world data, science problems.
Reasmey Tith: My biggest sort of
Reasmey Tith: learning or take away from it as well. Having worked for a couple of years now is
Reasmey Tith: that coding isn't necessarily the most important skill that you have to have.
Reasmey Tith: Data. Science has a lot of different components to it. Problem solving and communication tend to be, you know, the biggest things that
Reasmey Tith: you can take from whatever experience or background you have in whatever industry you're in. That you can take through into data, science.
Lucas Tan: Perfect. Thank you. Rosemary. So on that note, what are the emerging career? Opportunities and data science in your perspective?
Reasmey Tith: Yeah. Well, I'll I guess. Expand a little bit on Anthony's answer, because it's very much everything that he talked about.
Reasmey Tith: Large language, models a huge and and then gaining traction, especially in the public eye at the moment. And that's where a lot of the opportunities sort of lie even in my workplace at the moment.
Reasmey Tith: I'm in a natural language team and a big focus of our work is, you know, trying to
Reasmey Tith: work out how we can use large language models
Reasmey Tith: for good rather than able. And and how do we manage all the risks that are associated with.
Lucas Tan: Yeah, Anthony, do you have anything to add on to? Reshma?
Anthony So: Yeah, I think currently, what we're seeing is that there are more and more companies that implement model and put them into production. So it's more, less and less R&D type of activities where? And
Anthony So: not only for big corporation like Google, Apple or Meta. Now, more and more organization. Are we using this technology and anybody into the day to day activities or try to enhance our customer experience of customer satisfaction
Anthony So: which stands again to to shift
Anthony So: discussion and a narrative about business impact
Anthony So: rather than just purely energy and achieving high accuracy score high.
Anthony So: Helpful matrix is really what we can get out of these models, and how it can help the businesses or how it can help people.
Anthony So: So that's why we see the shift is towards more and more the end user. And therefore all this question that super important needs to be answered.
Lucas Tan: Yeah, thank you, Anthony Tony, of another questions for you. So how can people best prepare themselves for these opportunities in your opinion? And what skills and competencies do you think are the most important for data? Science professionals.
Associate Professor Tony Huang: Yeah, I think. The first, of course, take the master for
Associate Professor Tony Huang: data science and the innovation course. So that you have all. essential, no technical skills. And also some soapless skills required for for you to do data science job.
Associate Professor Tony Huang: also, you you want for you to be prepared for this course, it will be good if, as I said, you, you have a a baton a degree
Associate Professor Tony Huang: give me if I give you tomorrow have for a batnet degree you could have for relevant data science
Associate Professor Tony Huang: work experience, or that has relevant work experience, so that you have some understanding
Associate Professor Tony Huang: about data, how data will be processed and then analyzed.
Lucas Tan: Yeah, I'm muted.
Lucas Tan: So risk May and, Anthony, do you guys have anything that you think would be beneficial? For the others who would be preparing for this opportunity. And what has skills in your opinion? Into real world like experience, you think would be beneficial.
Anthony So: Sure. I I think for me, there's really 3 component. One is problem solving skills. As our resume mentioned, it's
Anthony So: he. We're trying to use some new technology to solve real problem, real business problem.
Anthony So: Right? So what we want is really to make sure that we have the understanding how to frame the issue, how to frame a project, how to manage it, and then use all the tools that we learn
Anthony So: with the with the master, and properly craft the solution to it.
Anthony So: The second one is
Anthony So: should be a a, no brainer for people that play with data is analytical skills.
Anthony So: So we use data to make better decision.
Anthony So: And that's not only playing with the data set, but as well
Anthony So: for us as a practitioner how to make good decision in the project, making sure that we are making the right recommendation. And so we need to use data to do that and prove that we are hitting the right spot and achieving the right result.
Anthony So: And then there's still something called skill that are needed. Because again, right now, the the technology is not mature. You still have to get your hands.
Anthony So: 30 Nope.
Anthony So: In 10 years, 20 years time, maybe that we less need to go to that level. But still, currently, we need to be able to be able to play and use the technology. So we still need to be hands on and have some practice with it.
Lucas Tan: Fair enough. Thank you very much. Me anything that you want to add on.
Reasmey Tith: Just quickly. I guess a short anecdote case in point. Is that
Reasmey Tith: yes, you need some technical skill just getting your hands dirty playing with data practicing, you know.
Reasmey Tith: On a frequent basis, we'll get you the skills that you need to to do it. And also, you know the technology is is evolving such that
Reasmey Tith: we are trying to create or or use AI to our benefit. Even as data science practitioners. So I am currently looking at
Reasmey Tith: finding a model that can generate code comments automatically for me, for my work. So it's a time saver. It's utilizing AI, and it's also helping me do my work in order to do that, though we have to, you know, have the understanding to verify that
Reasmey Tith: what model is doing is what we'd like to see is the outcome.
Reasmey Tith: So all of those skills kind of wrap up into this little
Reasmey Tith: example of how you would use it in day to day.
Lucas Tan: Yeah, thank you very much. Resmate. So now that we have, like a broader perspective with the data, science trends and insights. Tony, would you mind? Maybe sharing some of the best specific highlights or like features of our data science programs and also explaining how they designed to align with these industry developments. If that's the case, so feel free to share screens. And yeah, to talk about the course. Thank you.
Associate Professor Tony Huang: So let me share my screen.
Associate Professor Tony Huang: and I hope everyone can see my screen.
Associate Professor Tony Huang: So I will be very briefly to to talk about Mds site and also highlighted some unique features of our program.
Associate Professor Tony Huang: First, as you may already know nowadays, data is available everywhere in almost every discipline
Associate Professor Tony Huang: and the data. Scientists are required by almost every industry to process those data
Associate Professor Tony Huang: for business purpose.
Associate Professor Tony Huang: joining from finance, healthcare, manufacturing telecommunications.
Associate Professor Tony Huang: energy, retail name and field.
Associate Professor Tony Huang: Therefore there is a very high demand for data scientists in the market for data, analytics and the decision-making purpose.
Associate Professor Tony Huang: And then that is also why data. Scientists are among the top paying positions in industry.
Associate Professor Tony Huang: This, of course, is there for designed specifically for data science and the data. Science is a multi-disciplinary field
Associate Professor Tony Huang: requiring knowledge and skills from different disciplines. For example, math and statistics, computer science, business intelligence and a vision edition.
Associate Professor Tony Huang: Further, in order for data centers to process the data to insights from the data and make decisions properly.
Associate Professor Tony Huang: They were also needed to have a good knowledge of data, ethics.
Associate Professor Tony Huang: privacy, and the security issues.
Associate Professor Tony Huang: And because of the multidisciplinary nature of the data science, you will have more career options after you complete the course.
Associate Professor Tony Huang: In addition to traditional ones, such as a data analyst selected the teaching and a software engineer, you can also have options such as data scientists
Associate Professor Tony Huang: data, engineer visualization, specialist machine learning engineer, and so on.
Associate Professor Tony Huang: There are a number of data science course on the market. So why should you study data science at Uts?
Associate Professor Tony Huang: What make Mds, I different.
Associate Professor Tony Huang: Mds I is. Is the only transdisciplinary data science program in Australia that has creativity and innovation components.
Associate Professor Tony Huang: This means that we will not only teach you technical data science skills, but also train you to hybrid relevant soft skills
Associate Professor Tony Huang: through a range of in class and learning activities and assessment tasks.
Associate Professor Tony Huang: As a result, you will be able to approach data problems in creative ways.
Associate Professor Tony Huang: also from data perspective and from ethic and human senator perspective.
Associate Professor Tony Huang: Our course subjects are specifically developed for data science, and is regularly updated. To cave up with the changing needs of students under the job market.
Associate Professor Tony Huang: Not only that the closest objects and the development
Associate Professor Tony Huang: developing in collaboration with our industry partners and a delivery by current working professionals
Associate Professor Tony Huang: and our quality academic staff members.
Associate Professor Tony Huang: So after the naming process, you will have a lot of opportunities to work on railroad projects with actual datasets.
Associate Professor Tony Huang: So when you, when you are graduate, you are industry ready.
Associate Professor Tony Huang: Also, we understand that our students come from very different backgrounds with different linear objectives.
Associate Professor Tony Huang: So we have a flexible and a comprehensive course structure with a 2 year, one and a half year and a one year, 4 time course durations
Associate Professor Tony Huang: providing flexibility for you to shape your own data science path.
Associate Professor Tony Huang: If you are new to data science. You may take a Mds as a pathway into the industry
Associate Professor Tony Huang: by starting with the fundamental subjects first.
Associate Professor Tony Huang: but if you are already
Associate Professor Tony Huang: you already have some data, science, knowledge or work with children.
Associate Professor Tony Huang: You may also take in Dsi as a next step
Associate Professor Tony Huang: to develop more advanced and specialized data, science skills.
Associate Professor Tony Huang: industry, partnership and the engagement are a core part of the Mdsi program that is to prepare students to address complex their order issues
Associate Professor Tony Huang: through the industry partnership program. We developed the course with input from our partners.
Associate Professor Tony Huang: We invited working professionals to teach, and I give guest lectures.
Associate Professor Tony Huang: But I still don't have opportunities to work on railroad projects
Associate Professor Tony Huang: through our partnership program. You can build your professional network and the connections with our partners
Associate Professor Tony Huang: and connect with your fellow students.
Associate Professor Tony Huang: Many of our students are also professionals.
Associate Professor Tony Huang: You could also have internship opportunities to work under the office of our partners.
Associate Professor Tony Huang: such as a national heart foundation
Associate Professor Tony Huang: and the orange energy.
Associate Professor Tony Huang: Mds. Has been designed for working professionals, so our classes are usually held after 5 30 PM. In weekdays
Associate Professor Tony Huang: and during the day. On Saturdays.
Associate Professor Tony Huang: Mds. I. Is delivered a Poland and mode, which means there are classes holding on campus.
Associate Professor Tony Huang: We're solving to get an opportunity to network. And then from our academics.
Associate Professor Tony Huang: students are also expected to use online content outside of the class, to study, either individually or in groups.
Associate Professor Tony Huang: Most of our subject content and assessment tasks are project based with real data.
Associate Professor Tony Huang: This means that a student in the world created chain to effectively apply those so often isolated and notity points
Associate Professor Tony Huang: for railroad problems.
Associate Professor Tony Huang: They also have a worker integrated and lending opportunities. So internships and worker placement.
Associate Professor Tony Huang: authentic assessment has been useful for yeah. Mds, I. This means that assessment is based on student performance in applying what they have learned in class into assignment tasks.
Associate Professor Tony Huang: And there are no exams for in-house data science subjects
Associate Professor Tony Huang: wonderful subjects that are offered by other faculties.
Associate Professor Tony Huang: It is possible you missed the only need to say the initial exams.
Associate Professor Tony Huang: We also provide additional support for those of who need help. This includes you pass program.
Associate Professor Tony Huang: Your pass program is a design for peer learning.
Associate Professor Tony Huang: It is run by senior students for students, happy students.
Associate Professor Tony Huang: then also students, Microsoft, the team site and also select channels for networking
Associate Professor Tony Huang: Poe and the job information.
Associate Professor Tony Huang: As you can see on the screen. This is the typical course structure for 2 year program
Associate Professor Tony Huang: students are required to complete 96 quality points.
Associate Professor Tony Huang: Who can put it to the course.
Associate Professor Tony Huang: This includes 44 credit point, core subjects and 50 credit electives.
Associate Professor Tony Huang: The core subjects are a set of subjects for you to learn core data science skills.
Associate Professor Tony Huang: including, for example, machine learning algorithms
Associate Professor Tony Huang: and
Associate Professor Tony Huang: and also for electives. You can then more technical
Associate Professor Tony Huang: subjects, for example, deep landing, big data engineering and natural language processing.
Associate Professor Tony Huang: Students also learn to build railroad project. Digital buildings develop a center. The perspective under ethical thinking. On bigger data.
Associate Professor Tony Huang: our electives can be chosen from in house data, science, optional subjects, and also from course of faculty subjects.
Associate Professor Tony Huang: As I mentioned before, we have a very flexible and at the same time completely offensive course structure.
Associate Professor Tony Huang: with a total of 36 subjects choice
Associate Professor Tony Huang: to meet individual needs of our students
Associate Professor Tony Huang: with this flexible course structure, so that will be able to develop their own telephony, science interest and the expertise.
Associate Professor Tony Huang: Mds. Has a suite at the mission points.
Associate Professor Tony Huang: which are for 2 year, one and a half year and a one year durations
Associate Professor Tony Huang: depending on your qualifications and the work experience.
Associate Professor Tony Huang: You may be eligible for one of them.
Associate Professor Tony Huang: For the two-year course students are required to complete 96, quite a point to complete the course.
Associate Professor Tony Huang: This includes 44 credit point core subjects and 50 point electives.
Associate Professor Tony Huang: Accordingly, for one and a half year course, students are required to complete 72 credit point.
Associate Professor Tony Huang: where, for the one-year course, students are required to complete 48 credit points. To complete the course
Associate Professor Tony Huang: for more details. Please check our handbook
Associate Professor Tony Huang: online
Associate Professor Tony Huang: for other missing criteria generally. If you have a bachelor degree from any discipline with a Gpa. Of what at least 4 out of 7. You may be eligible for the 2 year course
Associate Professor Tony Huang: to be eligible for one and a half year. Entry. Your qualification should be from relevant fields, or you have at least a 2 year relevant work experience
Associate Professor Tony Huang: to be eligible for the one-year course you are needed to have underneath the bachelor level degree, you know, relevant field with a Gpa. Of Alan East.
Associate Professor Tony Huang: for out of a survey
Associate Professor Tony Huang: you are also required to have a minimum for 2 year, full time or equivalent part-time work in it.
Associate Professor Tony Huang: or data analytics fails within the last 5 years.
Associate Professor Tony Huang: you know. Addition to the 3 other mission points to our Mds, I. Course.
Associate Professor Tony Huang: we also offer 6 months graduated certificate and a one year graduated diploma in data, science and innovation
Associate Professor Tony Huang: and the micro credentials.
Associate Professor Tony Huang: Always one job, flexible learning options in data, science and the innovation allow you to focus on developing the specific skills you need.
Associate Professor Tony Huang: We, you needed him.
Associate Professor Tony Huang: We have 2 microfotential scores
Associate Professor Tony Huang: apply the data science for innovation and advance the data science for innovation.
Associate Professor Tony Huang: These are 2 critical in the online shorter courses, and it can be completed in 6 weeks.
Associate Professor Tony Huang: The credit point that you received from the micropotentials can be recognized. If you continue on doing the master level course.
Associate Professor Tony Huang: you may find more information about them from our Uts open website
Associate Professor Tony Huang: open to the uts. Do the etu
Associate Professor Tony Huang: daughter, EU?
Associate Professor Tony Huang: And I think this is all from my side. I'll hand over to
Associate Professor Tony Huang: Lucas.
Lucas Tan: Thank you very much, Tony. So he has some key information for our attendees today. So participant can as sorry participants. Cameras and microphone have been muted to maximize the webinar experience. So if you have any questions, please feel free to put it through the QA. Function at the bottom of the 2 bar.
Lucas Tan: So the next intake for our master of data, science and innovation would be spring 2024. So classes will commence on August the fifth and application deadline for domestic student would be the thirtieth of June, and as for overseas international students, your application deadline would be thirtieth of April.
Lucas Tan: And if you are an onshore international student, the latest that you can apply would be the 30 first of May.
Lucas Tan: Do. Note. Uts do recognize pro learning, and it it will be handled in a case by case basis after we have received your application.
Lucas Tan: So we also have. Some financial support. Options at Uts is available for Australian citizen. New Zealand special category visa holders as well as humanitarian visa, permanent residents.
Lucas Tan: If you are an Uts alumni, you will also be able to enjoy the 10% discount. And we also have scholarship available online.
Lucas Tan: So let's move on to our QA. Session. So once again, thank you very much, Tony and our guest speakers. Anthony, and reason for joining us today. So for for these
Lucas Tan: like AI stuff. It would be quite challenging from time to time. For different like experience. So risk may quick question for you. So what kind of industry, experience, and opportunities did you encounter while pursuing the Mds I. Program? And how did they enhance your learning? In that case.
Reasmey Tith: Yeah, that's a great question. So there was
Reasmey Tith: actually quite a lot. It was one of the reasons why I chose the Mds. I over some other courses that were on offer.
Reasmey Tith: A lot of the courses that I looked at.
Reasmey Tith: We're very heavily theory based, which is important to have. It's important to have a good understanding and the opportunity to learn the background knowledge that you need in order to
Reasmey Tith: to work in data. Science,
Reasmey Tith: uts does still offer subjects like that. But there are also.
Reasmey Tith: in addition to
Reasmey Tith: more practical components. So there was a couple of subjects where we actually worked.
Reasmey Tith: With industry. It's kind of like an internship. There might actually be internships. On offer. Now.
Reasmey Tith: where we had an industry partner. I worked with a a couple of different industries. So a smaller consulting company and I also worked with Nbn car on 2 different projects.
Reasmey Tith: I worked with peers. During that project as well. It was about a semester long. We had data. We had a problem solve. And we had a lot of autonomy and how we wanted to to shape that project, what we we research
Reasmey Tith: how we might approach the problem, and then we got to present our results back to the business. At the end of the project. So that was really quite an eye opinion experience for me. And not necessarily what I expected to get out of a master's degree. That was also quite a good leg up in terms of gaining the the skills that I needed to actually work in industry. A lot of data, science projects actually follow that same process. So we have.
Reasmey Tith: You know, we have to curate some data. We have to solve a business problem. And then at the end of the day, we have to make sure that
Reasmey Tith: whatever we experiment, or whatever results come out of it, we can explain it back to our stakeholders in a way that makes sense and
Reasmey Tith: allows them to make the right decision. Based on that that research at the end of the day.
Lucas Tan: Yeah, thank you. Recently. But some of it is like a lot of different stuff that you will learn as well. So, Tony, quick question. So how flexible is the teaching and learning in this program? And what's the recommended amount of time that students should dedicate each week to study and do research.
Associate Professor Tony Huang: Yeah. In terms of flexibility. We have, a number of option subjects under electives. So our solution to who could choose
Associate Professor Tony Huang: from a range of subjects based on their own learning objectives and also based on their background.
Associate Professor Tony Huang: So we do not fix
Associate Professor Tony Huang: our students choice into an equal number for subjects. We also allow our student to choose any subjects
Associate Professor Tony Huang: from across the Uts.
Associate Professor Tony Huang: For example, you will you? They want to focus on the data science agency, specific discipline. For example, in every country, you know, they could choose to subject from that faculty as well.
Associate Professor Tony Huang: So we offer free choice options to our students.
Associate Professor Tony Huang: In terms of amount of time they should dedicate each week, usually for 4 time students, that is about
Associate Professor Tony Huang: that is, 24 credit points. So that's 3 subjects in total. We recommend our student to dedicate about 15 h per week for for 8 credit points.
Associate Professor Tony Huang: Subjects.
Associate Professor Tony Huang: of course, the actual amount of time, and that is based on individual situations. For example, you've certainly don't wanna have much back on for for subjects this may need more time.
Associate Professor Tony Huang: Took it up a bit, but I give someone who already have some. You build into all knowledge for their subjects.
Associate Professor Tony Huang: He may need
Associate Professor Tony Huang: a lesser amount of time.
Associate Professor Tony Huang: And also you sort of need to dig deeper into into a specific subject who may devote more time into data subjects.
Associate Professor Tony Huang: So in general, it's just, it will be about 15 h per subject
Associate Professor Tony Huang: the way each week.
Lucas Tan: Okay, well, that's some some work to do. Thank you, Tony. Anthony. So a a lot of people might actually consider studying data, science. And they might also wonder whether is the right skills, or background for their course, because, like some of the students might be switching from another area. For example, business. What advice would you give to those individuals with varying backgrounds who are interested in pursuing this program?
Anthony So: Yeah, that's a good question. We got this question part often. Actually. So I'll definitely, I think that assigns the the place to to be. I think we have a shortage for the coming decades or that kind of skills and role. So that's definitely something. If you invest your time for the future. That's definitely future proof.
Anthony So: Then, usually, what we describe data science as a mix between 3 practice, coding.
Anthony So: math and business that come in.
Anthony So: And what we want is really to become a good data scientist. You need to be good
Anthony So: in all 3 and discipline. So you need to cover all of them.
Anthony So: then, doesn't really matter which one you start with
Anthony So: as long as you want to cover all of them.
Anthony So: So you may have some experience with coding. Then you need to focus on math. You need to focus on the business I come in.
Anthony So: You have experience working in the business already. Then it's maybe more of the math and the calling
Anthony So: at the end. It's really
Anthony So: we are helping you to recover your Blind Spot and make sure that you have all the skills and experience to become a good data scientist.
Anthony So: I know that a lot of people that feel scared because they have no coding experience. A
Anthony So: quite
Anthony So: you know, when you look at the some of the content online, they say I can't understand. I don't know how to do it.
Anthony So: Can I show you that? We have designed the course to help student to really smooth out the learning curve.
Anthony So: And I do have actually a lot of students that have no coding experience at the beginning of the program.
Anthony So: and by the end of it they are very, very comfortable in writing codes in training machine learning algorithm analyzing the result.
Anthony So: So that's I think the specifics of of that course as well is that we're not focusing on only one side, coding on math history, all of these 3 components. Again, we're here to help you to become a good data scientist.
Lucas Tan: Perfect. Thank you. I'm sure all the others will be a little bit more relief when they hit that coding is not compulsory. So risk may just quick question. So since you are graduates, and when you were studying this course, this did the program deliver online or face to face or like. Can you just like kind of like, share your learning modes? When you are students here.
Reasmey Tith: Yeah, the learning style for this course is definitely blended.
Reasmey Tith: So it suits a lot of different people who might have you know, a lot of different backgrounds. It it really suits people who are working. So I I was unique in a sense, as I started Pre Covid, and then kind of finished the degree during Covid. So I got to experience both on campus and purely online learning and everything in between so it didn't lessen my experience at all. Having all different modes. It actually complemented.
Reasmey Tith: My kind of work life balance really? Really? Well.
Reasmey Tith: and I think that's something that they've kept through, even to the delivery of the course today is to keep that flexibility in mind.
Reasmey Tith: I definitely recommend, if you have the opportunity to go on campus and and meet people. That's a really great way to network. There are some people I still keep in contact with who I was studying? During the degree as well. It's great for opportunities later on your career. But having that flexibility to to learn online and and do a little bit of self pace. Planning was really beneficial as well.
Lucas Tan: Yeah, amazing to hear. So, Tony, at the moment. From what you mentioned in the presentation is still blended learning. But would you say that like, for example, if there's some in person classes that students miss out? Some lessons would be recorded, or like lectures would be recorded.
Associate Professor Tony Huang: Yeah, this is a good question. Actually, we have for most students zoom before asking question whether our subject can be delivered online. I want to emphasize that the Mds. I. Is on campus course. We deliver our classes in classrooms.
Associate Professor Tony Huang: So although, on the other hand, the courses is delivered in blended mode, which means we have, we do have online content.
Associate Professor Tony Huang: That's our student needed to study either individually or in groups. But we do have classes on campus. When that is a classes on campus, we expect our student to come into the class zoom to interact, to interact with our lecturers, and also our students.
Associate Professor Tony Huang: Having said that
Associate Professor Tony Huang: we do have a lecture next who records their classes. So students, for example, use the counter attend. They can review the lecturers. But this is more of compulsory, so is complete. It depends on individual subjects.
Associate Professor Tony Huang: Some of of our subjects are very interactive recording does not really make much sense. So I so just to summarize Mds is on campus course. We expected our student to come into classroom
Associate Professor Tony Huang: when that was a class.
Lucas Tan: Yep, thank you, Tony. Anthony, you asked the senior lecturer. For some subjects here at Ucs. Could you share some examples of some practical learning opportunities or innovative projects that students enjoy the most in the program.
Anthony So: Yeah, sure. Between a lot of the the subject the assignment product based.
Anthony So: So you rework on a real data set that the lecturer has designed
Anthony So: and shared.
Anthony So: and therefore your exposure to the contacts to the industry, and some of the specificities of of the that problem.
Anthony So: and that's what a lot of students enjoy. So it's practical, but it's engaging as well.
Anthony So: Some may have already worked in the industry, or some may not, but I'm interested to get a a hand of it, and
Anthony So: what we see as well as that they, the students, tends to use that has a showcase in their resume when they graduate, or to find a job, then they will use and refer to the this project.
Anthony So: So they are proud of it the result that they got, and the numbers all the time that they pass on it. It's something that they really resonate with them.
Anthony So: Then we have other courses as well. Like. I love where you're going to work collaboratively in a group.
Anthony So: and you have to manage end to end project. So the assignment in the in the other subject. They are
Anthony So: quite a lot of guided. But I love it really. Here's the problem
Anthony So: now can be very creative. You can design your own solution, you can design your own approach.
Anthony So: and then you try your best to make that happen.
Anthony So: and we have internship as well other for students value that opportunity where we can partner with some companies that Uts has selected.
Anthony So: And where? It's a safe environment for students
Anthony So: to learn, but as well to be able to help
Anthony So: a company to deliver some, some outcome.
Anthony So: And most of the time is
Anthony So: some skills that they don't have in this kind of organization, and therefore they are quite eager
Anthony So: to learn with with a student. So it's great opportunities for for students to really get their first foot
Anthony So: in the industry, but having again a safe environment
Anthony So: where you have a bit of mentorship.
Anthony So: and some some advice as well from real professional. So that's what makes an Mds high degree quite quite interesting.
Lucas Tan: Thank you very much, Anthony. So we have a question from the audience. And I think this would be probably directed to Tony and Anthony. So this student is from the computer science backgrounds. Is there any mathematical or statistical theory imparted as part the Mdm Mdsi curriculum.
Associate Professor Tony Huang: this is a good question. We? So the focus of our course is
Associate Professor Tony Huang: is practical. We teach our students. The yeah, would practice data science practice. Having said that we do tg, service. for example, machine learning algorithm, so that tick
Associate Professor Tony Huang: background, but our focus is to go on practical aspect of data science.
Associate Professor Tony Huang: maybe. Eisen, you can talk about this. I need a bit more.
Anthony So: Yeah, absolutely.
Anthony So: As I mentioned before, there's a there's a real learning journey that has been designed in in Dsi, and we want to make sure that at every stage you're comfortable with what you learn.
Anthony So: So we try to be more practical at the beginning, so to make sure that you are happy to use this technology play with.
Anthony So: And so there's a bit less theory and more hands on. But as we progress.
Anthony So: we'll get into more and more into details.
Anthony So: Because we want to get you into the more advanced concepts. And therefore that's where we think that's the best time to learn some of this math behind the algorithm, because that will set how they learn, and therefore you will learn the limitation and how they are in which and they are good.
Anthony So: and how to interpret the result as well.
Anthony So: So it's it's a blend of everything. But
Anthony So: as you progress in the in the in the degree you'll be exposed to more and more complex
Anthony So: concept. And that's where we want you to be comfortable with some of the basic first, and then, step by step.
Anthony So: get into the more advanced
Anthony So: level.
Lucas Tan: Yeah, thank you very much. So risk me. So artificial intelligence. AI continues to play a pivotal role in data science. How do you foresee, AI advancements shaping the future of their science as a discipline. And what impact might it have on Job roles and specifically for you at the moment. At the Ato, right now.
Reasmey Tith: Yeah, that's that's a huge question. It's a very kind of like a
Reasmey Tith: philosophical Linkedin post sort of question. But it it's one, I think, that keeps coming back again because it's at the top of people's minds, you know.
Reasmey Tith: We're only just at the beginning of of the power of AI and and what it can do to to shape
Reasmey Tith: our lives in general. I, I feel like we've only really just scratched the surface. So the technology has been around for a little while hasn't really been widely understood by the general public. It's been held kind of just by, you know those experts? And now that it's kind of opened up
Reasmey Tith: we're really gonna see a lot of exciting things in this space. At the Ato in general, where we're trying to be across it all it is is moving very fast.
Reasmey Tith: We are following the trends very closely.
Reasmey Tith: And seeing you know how how people manage this space. That are being part of the team that that does some work with large language models.
Reasmey Tith: the the governance aspect of it, or the the rules around in which we place responsibility.
Reasmey Tith: That's kind of where most of the work is being done especially for the ato, you know, we wanna manage as much risk as possible.
Reasmey Tith: so that that space is where a lot of work is happening.
Reasmey Tith: We know that technology is there, and it's kind of humming in the background. And then when we're ready to explore it or or have those settings in place to implement it. Then we can kind of bring it out and and see what it can do for us.
Reasmey Tith: I feel like. That's probably where a lot of industries, even outside of government, might be looking at.
Reasmey Tith: yeah, I think governance is is going to be the first thing that that sets the tone for how AI takes off in the future.
Lucas Tan: Perfect mate. We have a question from the crowd. Want you to? Answer this, so can you elaborate more and share more of the real world projects or internship like opportunities in the program that can connect with students and within the industries, or like with companies as well.
Anthony So: Sure. So some of the assignment, for instance.
Reasmey Tith: Absolutely.
Lucas Tan: Yup, Anthony, you can go first, and we'll come back to you. Resume. Thank you.
Anthony So: So some example of real work products, we are trying to predict what will be the salary of graduate students. So you graduate one year to your time, you find a job, but to predict will be on your salary.
Anthony So: That's a real project that our Hr company has, or university has
Anthony So: we tried as well to put it turned for marketing campaign as well. Who is more likely to respond to an email
Anthony So: all for detection as well for banks. So that's kind of on some of the project that we have in the machine learning course.
Anthony So: And again, that's really coming from the industry. That's real program that this companies are trying to tackle right now.
Anthony So: and we embed that into the degree
Anthony So: internship. It depends on on on the partner can be a big company, small companies. I think currently, this semester. We do have some student that works for consulting company in analytics.
Anthony So: And I'd be more exposed to big data and email at scale.
Anthony So: So it really depends on on the company that the
Anthony So: you will be working with.
Anthony So: And but again, what we try to make sure is that you work on project where you can use your skills.
Anthony So: So it will be not something that's not related to the program. We really try to make sure that
Anthony So: you will
Anthony So: keep crafting your skills
Anthony So: and gain experience with this internship. So that's why it's good to have the Uts framework for that.
Anthony So: because we can help you to vet some of the internship and help you to focus on the right ones.
Lucas Tan: Thank you. Ris. Do you have anything that you want to add on as a student perspective.
Reasmey Tith: Yeah, just a a little bit to add on I suppose because there is a lot of projects that get curated. For the Mds side, you have a lot to choose from, and deciding on which kind of direction you want to take, which industry you want to work in is probably the the hardest decision you have to make as a student.
Reasmey Tith: In terms of the experience. Once you, you know, eventually go out and choose your project.
Reasmey Tith: No. 2 projects are gonna be the same.
Reasmey Tith: the the way you get to collaborate with your peers pull on their experience in in their own careers. To help you solve a problem is going to be the same. Whatever project you do in that collaboration, experience is is definitely going to be the same
Reasmey Tith: and then you get to learn a lot from all of the different problems people is solving from those projects as well, so I worked. I I did 2. I lab projects essentially
Reasmey Tith: so the first one with with a consulting company. They were very. They were quite a small company. They didn't have any data, scientists or data analysts, or any data team that existed at all. I was kind of like their first
Reasmey Tith: intern per se working with them. So I had a lot of fun, I suppose. Trying to understand. You know, the data maturity. The data storage was
Reasmey Tith: kind of in excel spreadsheets. So I had to, you know, do a little bit of rudimentary work to to put all that stuff together and come up with a result in the end.
Reasmey Tith: In stock contrast. My second project was with Nbn car.
Reasmey Tith: They had this whole external platform that they sort of rented out to us where we could securely access.
Reasmey Tith: A small portion of their data. We could do all of our work inside of this particular portal or technology and have everything locked down and secure. So they were very mature in terms of their
Reasmey Tith: data processes. And and you know how many people that had working in data science?
Reasmey Tith: So yeah, different experiences. But well, I learned lots from from both of those.
Lucas Tan: Oh, thank you very much. Chris may. So with that, as our final question, we'll basically conclude our session here. We'll quickly want to. Introduce you to this new booking consultation thing. So if you are a student that you want to enroll into the master of this science and innovation, feel free to book a consultation with our team to ask any further questions.
Lucas Tan: And firstly, thank you very much for submitting all the questions through the QA. Function? But rest assured, if we can't go through all the questions, we'll also be responding to all the outstanding questions via email, we also like to express our gratitude to our guest speakers for sharing their insights, their expertise into the latest trends in global science.
Lucas Tan: and as well as are highlighting the opportunities provided by our distinctive data science and innovation program at Uts.
Lucas Tan: we look forward to the exciting future that awaits our graduates.
Lucas Tan: For our audience here. We have our next intake in spring. 2024 so if you are interested. Please feel free to book a consultation with our team. Our team will be running intensive consultation. Week from tomorrow, the third of April to the twelfth of April. So if you have any upstanding questions, please feel free to scan the QR. Codes and book a session with us.
Lucas Tan: And yeah, please feel free to contact us on innovation@uts.edu.au if you would prefer to have any written questions. Instead.
Lucas Tan: we look forward supporting you. Your learning journey at Uts, and for those interested, please remember, application is now open for the April fifth, 2024 intake
Lucas Tan: you can submit your application via the Uts zoom Portal. We appreciate your participation, and we hope to see you at Uts very soon. Thank you very much, and have a wonderful evening. Thank you.