South Big Data Hub Education & Workforce Working Group, 14 Oct 2021

Previous Meeting Next Meeting

⏯

youtube image

►

From YouTube: Eduction & Workforce WG: DS+X:An Immersive and Interdisciplinary Approach for Data Science Education

Description

South Big Data Hub Education & Workforce Working Group Meeting
August 2021
Presenter: Dr. Jian Tao; Texas A&M

A

To really use what you learned to do some real-world projects you'll need some programming skills nowadays to leverage all the tools, all the tools and software libraries that uh um have been developed. You know in the last several decades and um lastly, you'll need a something like knowledge to either. You know pro ask questions yourself um or you.

A

You need to use the main knowledge to talk with the experts to really start working on the real world uh data science project, so our curriculum design and also the course design based on these three components and I'll talk about that in bit more details.

A

So if you look at it from a different perspective, data site itself, is uh you know as a core here by itself, then it's interacting with different fields like iot, internal things, material, science, speech for humanity and also drug describe and many others, and that's what we call data science plus x d s plus x, and following this paradigm we are dividing our um curriculum as well as our individual module for the course into three corresponding components. Basically, we have some core courses on mathematical foundations.

A

If we talk about the curriculum level- and we have some regular training sessions offered by pummies the use of data science and also the high performance research computing, which is a meeting facility at pc center at texas, a m- and we are offering all these kind of regular training programs early in the semester uh on linux programming python are the basics about how to use the big machines how to get started on programming inside and then.

A

Finally, we have some project-based data, science, training programs and we are working with our researchers and also we encourage students to find their own projects.

A

So this I have been designed uh to work as a semester long or even sometimes this will be multiple semester, effort to get the whole curriculum up and running. um But if you just look into one course, you can divide your course into three parts or even even more, depending on how how you know how fine the structure you want your course will be. You can divide the first several. You know modules in your course to cover some basics and fundamentals, and you can have have some modules.

A

You know teaching students how to get started and then, finally, you get um asking to propose some projects and we have a master program created. I think we, we started a masterclass master of data science program a couple years ago and have been working on that for a while and now it's uh starting from this fall. We are offering this master of data science master of science in data science program at tommies, and we also have founding opportunities for our researchers and faculty members to propose data science course.

A

So there will be integrating our core material developed by the targets uh with their own domain knowledge, so that they can design their own data science best courses and, while working closely with all the researchers on campus, to help them on this on the course development.

A

So, particularly, uh we are focusing on all these small tiny modules to make sure all this will be working well, uh you know together and we've focused on credit courses, short courses, workshops, etc. For the credit courses. You know it will be both on underground and granular levels and we are offering spec special topic courses just to try out some uh very specific uh subject: research subject. Most of those are special special topic. Courses are related with a particular research. A student will be get involved in some particular domain and they all need some.

A

You know the phantom member will need some modules uh from tommy's to help them to develop some data science models, uh we'll work with them and we'll give guest lectures and help them to integrate some material help the student trained on particular subjects.

A

So the short course, as I mentioned, those are offered regularly at the early stage of every semester and students. While they are taking all the data science courses, they are taking all those training courses as well and for my course for the course I was teaching like contextual data science course. I require my students to take at least the two of those four courses.

A

Every semester I'll require a certificate of participation for those uh short courses, so that I know for sure that once there and then go there and uh and and learn something and also we're organizing regular, hands-on data sounds to students engaged and particularly, we are encouraging students to participate in international competitions and I'll talk a bit more about those, and I also supervising I'm superman sponsoring student organizations through the time.

A

It's you know through a budget and comments, and so then they can organize their own events and we can sponsor them on some of the some of the costs.

A

So for the classroom teaching we are trying to integrate a big room and, and uh small you know together, which is actually a big concern for, uh as you probably know, um it's pretty big uh in the size. It's 65 000 students for engineering. We have about 25 000, close to 20 more than 20 000 students in engineering alone and to integrate large lectures. We can have more time teaching time teaching hours for our students for small individual sessions. So that's basic philosophy.

A

More for more, I think that's something I have been talking with the different department heads and even some teams from some colleges to ask them to see. If we can crawl in some of these large courses, then we have more time for small individualized sessions and, of course, because of pandemic, almost everyone has been doing all the remote teaching in last year. Also, so we are trying to see if we can leverage some online resources and I'll talk a bit more about that in a second um on the project, basic uh learning and environment.

A

We are working closely with our local research community, as well as our operational units. We are working with energy and utility and energy services folks and and work with them to help predict the the power consumption and help them to see how the chill water, hot water will be used. During the you know, the whole semester, etc, and also working with our divisional research, our vps office.

A

Vice president, our research office and working with them to do some uh development um to analyze the research competence capacity of and for this, this one is looks like not relevant, but actually this is a very relevant to data analysis now, because for a lot of big data projects, students are supposed to be learning a lot more about high-performance computing. So I have been leading a team at nm to participate a couple international competitions for clustering student classroom competition, and you know not just them.

A

I I myself learned a lot from these humans, so another one actually is relevant is about building vr ar projects, and we are working closely with uh with different. uh uh You know researchers from different departments and we are trying to build this working environment for texas and morales campus, which is a brand new campus designed to to to have a lot of universities around there, but we are trying to build this uh virtual environment to for training the uh our autonomous vehicles and also doing some other related research.

A

It serves as a platform for many other projects and this the the one just mentioned previously. It's connected to this other drive chain which just finished in june and our team. I believe that we got the uh number one number two overall, probably I believe is number one at the overall score. Is it's not posted on the website yet, but the dynamic event? We got number one uh yeah, it's pretty good news.

A

um The last thing I want to talk about some uh some program. I have been leveraging in last several years. It's called the deep learning institute university ambassador program, it's from nvidia. They are giving away 500 for each event, you're hosting on your campus. It's free, the other materials, is free for all the university used.

A

Of course, there are charging fees for external participants, but for university students, it's free for everyone, you can use their online materials and also their cloud computing resources to host all these resources hosting or to host all these uh online workshops, and- and actually you can do this offline and and that's what I've been doing before the pandemic.

A

So you can get 500 to cover the refreshments and other things, and I think it's a very good thing for you to scale up when we talk about more for more. This will be one thing to to get a lot of students involved to learn a bit more about deep learning and data science.

A

You know if you have the facility to do that, and um you know I have all the information here. I shared as a slice. If you are interested just click all the links and try to figure out, uh you know find where to to uh to to get more information and the jew bongo who is uh manager was there I, I know him pretty well. If you really want to get started, I want someone to introduce you to them. uh Please please feel free to. Let me know I can just get you started.

A

So that's all from me.