MDSI events

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 Euro nation, upon whose ancestral lands our Uts city Broadway campus now stands. I would also like to pay respect to the elders, both past and present, acknowledging them as the traditional custodians of knowledge for this land. as we allow some more time for our attendees to join us online. We'll run through some tips for you to have the best experience for tonight's webinar participants. Cameras and microphones have been muted to maximize the webinar experience. Please feel free to ask your questions throughout the session via the Q. And a function at the bottom of your toolbar. We'll endeavor to answer all of these questions at the end of the session. Okay, so welcome everyone who just joined us today for the data, science and innovation showcase. I'm Lucas from the domestic team. In this session you will gain insights into the data science industry, including the latest trends, challenges, and career opportunities. You will also be inspired by personal stories from alumni and academics. You will discover our program structure which focuses on industry connections and is tailored to meet industry needs. you'll be able to learn about the study options and delivery modes, financial opportunities, and more to kick us off with the session. I'm pleased to introduce our guests we have associate Professor Tony Huang. Tony is the director of the data, Science and Innovation course at Uts, and a member of the Executive Committee of the Uts Visualization Institute. He specializes in data, visualization, research and with focus on visual analytics and human computer interaction. His expertise includes designing visualizations, user interface and interaction methods that integrate data values with human intelligence for effective data, exploration, communication and decision making. We also have Anthony. So who is the head of data science, and at Ilula, where he has held various analytics and managerial roles across telecommunication media and finance. In recent years he also worked with multiple startups contributing to grow by implementing and deploying innovative AI based solutions. He's the author of several books on data, science, deep learning, and reinforced learning. as he also served as a senior lecturer for the master of data, science, and innovation at Uts teaching subjects in machine learning and deep learning. or that Patrick is the director at AI for projects where she leverages her extensive expertise in project, management and consultancy to enhance decision making through AI machine learning and data, science renowned for her influence across various portfolios and programs. She plays a pivotal role in driving organizational success. A 2023 alumni of our amazing master of program that combines practical experiences with academic insights to deliver innovative solutions and fostering growth. We also have city. So City is a data scientist at optus for over 13 years of experience in it, and data science as well as an angel investor with inflection points, ventures. She supports startups in sectors, such as cybersecurity. Fintech, Ed. Tech, AI and Fmcg driving innovation and growth as a Uts alumni from 2020 one's cohort city excels in applying entrepreneurial strategies to achieve impactful business outcomes with our introduction completes. Let's delve into the exciting world of data science to kick us off. Thank you very much for joining us everyone, Tony. given your trajectory in data science, both in Australia and abroad, can you please share a little bit about your thoughts on why data? Science is one of the fastest growing fields worldwide. And what attributes do you think someone should have to pursue success in this field?

TONY HUANG

Yeah, that's a very good question. There are a few key reasons for this growth. First, st data explosion. Every day because of digitalization of our activities. We generate a massive amount of data through social media and networking devices or online transactions. Companies are eager to make sense of this massive data set and to make informed decisions. The other reason is, the technology is advanced in everyday with improvement in computing power and also storage. Those data can be stored for a very long time. And also that is software. Well, tools that are also available. Allow us to make sense of this data. Set and make this kind of sense, making process easier and quicker. The 3rd reason is it's okay that now nowadays, more and more business recognizing the value of data driven decision making process data science help business to optimize operations enhance customer experience and uncover new opportunities. Finally, data. Science is a versatile field nowadays. Yeah, almost every industry produce data, and they need someone to analyze those data and make sense of them. So there is a high demand. And there are many plenty of job opportunities for data scientists in terms of what attributes should someone have to succeed in this field? I, yeah, there are some key attributes which are important. The 1st one of course, we would require you to have some basic qualifications or use children's so that you have high chance to succeed in in data science course. There are also other what do we call soft skills or or and harder skills? For example, if you have a strong analytic skills that will help you to interpret complex data and your meaningful conclusions. The other. Another attribute is a technical proficiency, which is also important. If you are familiar with programming language like Python or R, and also know, have knowledge about database or machine learning models or frameworks. and then there will be set you apart from others. The 3rd important attribute is that you will have so the foundation knowledge in statistics that I will be very helpful because statistics is actually the basics for all machine learning or deep learning or artificial in intelligence models and technologies. This knowledge will help you to make informed decision based on the data in terms of software skills. A problem solve solving mindset is essential because data science data scientists often make decision based on ambiguous challenges. So this require you to be creative and have a critical critical thinking. Communication skills is also important. You needed to be able to convey your findings to non technical stakeholders or managements clearly, so that they can understand and make decisions. Lastly, commitment commitment to continuous learning is also vital. Because data science field involves quickly. And if you stay up to date with new technology and and techniques that will help you to stay updated. And also meet the challenge of the data science problems. Yeah. And this is what I have now for this question.

LUCAS TAN

Perfect. Thank you very much, Tony and Anthony, thanks for joining us tonight. So in the rapidly evolving field of data science, what are the most significant changes and trends that you have observed in this industry, and particularly in Australia.

ANTHONY SO

Yeah, sure. I think the trend is our own generative. AI has been there for one or 2 years now, and we're seeing more and more use case where industry and companies are trying to use. This technology. I think the the biggest challenge right now is to really get benefit out of it really proved as a actual usage where we can really benefit from this technology and as well that one of the biggest challenge as well, that slowing down the adoption of that technology is about data, privacy, transparency of that kind of model. big companies tends to be more cautious about the risk. So it's not about having this new technology. It's cool. It feels like it can do a lot of things. It looks very small, but the reality is, you need to look at what that's the limitation. What are the case where the model will make incorrect collection, or even send incorrect or invent incorrect answers. because that's the one that have the biggest impact on the business on people on such society. So I think we passed that at the early stage where there was a lot of hype, and you know a lot of misconception of what the things you can do. And now we start to see actually. 3 asking the right question about it. How can we put the framework behind it? How can we complement it rather than oh, let's adopt the technology without any thinking. And let's go crazy. So overall, I think that's will be always the case. So right now it's generative. AI. But in one or 2 years time we may have a new model, a different type of of architecture. and we'll get again to the hype. I think I believe what we need to do as A AI protection or data scientist machine learning engineer. because we are the expert, we are the one that have the responsibility to be able to translate that back into business term business outcome and as well explain, limitation isn't about setting the dream. It's our having the right impact on the business on people, on user or on the community. So that's something that we need still always to be mindful against, not only a technical problem, it's actually impacting people's life. And that's why we need to make the right decision. And we need to impress that responsibility.

LUCAS TAN

Perfect. Thank you very much, Anthony. So with your experience as a uts alumna, and as the director of AI for projects. How do you see the rapid advances in artificial intelligence machine learning and data science and impacting organizational decision making and also based on your experience in the field? How do you think the demands of other professionals will evolve over the next few years in Australia?

ODETTE PATRICK

Oh, thank you, Lucas. for context, I think in the past data and the output of data analysis were tightly held within a department in an organization. They're often difficult to access. expensive to store and manipulate as a business end user, which what I was most of the time before I started my master's trying to make decisions. You were constrained by available data, the existing models, reports. and the specialist technical expertise that you could access any request for additional information or changes to reports was very expensive to resource, and it also took a lot of time. Sometimes, by the time you got an answer. The problem was gone, and the data was very old and decision makers have had very limited choice in the data and the analysis available. I think today it's almost the opposite. There's a lot of data available. Storage and analysis are relatively inexpensive. The challenge decision makers fight face now is having data, professionals or professionals on their team that can translate their business needs. We call it the why. what problem are we trying to solve? And you need to find the right data, use the most appropriate analysis technique and be able to interpret the output in in the context of the business decision that you're trying to make. and then you need to be able to iterate being able. Maybe you get an answer. But then you think, oh, actually, we need to go back and repeat this cycle and have a look at it again until we're satisfied that we really have covered everything. and that we're not making assumptions, or we're not interpreting the output incorrectly. So I think the demand for data pre professionals is and is and will continue to increase exponentially. And I'm seeing that all the time there will be one or more in every department in an organization. And we're seeing that already in some of the big financial organizations, such as Banks.

LUCAS TAN

Perfect. Thank you, Siddi. Can you tell us a little bit more about your story? And so what's your experience like being a student at Uts. And what did you enjoy most being in the Dsi program? And what might be your biggest takeaway.

SIDDHI AUTI

Thank you, Lucas. So I had few years experience in the data world, but mostly in data, warehousing and data visual before I joined uts. So I was on the lookout for the course which will complement my experience and help me learn data science through industry, relevant projects rather than just textbook based approach. And then I came across this Mdsi course online. And that's how I got interested. And that's how I joined. Then Mdsi also gave me an opportunity to choose from wide variety of elective elective subjects rather than just the main subjects. Though main subjects prepared me with the foundation of machine learning and data science. The electives gave me opportunity to choose what I like most from the data, science industry, and at the same time Ilabs. We had 2 ilabs which gave us opportunity to work with industry partners, and, you know, get to work on the real problems, work with real stakeholders and understand the Australian data science market as well. So that was another interesting thing. Oh. from what I like the most is one is Ilab, as I said, but at the same time, we also got opportunity to work with like diff classmates from different different backgrounds. And it wasn't just like, okay. Everyone was a student and studying for the 1st time, but there were some from the finance background. There were some from cyber security background, there were some from some other backgrounds, and that cross industry, experience, and exposure that was something that enjoy. I enjoyed the most as well.

LUCAS TAN

Yeah, definitely. Well, industry experience would be the key to carry on with the career. So Sadi and all that. So how did the Uts data science program prepares you for the challenges you might face or face already in your career. And any specific skills or experience that you gained from a program that has been particularly beneficial in your role in the sector. Right now. maybe stuff from you against city.

SIDDHI AUTI

Sure. So From my latest experience, I would say that we have lots of data coming at us, and we have to choose the right questions and right answers. For, based on the data that we have. we have to choose where we are going to get the right, Roi, rather than you know, just getting the data and throwing some outputs at the stakeholders. So that's something that uts gave us like problem solving, choosing the right questions and choosing the right answers, and at the same time looking at this data from the ethical perspective. If what I'm doing is going to give the right ethical answer rather than just showing something and making up something. So that was one thing that Uts prepared other. Apart from the technical skill set and technical skill set. I feel like nowadays. We can. You know. we can keep learning, and still we will be. Probably, you know, we will be in the need to learn more. But the basic foundation that Uts gave us that was something more important to me in this world problem, solving ethics and choosing the right question and right answers. That's something I would appreciate.

LUCAS TAN

Yeah, fair enough. So true. What about you? That's what about your experience? Has it been a little bit similar or completely different?

ODETTE PATRICK

Well, there's I, I think, City and I have got a very much the same view on a lot of this, and I'm probably I'm going to cover a lot of what she said, probably just from a different angle. You know, I think uts data. Science covers all data science disciplines. So if you want to specialize in a particular area, you can, such as data, engineering visualization or deep learning. In my case, I needed a generalist sort of more global view. I needed to understand enough about all the data science disciplines to know what good luck looks like. You know how to deliver certain projects myself or with the discipline specialists. I think one of the most valuable experiences gained in the Uts program is knowing how to deliver an outcome or a project from the very beginning, which is basically what cities also said, you know, you've got to understand what the problem really is. You've got to find the data, the right, the best data. You've got to clean it, which usually takes 85% of the time. But that's a real world problem. And you need to know how to do that. And of course there are. You know, there's there, you know. There are improvements to the types of programs that can help you clean it. You need to work out what is the best type of analysis for the particular audience. So sometimes you need to be able to explain how you came to the result. So how the model works, and then other times they'll just accept a black box result. And it really depends on your stakeholder. And then also how to present the insights to non technical team members of which you will have a lot of. And and yeah, essentially, the course does all of that for you. You know. And business stakeholders, executives are not usually interested in. You know how you found the data, how hard it was to clean it or every modeling technique. They just want to know what you found the limitations, assumptions. or anything that could be that is unclear in the results. And they could, if that if they take that insight there is a risk that they could have, it could have a negative impact on their decision making. So you really need to understand from the very beginning of the process all the way through, so that you can provide your business stakeholders with all of that information. And I think that that's what makes this course really unique is that it gives you that experience.

LUCAS TAN

Yeah, definitely, with unique experience, will definitely be a good, I would say a checklist for getting into this degree. So, Tony, question for you. Is it true that individuals can still pursue a path in data, science, and innovation, even though they might not have that much of a background in data, analytics, or coding? And how can they best prepare themselves for these opportunities? And what kind of skills and like, do you think that it would be important for data, science, professionals.

TONY HUANG

Yes, this is a question we often get asked it. And yes, it is true that individuals can still pursue a pass in data, science and innovation, even though they have a limited background in data, analytics, or coding. In general, basic statistics, quantitative and coding skills are helpful. But for our course is not required to enter the program our course is designed in a way that that anyone who meets the entry and entry criteria are able to complete the course. So our course is comprehensive, covering a lot of topic and also flexible. So we're, for example, we have up to 24 credit point for students to choose across uts for electives. So if you do not have basic statistics. If you think your your coding skills are rusty. You can choose electives, for example, program subjects or statistic subjects in your 1st year before you choose specialized data science subjects. So so our course is flexible for everyone to pursue in their own data science path based on their own backgrounds and their learning objectives. If you are new to data, science have no coding or data analytics skills, you can enter to our program to study, found. To start with fundamental subjects, then go on with specialized subjects. And if you already have some data, science, knowledge or basics, you can choose our more specialized subject or advanced subjects for your program. So either you have background in that sense or have no background in that sense, will prepare you to complete the the course. Also, there are also many online resources for self learning. And we also have. Our teaching is a blended method. So we have in on campus sessions. But we also have online sessions for those who need extra learning materials or for those who need advanced learning materials. We also have those online materials for those type of students to learn themselves. So yeah, doesn't matter you have or you do not have. Or so you will have amazing, amazing experience. for in this course.

LUCAS TAN

Perfect. Thank you very much, Tony. So anything that you consider what Tony just mentioned about how individual can best prepare for a career in data science. Could you please explain what this data science and innovation program entails and who it is designed for. And additionally, what distinguished the data, science and innovation program at Uts from other similar data. Science programs outside.

ANTHONY SO

Yeah, good question, Lucas. So usually, when we say, a data scientist needs to have a coverage of 3 main skills, one is about coding. So you need to be able to know how machine works, how to build program, how to develop it. Business acumen. So making sure that you can translate a business problem into a technical problem that machine can help you to solve finding as well. What's the risk? What are the positive and negative impact that require some analysis and ethical skills to deep dive into the result and the transit again into a business outcome. And then that involve a bit of math as well. Statistics probability, because at the end. under the hood, that's what the machine are good at. So I will say the other program will do the same. So they will cover those topics. I believe that they tend to focus more on the technology side. Well, we focus more on holistic view. We don't want you to become good in the technology or good in coding good in training model. We want you to be good in analyzing, understanding the limitation, understanding the negative impact of a model, be able to make the right decision to transform the data tune the model. All these things has to be embedded into one single approach. And that's what we at the with that master in these degrees can give you. That's what we call the transdisciplinary approach. It's not just a combination or addition of different skills. It's be able to embed all these skills into one single practice and create a new practice. because the reality is that this technology is far from being mature. Right now, when we say we split into 3 3 categories, we are just applying recipe from other fields. And I think that's that's wrong. That's we need to invent a new way of building AI, a new way of collaborating and getting results from AI. So that's what we focus on. We disagree. And what it means in practice is that we are focusing a lot on new. not on the machine. I like to say that we helping you to become more human and less machine. What other degrees tends to get you to more on the machine side you will learn a recipe, a to Z. That oh, your works all the time, and when you face the agroprime industry you realize that's not the case machine can do a lot of things, but they have a lot of limitation. I can give you a simple example is a machine, no matter how smart they are, they are still not able to do data cleaning. Yeah, we have the the stats about what is a data sense project, 80% is about data cleaning 20% is about mobile training. Why, if they were so smart, why, they haven't automated that part already. that part is actually because you have a lot of decision lot of options that require business understanding, getting information, communicating, asking questions to make the right decision. And that's what machine cannot do. They don't have that context. They cannot think that way. They can optimize and do a lot of calculation. But that's what we want to focus on with this degree is making sure that you understand what's involved. What are the responsibilities when you are working with this kind of technology in that kind of project? It's a lot of decision that you need to make. But how to do it is to use your problem solving skills, your critical thinking, your identical skills and help the business or society to benefit from from the technology. So I think that's very unique in all the university, and even not only in Australia across the world where we are re putting emphasis on new and be complemented by machine, not you to be almost controlled by the machine. I press a button, and something happened. The machine should be right. and I don't know why I cannot explain. That's not what we want you to to achieve. We want you to be in control. You know exactly why the machine is doing this. You know exactly why the machine is not going the right result or giving the right result. You are the one that in control you are the one that has that responsibility in summary. I think if I have to summarize in one sentence this degree. We are not here to help you to become a data scientist or machine learning engineer or AI engineer where he to make you a good data scientist, a good machine learning engineer, or a good AI practitioner. I think that's the main difference compared to other other degrees.

LUCAS TAN

Perfect. Thank you very much, Anthony. So before we continue with the panel and open the floor to questions from the audience, could you please share some highlights about the data science programs at Uts. I would love to hear a little bit more about the curriculum hands-on learning opportunities and industry partners, and how that all actually aligns into what is happening in the industry right now. So, Anthony, over to you, thank you very much. Alright.

ANTHONY SO

Can you? Can you see my screen.

LUCAS TAN

Yep, perfect.

ANTHONY SO

So I will start with. You have chosen the right field. having a career in data, science or machine learning, or AI is kind of future proofing your career. the domain, the demand industry, owning research in government for that kind of role will be increasing exponentially year after year, decade after decade. So again, that's we, I think, was 5 years ago where they said that the data science or the artist is the a success job in in the world, I believe it still is, and will still be for at least 2 or 3 decades. The other thing why, you are in the right place is because using data making a decision from data. it's applied in any industry, finance, healthcare, manufacturing health. telecom energy. So on and so on. Anyone generating a lot of data. Now, it's about, how can we help them to get the value out of it, get the benefit out of it and make sure that get to the next level of maturity in the data-driven decision-making process. As I said before, there are a lot of skills that's involved. More, the math side more on the coding or communication, business understanding. all goal is not just to focus on one or the other. You may start from a technical side, and therefore you want to cover the other one, or you're much more from the business side, and therefore you want to cover the other 2. It doesn't really matter where you start. because we know that we want to help you to cover as much as you can. But not only that. We want to put that transdisciplinary approach into it again. It's not about adding these skills independently. It's be able to merge them together. Come with a new approach that help you to manage such technology or such project. So again, that's 1 thing that's very unique. With this degree. it's that's the approach where we are refocusing on you and helping you to grow as a human, that going to use that technology and use machine to help you to get better results faster in a more automated way. So I already mentioned that transparency approach. I think that's really one of the the key differentiator against other other degrees and other university. The second point is. we have a lot of collaboration and partnership with industry partners. So, for instance, me, I'm teaching. But I work in this way. So I, my day-to-day job, where I am training models. I'm analyzing the results, making recommendations to business. And all these things, then, can be fed back to the the calls and help you to know what's the best practices that's happening currently in the industry. What are the the tools? What are the consideration that's happening. So you have kind of the best of the both world. On the Academia side, where you, you'll get access to the latest state of the art models or research papers as well how it's applied industry and how businesses benefit from it. We are big as well by on learn by doing. It's not about only learning by heart the theory. It's about how you can apply them, how we can get output, how we can get result of it. And unfortunately, I believe that's the best way to do it is really to try. Get your hands dirty. make mistake. but at least you'll get some running out of it. I believe that's the safest environment for learning. When I say you make mistake, I'd rather you make mistake during your studies rather than making mistake. When you're working in the industry the impact will be way way greater on the on the history side. You want to learn as fast as you can during your life as a student. Because that's where you're not going to repeat these issues. this, this prime later on. So that's what learning by doing means. then we have a lot of flexibility as well. So we have a different variant of the degree that can cater for a shorter or longer period of of studies and a lot of the classes as well are outside business hours. We can have your your day-to-day work or part-time job and be able to still attend the classes. We always always provide as well some online content where you can learn at your own pace and as well some from some of the the courses and activity they. They may have some class on Saturday as well. And finally, I think a big, big emphasis. As I mentioned, it's not only about energy, it's about you be able to make the right decision and be able to take into account some ethical concerns. So my recommendation with that model is to put it in production, or I believe it's not good enough for its reason. It will be focusing too much on that part of the cohort or the portfolio, or is going to you know. Be biased towards another group. That's only you can make that commission, not the machine. The machine can optimize. But you will be able to transit that back into a business problem and into risk and benefit. So that's why it's really about you making the right decision, you making the the right choices and the right recommendation and suggestion to the business. That's it. A lot of benefit. The key point is our partnership in going, having that link with the industry, making sure that everything that you have been told, or that you are learning are still relevant. When you're going to look for a job. there's some tools. There's some technology. There's some practices you will learn, and you will see straight away that you can. You'll be able to apply it when you get your 1st job in the industry. The goal is really. We want you to be as prepared as possible when you join. When you start your career as a data scientist or machine engineer or AI practitioner, what we want is help you to build those those expertise have those experience manage the kind of project so that you are confident. And you can showcase those skills when you're looking for a job or A lot of our assignment are product based. So you won't see exam, or a lot of quizzes is really we want you. We're going to give you some real problem or close enough to what you're going to see in the real world and try to solve it. solve it with technology, solve it with your problem, solving skills, your critical thinking. And so on. Yeah, I said, so there are some. Teacher that come from the industry, me included, we have more. So again, you have that additional benefit where you can ask more specific question about what's happening on industry, how such project will work. or what will happen if I make the decision in in that kind of environment. So that's additional benefit you got. And I said, as well. we tend to have classes outside of business hours. So then you're able to have your your work life and as well as study outside of it. And yeah, we have a lot of opportunity for you to practice with real world data. So we have. I labs, we have internship. Some of the assignment is really designed from actual use case, the data will be very similar. It won't be the actual data set. It will be very similar to what some other companies or some teams have been working on and try to solve using this technology. And the other thing I want to mention as well is that Uts provide a lot of support. When I mentioned that, it's probably the safest environment to learn every minute. It's not only you with assignment or with the content. We, the teacher or staff, are here to help. I tend to try to answer as many questions as as you are asking. but as well. Then we have additional support. For instance, you pass where there are student from a previous cohort that did the the course know the pitfalls know what are the best practice. or increase your chance of success in those courses. and they allocate time to help you answer your question, guide you towards some topics or some resources if needed. And we have a big community of students as well that helping each other. So we do have alumni. We have I said, students from previous cohort? The key thing about about this degree is about networking as well. It's not only you yourself in front of the computer. It's about learning with each other, collaborating with each other, because that helps you to gain new skills or as well be able to reflect and see what's your Blind Spot? And if things that you want to work more or new skills that you want to gain by seeing what others are doing. then how does it work? So what's the the structure of the course? So we have the standard master of data science innovation. So that's 96 credit points. it's split between core subject and elective. So the 40 credit point is for core subject. So that's subject. You have to do so. We have data science for innovation, statistical thinking for data, science, machine learning algorithm application. That's the one I'm teaching data position and narrative. And then you have an additional one called I lab, and you can take 3 different pathway. One is a capstone project in more real world project or internship. So you may find a partner. Actually, they have a data set that have a problem they want to you to work on or solve. Or you can go into the research project. So it's more about research and a review of literature come up with a different approach, come up with a different solution to a given problem. Then we have some elective that's more focusing on specific skills or topic. So we have python programming. So, for instance, if you're not very comfortable in programming, then you might be a good one to start with. So to get the exposure to programming and python. we have applied natural language processing. I say, there's a lot of hype right now with a large language model. So that's a lot of things that you can learn to see what's going on in the market. And with that technology. we have deep learning as well. So it's more technical one. It's really making sure that you understand the framework of neural networks and deep learning and how you can architecture the model and train it. Using that amazing technology. we have data center practice that's more focusing on additional skills that's involved. When you run a data center project. one sense, how to better manage your code, how to query database, how to make sure that you version all your project and build a data product. Then we have big data engineering. So that's more on the data engineering side, you're going to learn a lot about big data streaming. All the the current tools that are used in data, lake data, warehouse. artificial intelligence, principle and application. So that's more focusing on the reinforcement learning side of the AI field. And we have advanced machine learning applications. So that's the follow up of the machine learning algorithm and application. Where you're going to learn more advanced techniques, learn more complex model. but as well learn not only how to train a model, but how to maintain and manage a model once it's in production. There are a lot of Ml. Operation or Ml. Ops involved. and as well how to make sure that your model is consistent, consistently performing. And when you start to degrade, how do you make the right decision to retrain and update the model? And then we have other active from other faculties as well. So you're allowed to use up to 24 credit points on active from other faculties. So from the science faculty, from business school or engineering, and and it so they do have other electives that you are able to to enroll and learn from. So that was the the standard one. So that's the 1st 1st line. So the 2 years full time, 4 years, part time with 96 credit points. So there are specific admission requirements. We have a shorter version of it of 1.5 years, full-time or 3 years part-time. So here the the requirements are a bit higher. So we want you to make sure that you either have a degree that's relevant to that field. or you have enough years of experience. In that field. And finally, we have a new version of the master that's called the executive Master of Data science Innovation. That's a 1 year, full time or 2 year, part time. But this one is really focusing on currently practitioner in the field. They have management, experience or experience in any data related practice. So that's helped them really to get a bit more. A deep dive into that technology and be able to apply it in their environment. And then apart from the master, we do have shorter option. So we have the graduate certificate. In that sense innovation. It's a half year, full time. One year, part time. It's really helping you to get introduced to that field. be exposed to this technology that the that way of solving problems. managing ambiguity or uncertainty. Then we have the graduate diploma. It's 1 year full time or 2 year, part time where you'll be able to do more courses and really start focusing, maybe or specializing in some of the fields, for instance, data, engineering or deep learning. And finally, we have micro credential as well. So it's a very short course that run for 6 weeks. We have 2 courses. One is to apply data, central innovation. So it will introduce you to machine learning algorithm and how to use it, and the advanced one will go into a bit more advanced topics and more complex algorithm. So there's a website open dot Ets, edu.au where you can find more information about these micro credentials and the date of the next. the next next course. And that's it for me. I think that back to you, Lucas.

LUCAS TAN

Thank you very much, Anthony. So in terms of moving on before we move on to the Q. And A, and take any further questions from our online audience. I'd like to quickly remind everyone that our new next intake will be for the master of data. Science and innovation will commence class. On the 17th of February 2025, and application will close on the 26th of January for 2025 for domestic students, and late November to mid-december for international students. for both offshore and onshore uts. We do recognize prior learning, which will be assessed on a case by case basis. After we have received your application. We also offer financial support options at Uts available for Australian citizen, New Zealand, special category visa holders and permanent residents on humanitarian visas. Additionally, uts alumni are also eligible for a 10% discount. And we have scholarship available online that you can also have a look. So thank you very much, Tony, Anthony and our guest speakers, Adette and Sydney, for sharing your insights with us so far. And now we are opening up for a question and answer session with the audience. So for those in the audience right now, please feel free to keep on sending your questions through the Q. And A. Box, and we'll direct your questions through. So to get quickly started. Sadi. What kind of industry, experience, and opportunities did you encounter while pursuing the master of data, science and innovation program. And how did that enhance your learning? So far.

SIDDHI AUTI

So in general, throughout the course, all the courses used publicly available industry data sets. So I remember working on a huge data set of New York taxis. While learning big data engineering and a crime data set during my statistics project and as many assignments for group projects, it gave a team environment which helped to learn work, delegation and collaborative working as well. Not just that. But we also had 2 opportunities to work with industry partners in the Ilab projects where we worked with stakeholders. Some of us got opportunity to work from Industry Partners office in Sydney, and some were able to communicate through the weekly calls as well. We got opportunity to work on these problems from like start to end, from understanding the problem statement to getting access to the data from cleaning the data to building effective machine learning models and from developing meaningful insights to communicating them with the stakeholders. So it gave me a holistic experience of the data science world. And so along with the faculty members for these Ilab projects, we were also assigned industry mentors who are already working in the data science industry, and they helped us navigate through all the challenges that we faced during these Ilab projects. All of this prepared us to tackle the challenges in the real world, and also provided a good standout point on the Cv. As end of your interviewers. While looking at our Cv. They were more interested into the projects that we were during the ilab and like it helped us to, you know. Talk about our skill set more. Yeah.

LUCAS TAN

Perfect. Thank you very much. Tony. So what coding languages? Would be beneficial for someone who is looking into joining the program. Tony, would you be able to answer. Yeah.

TONY HUANG

And this is also a question. Often we could ask it. Yeah. So our course teaches python for data science. You can do python or R. But we. We usually teach both in our course. But now we are teaching python in all, almost every subject in the course. so you will have a passing program skills that will be very helpful but if you have other language, for example, Java c plus plus or r it's also helpful. Because all this just different language using Tiffany syntax. If you know one program language that will be easier for you to learn another one. So yeah, program language any program that we have. But for for our course, pricing is the one we will be using for almost every subject for the for the course.

LUCAS TAN

Thank you very much, Tony Adette. We have received a question from the attendees about how much coding is required to be successful in the data, science, industry, what are your thoughts?

TONY HUANG

Maybe. Well. Obviously.

ODETTE PATRICK

Press, sorry. So adept, please. Sure. Well. I didn't know any coding before I started the masters, so It was fantastic to learn to code, and I felt it gave me a great sense of confidence afterwards that I could basically take on anything because I could learn to code. And after that during my working career. I use use it a lot at the moment now, but, however, I don't need to go into, you know, great great great detail, and build lots of different models and everything. Because I have, I focus more on a domain area, and I will use other people to help me do the coding. So it's great. It's a really great tool. I understand that I use it all the time. However, I'm not a coding specialist. I am more a business specialist. So that's that's yeah, that's that's how I use my coding. Yeah.

ANTHONY SO

We emphasize that right? So again, what I mentioned before is that a lot of university and degrees will focus on the technology and the coding side. It's important, but that's not the only thing that you. It's it's a tool like anything else. Once you get the the principles you know how to do it. The rest is about speed. How fast can you call, or how long can you talk? But what we want you to know is. how can you use it? And it's important because a lot of things are still not mature now, so you may still have to go sometime in the deeper level, fixing some issues. getting the extra boost of of performance. You may need to go to the level or on the side, as I mentioned as well. So if you're more on the business side, you will have more meaningful conversation and discussion with technical teams. You'll be able to guide them better. because, you know, and you have been exposed to it. or even if your manager later on, you may not be hands on. But again you will go to give them the right direction, give them the right work. frame the problem for them, a project to work on. So at the end again, no need to worry. I have a lot of students that have no coding experience. We have designed the course and to cater for that. So we are really trying to smoothen the learning. expands and cater for a broader audience and make sure that everyone is learning and getting some new skills out of it.

LUCAS TAN

Thank you, Anthony. So talking about the learning and like gaining new skills, that's how important do you think is collaboration with industry partners in enhancing the learning and career development. Then.

ANTHONY SO

I think.

ODETTE PATRICK

It's the most critical part of learning, and it's an incredible opportunity to prepare you for your professional practice and working. And you also get to build your network, which is really important. So you start to work out how to find people to help you. And I found most people in the data science industry are really happy to help. If you've got a problem, it's not your area of expertise. They don't. They don't mind that. They don't. Don't have a problem that you might not understand exactly, because it's such a big area. There's so much to understand, and they're more than helpful to help, you know, more than I'm happy to help you. You're also dealing with real problems, data stakeholders, team members, specialists. And I think that's everything that the previous speakers have covered. And Anthony particularly, you know, it's a this is a real problems. the real situations. And it's really. And it is exciting to find insights and provide valuable impact. You know, input back into business decisions, because you also realize, too, that what you're providing could be the difference between maybe in. I had a particular internship where it could be the difference between somebody getting a home loan or not, you know, this is really big ethical questions. So there's it's yeah. The you know, the projects I worked on during my eye labs and also my internship. I actually draw on those experiences pretty well once a week. if not more often so invaluable.

LUCAS TAN

Yeah, perfect. So, Anthony, you have also talked about the industry needs for data analysts, data, scientists and data engineer, could you please elaborate on what each role entails, and whether the data, science and innovation course at Ucs is more inclined towards any of those 3 areas.

ANTHONY SO

Fair enough. I will try to summarize as quickly as possible, because I'm almost running out of time. Visually, data analysts are more focusing on uncovering answers from data finding insight meaningful for the business. But that's a lot of data wrangling, using statistical methodization and communication data. Science is more involving more advanced technology and algorithm. So using machine learning or deep learning to start automating some of the the task process and then that that engineer, they are more focusing on building that asset. So how to transform raw data into asset that other people can use. That's clean enough and in a structure way, so that I can unlock the potential of the data. and it tends to more working, more on the storage and building pipelines, data, transformation pipelines around it. Then to your second question for Mdsi. We are actually focusing a lot of all these different type of roles. so you will be exposed to all of them. But you'll be able as well to focus on one if you prefer that field. For instance, if you're more inclined to data engineering, then we have elective to help you to dive deeper into this concept, in this energy, or more on the data science with machine learning and AI or more about managing project, uncovering insight and how to manage business expectation, how to prove that you're bringing the right output and the right benefit with the business. So I think that's what Odette mentioned as well is. We do have a very comprehensive coverage with that degree. But then we allow you to choose and focus on the area of your of your preference or your passion.

LUCAS TAN

Yeah, perfect. Yeah. So a question for city and Anthony. So we got audience asking about the class sizes so silly when you were studying. The master of data, science and innovation. How many students do you have in the class? Roughly. And for Anthony, what kind of like companies that students will also work with.

SIDDHI AUTI

from the class size, perspective, I remember, like it varied from class to class, but around, I think, 60 to between 60 and 100 depending on the subject. And we used to have multiple faculty members, so that, you know, each group would get right attention when they were. They are teaching us. Correct me if I'm wrong, because this is 4 years ago.

ANTHONY SO

Whereas 4 years ago, when I was teaching you that was the case. Now we have more. So. It's a class can be 100 to 150. So I'm right now teaching. So the machine learning algorithm and application, that's 100 52 students. Well, that's semester. So a lot of possibilities to collaborate and learn from others. Which is great, because then the community keep growing. I like to to make sure that there's what we try to achieve as well is the collective intelligence. When you realize that by adding more people, that's where you make better decision, you learn faster. I think that's where you unlock a lot of the potential the degree and fast track your learning journey. No. as Odette mentioned, I say, you try to to work, not only with people that have the same skills as you, because pretty much you won't want to learn a lot, but if you're more the coding side. You work with someone that's more business exposure or more statistical or math exposure. You will learn from them, or vice versa. You're more on the business side. You will work with people that's more technical. Again, you will see the Blind Spot and be able to say, Okay, I see what I'm missing. And that's the thing that I want to to focus on. You know, that's my blind spot. I want to to build on that skill. So that's definitely something I really want to highlight. It's work with people. work with your peers. but work with the the teacher as well. So lots of students During the the semester. They don't dare to ask a lot of question, and what happened is the end of the semester. And then, oh, now, flow of question. So you should do that earlier. We are here to help. Right? It's a learning experience, and we are not just here to mark you. We are here to help you in your career in your growth. So anything that you can use to help you to fast track that journey, you should do it with the lecture included.

LUCAS TAN

Perfect. Thank you very much, Anthony. So with that final question and tips, we conclude our session. Thank you very much for submitting all the questions through the Q. And a function. Unfortunately, we are running out of time for this Q. And a part. But rest assured our team will be continuing, replying to all the outstanding questions via email. we would like to express our gratitude to our guest speakers for sharing their insights and to the latest trends in global data science, as well as for highlighting the opportunities provided by our distinctive data science and innovative innovation program at Uts. we look forward to the exciting future that awaits our graduates for our audience here our team will actually be running an intensive consultation week from tomorrow, the 23rd of October until next Monday. Monday, the 28th of October. So if you have any questions or outstanding questions, please feel free to scan the QR. Code and book a 1 on one consultation, to chat about your learning, opportunities, and options, to get your questions answered and feel free to contact us on innovation. At Utsedu au! We look forward to supporting your learning journey at Uts for those interested in learning more a little bit about our course and the work that our students have done. We are hosting a master of the science and Innovation current student showcase on October 30, th from 6 to 8 pm. So if you are interested for the October 36 to 8 Pm. Session. Please reserve your tickets. Using the link provided in the chats. Seats are limited. and additionally, applications are now open for the February 17, th 2025 intake, and you can submit your application via the Uts student portal. We appreciate your participation, and we hope to welcome you to Uts soon. Thank you, and have a lovely evening.