Listen live as Dr. Bahram Mobasher discusses how his work as an astrophysicist applies to engineering and data science. Dr. Mobasher designed the curriculum for the online data science specialization and is the Principle Investigator of the “Fellowships and Internships in Extremely Large Data Sets (FIELDS)” with a funding of $4.5M from NASA.
Ted: This is Ted Cake with the University of California, Riverside. Well, good evening. I want to thank everyone for joining us. My name is Ted Cake. I’m an admissions representative with the University of California, Riverside for the online Master of Science in Engineering program and tonight we, uh, will be doing a deep dive into the Data Science specialization. Uh, we currently have it looks like four people logged on with us. We’re expecting approximately 10 more but the – I want to assure everyone that this is also being recorded and the recorded webinar will be made available to everyone shortly. You’ll receive an e-mail to that effect.
At this point has everyone signed into the audio bridge so that, uh, you know, we can hear your questions, uh, or are you able to, uh, send me a question via the chat window? Okay, I just posted a question. Let’s see who’s the first one to answer. Okay, well we’re going to go ahead and get started with our presentation. This is for the Online Master of Science in Engineering, the University of California, Riverside. Our agenda this evening – I’ll be introducing Dr. Bahram Mobasher. Uh, he’s going to be talking about data science and this industry and what it means. He’ll go over the course details, what it means to become an expert, how data science applies to other, uh, engineering specializations. Then I’ll introduce myself a little bit more and talk about my colleague, David Chambers. We’ll talk about admissions requirements and then the section that you’re probably all waiting for which is the Question and Answer section.
Our speaker today is Dr. Bahram Mobasher. He’s the one who actually designed the curriculum for the online course in Data Science. He’s an astr – an astrophysicist and the principal investigator of the fellowships and internships in extremely large data sets with the funding of 3 ½ million dollars from NASA. At this point I’ll turn it over to Dr. Mobasher.
Dr. Mobasher: Thank you, uh, thank you for joining our webcast, uh, today. So what I’m going to do is to go through the basics of Data Science, why it’s so popular and what we can do with it. And then introduce our course here. The course presented as a Master of Science in Engineering at the University of California at Riverside. The, uh, the data science is rapidly becoming a very popular area and, and the employers are, uh, picking data scientists to work, uh, in different disciplines. It’s, uh, expected that, uh, the we’ll have a shortage of 140 thousand at least, uh, people in data science, uh, with the skills who could otherwise extract information from the data and, uh, this is only going to increase, uh, in future years. And there is no plateau to this because every discipline needs data scientist these days. And this is the aim of the course, to, to train the data scientists who could handle this, uh, this short, uh, this expertise who could, who would know about this and who could basically use data from different disciplines to make sense of it. The salary – the median salary is, uh, for just junior data scientist, starting is about 90 thousand dollars and it go up to over 200 thousand when you get, uh, more experience in the area.
So the course designed here basically is to train the – people to become a data scientist by the end of the course and be well positioned, uh, for employment in data science industry. And I, I, work with NASA very closely and talking to NASA managers, they have a huge shortage of data scientists and the problem is that people go there, once they, they gain the experience in data science then they get offers with higher salaries, I believe, though it basically is a retention problem too and, uh, this is the gap we try to – to fill in. And ______(5:16??) is a diverse field. We try to cover different areas in data science and as I explained the course is designed to basically reach out to different people with different disciplines with different backgrounds. So, uh, the, uh, the course at UC Riverside is basically designed so that it has a full, uh, core courses, a total of 16 credits on professional developments. That includes data management and in general, engineering management, the management courses which you need to manage a group and in, in the workforce. And then there are specialized courses on data science which are about four courses again, a total of 16 credits on that and full credit for a project. So in total would be about four – about eight courses and full credit project. And the project could be done in your home institution, we could arrange it for you to do it at – under a NASA scientist or you could do it at UC Riverside so it’s quite flexible in the area of your choice.
So the course as is designed now are basically go through the data science courses and it’s basically seven different courses, you need to take four of them but I explained that in the coming year it’s going to increase. The courses contain data management techniques, advanced computer vision, statistical computing, basically statistics for data scientists, and statistical mining methods, uh, machine learning and information retrieval and research and finally data visualization and B-Data tools. But these are the course which are being offered. On top of that, since we are making it accessible to people from different disciplines, we are introducing other courses in the coming year and that includes new imaging course, basically the techniques for analyzing MRI images. So that’s, that’s one full course. It’s, it’s quantitative finance is another one and which would train students to move to stock market to do financial analysis forecasting and finally we have deep learning – it is a part of machine learning so especially about machine learning and re-planning.
Also what we have in the course for students who register in the course, we have free – we provide free background course, information course to basically give them the necessary background to find the course useful. That contains an intensive Python course – our course basically is a program, a program in languages called Python, and are very, very popular – statistics for data scientists and basics of B-Data. So these courses will be provided online free of charge for students who register in the course and we strongly advise students to go through all these material before starting the course because what we get students from different backgrounds, from different disciplines, some of them got their degree years before, they’ve forgotten all these materials and some don’t know programming languages which we teach and use in data science. So we strongly advise you to go through that and do it and then take the data science course.
Uh, then we have the – the aim of the course basically is to, uh, is to make you an expert in data science which includes computer programming, both Python and all languages, statistics, simulation and modeling and machine learning and deep learning, visualization and significance, physical computing so you get something from all these disciplines by the end of the course. And if you want to go further deep into that the instructor would help you to become expert in any one of these areas, depending on your interest and what you need in your work. And this course basically is at the end you could apply for jobs in different disciplines becoming data officers and becoming data manager, basically managing the groups to do these software developer and social media person you could do basic research in social media or go to a financial market or bioengineering or analyze biomedical data. So it’s wide open and we have tried to introduce one course so – for every one of these disciplines so that you’ll be open to choose what you want to do and move into discipline of your choice.
So I stop here and if any questions – if you have any questions, I’ll be more than happy to answer. So, uh, Ted will take you through the admission process and then if you have any questions I’ll be happy to answer.
Ted: Okay. Thank you, Dr. Mobasher. Uh, it was very interesting. Uh, you do have two enrollment advisors here for the online program at the University of California Riverside. Uh, I’m here this evening. My name is Ted and you can see my ugly mug there on the slide and then my cohort, my colleague, David Chambers. We, uh, work very closely together so if you’ve talked to one of us, don’t be surprised if you call in and some one or the other answers and we are familiar with your application or where you’re at in the process.
Our primary role is to answer whatever questions you may have about the program but also to help you to determine if the program is a good fit for you based on your career goals. And it’s really one of our primary functions. We want to make sure that as you’re looking at this program that it is a well thought out decision, it fits your previous education as well as your career goals and, uh, hopefully it also ties in with what you’re currently doing. Uh, if it is a good fit and if you meet the admissions requirements we then will walk you through the admissions process, uh, if indeed that’s what you want to do. Uh, and that’s our primary role. We make sure that the program’s a good fit for you, you’re a good fit for the program and then we walk you through the application process.
So, that being said, let’s talk a little bit about the admissions requirements. Uh, first the general requirements. We want to see what we refer to as a STEM degree, something in science, technology, engineering or math. You may have seen on the website where it says “Engineering or related degree” and that’s what we mean. It should be in one of these areas. Specifically for this program, a degree in mathematics, statistics, uh, business or any engineering degree, uh, will be sufficient, uh, to get you most of the prerequisites that you need for this program. The other thing that we’re looking for is a grade point average, you know, everybody has one but we want to see a minimum – our desired minimum is a 3.0 in your Junior and Senior years. And there are some caveats to that that David and I can go over with you, uh, in more detail. So feel free, you know, to schedule an appointment with myself or with David to discuss your specific situation. We’re more than happy to go over that.
The other thing that we’re looking for is a minimum GRE score of no less than 300. And that’s the verbal and the quantitative, uh, you know there are three sections, written, verbal and quantitative. We’re not overly concerned about the written score but we do want to see the combined score of the verbal and the quantitative be 300 or higher out of a total of 340. We often are asked: What can I do to avoid the GRE? And the answer to that is if you have taken the fundamentals of engineering exam and passed we’ll waive the GRE. If you hold the PE designation or license, uh, and you can provide us proof of that, we’ll waive the GRE. Short of that, we recommend you take the GRE because it makes your application that much stronger. On the other hand, if you have 15 or 20 years of work experience that is relevant to the program, uh, you know, we can recommend to the admissions committee that the GRE requirement be waived. But for most people the GRE is a good idea and you should plan on taking it.
Specifically for the admissions requirements for Data Science, again we’ve got the preferred undergraduate degrees that we’d like to see, uh, computer science, physics, statistics, uh, business, uh, mathematics, applied mathematics, anything that’s going to give you a good strong background in math. The feedback that we get from students are they don’t have any – who don’t have an engineering background is that they, they don’t have any problem with the four professional development courses but they do complain a little bit about the math that’s in the actual data science courses. So anything that’s given you a good strong math background, uh, you know, as part of your undergraduate degree, those will be good. Uh, we want the 3.0 or higher, the 300 GRE score and the, uh, math score.
Uh, the application package, uh, there’s an online application and, uh, if you talk to either David or I we will send you an e-mail with a link to that. You can also find the link on the website. The application does need to be completed in full before it can be submitted and part of that application is that you have to be able to list no less than three potential recommenders – that is, individuals who have said yes, they’ll write you a letter of recommendation to the program. David and I both generally recommend that you have more than three just in case something happens and one of your recommenders isn’t able to get the letter in on time. I’ve seen it happen where a student wasn’t able to get his application submitted in time because he missed the cutoff deadline because one recommender was out of town so three or more, uh, four or five is best. In addition to listing those recommenders with their contact information you also have to, uh, attach two personal statements. One is a statement of history where you’re gonna talk about significant individuals and events that have lead to your choice of education and career. You also want to address any significant challenges you faced and how you’ve overcome those. And then there’s a statement of intent or a statement of purpose and that’s where you’re gonna talk a little bit about, uh, what your career goals are and how this degree program is going to help you to obtain those goals. So we have the three letters of recommendation that – you know, the three recommenders have to be on the application – two personal statements have to be attached to the application. Then you can press the submit button and you can, uh, pay the, uh, application fee. That needs to be done if you’re thinking about the fall term which starts September 25th. We need that application submitted no later than August 18th.
Supporting documentation which includes your GRE scores or proof of passing the FE or the PE exams, official transcripts from all post-secondary institutions and the actual letters of recommendation plus a copy of your resume – all of those things will need to be received no later than September 1st in order to be considered for the fall term, which again starts September 25th.
At this time I want to open it up. We’ll, uh, answer any questions that you have. Uh, we do have a couple of questions here so, um, feel free to go ahead and ask those and let me see what this first question is here. The question is: How do you evaluate foreign degrees? So I’ll take that question since it’s primarily an admissions question. What we do when it comes to foreign degrees is we still require an official copy of the transcript. Just to clarify, an official transcript is one that has been sent by the issuing institution and we receive it in a sealed envelope directly from the issuing institution. It should be in English. If your instruction or your official transcripts will not be in English you’ll need to get two copies, have an official in the original language sent to us, have one official copy sent to a certified translator and then have them send the copy to us so that we have both. We also need a copy of your graduation certificate, uh, both in the original language and in English. Those are evaluated in a similar format. We’re familiar with a lot of different, uh, uh, you know, transcripts from foreign countries. For instance, in India it’s not a 4 point scale like it is here in the United States. It’s based on a percentage and we do, uh, very much enjoy seeing those and we have a pretty good idea of what the percentages translate to. Uh, so they’re evaluated in an equivalency basis, I guess that’s the short answer to that question.
Uh, how many people are currently enrolled in the Data Science program? Let’s see, I think I have those numbers here relatively quickly. The Data Science program in the course of the one year that we’ve had it, uh, has quickly become, um, one of our most popular programs. Uh, we currently have, uh, students in 31 classes and there’s a total of 18 students currently in the Data Science program. We anticipate one graduating at the end of this term.
We have another question. Will this presentation be put online for later review? Yes, it will. Everyone who has registered and/or attended this, uh, presentation will receive an e-mail, uh, indicating, uh, you know, providing a link so that you can come back and you can review the entire presentation.
Uh, there’s a question, uh, about where can you take the FE or the PE exam? Uh, the PE exam you’ll want to check with your state; for the FE exam, uh, want to check with – you can Google that – I’m doing it right now. Uh, you want to go to, uh, the NCEES.org, uh, that is the organization that provides that exam so it’s N as in Nancy, C as in Charlie, E as in Edward, E as in Edward, S as in Sam.org/engineering/FE. You can find all the information you need about taking the FE exam there. And if you’re debating whether to take the FE exam or the GRE, I recommend you take the FE if it’s applicable to your specialization because that will look better on your, uh, resume and will further your career.
Let’s see what other questions we have here. Feel free to ask some others. Uh, someone has asked what is the PE? That is the professional engineer exam and that is the exam that typically leads to licensure in the state, uh, in which you reside. I have a question here for Dr. Mobasher, uh. There’s seven electives currently in the program, uh, how do you, we determine which electives to take, considering we can only take four of them?
Dr. Mobasher: It’s really, uh, up to you. Uh, firstly which you are more interested on, which of it fits better to what you do and your future, uh aspirations. So you’re free to take any of those, uh. So it’s really – it’s up to you. Also we have advisors that could advise you, uh, as for which ones to take. For example, if you are in the medical, uh, sciences proficiency, uh, maybe taking something – a new course we’re introducing in analyzing MRI imaging might be useful. Whatever you do visualization would be very useful. So my advice is to take courses which, uh, are more general, uh, so that you could apply to different disciplines.
Ted: Okay, thank you, Dr. Mobasher.
Dr. Mobasher: Statistics, for example, would be very useful and is applicable to many different disciplines.
Ted: Okay, we have another question that I’m going to ask you to take, doctor. It’s what is the format for the final project? Who assigns the project and would be we – would we be working as a team?
Dr. Mobasher: Yeah, uh, this – basically you, uh, if you come up, if you have some, uh, idea, some interest, you could approach us, you could talk to us and we design approach activity and assign an advisor and mentor so that you could work on that. It’s – it would be very useful if you could do it your working place and, and then again we provide the support. If you don’t have any, uh, any ideas what to do, uh, we could consult with you, we could provide some, some projects if you’re interested. For example working for NASA we could provide you some NASA, uh, scientists to advise you regarding the project or have your teacher assigned, the faculty, different disciplines, they could provide you. It’s, it’s quite open as what the format and what the subject of the project is as long as it’s on Data Science.
Ted: Thank you. Uh, I’ll add to that from an admissions perspective, uh, we often because this is part of the project design course, which is di – it’s a four hour course but it’s divided into four one hour segments, so you take one hour toward the project in each of four different quarters. In that first quarter is where you’re gonna select an advisor with whom you want to work. It’s, uh, never too early to begin looking at faculty bios, uh, you know, in the Data Science department, uh, on the website, uh, you know, once you’re accepted into the program, you know, you can request as Dr. Mobasher has indicated, you know, if you want to work with somebody at NASA, they can get you in touch with the right people. But it breaks down so that in the first segment you’ll actually select your faculty advisor and then in discussions with your advisor you’ll develop your initial proposal for your project and submit that. In the second segment you’ll do a literature review, obviously related to your project. In the third hour you’re going to get that project – you’ll do any revisions that are necessary as a result of your literature review and get final approval on your project. Then you’ll go ahead and implement it. And in the fourth project – or in the fourth segment of the project you’ll do a, uh, you know, a written summary, you’ll write up the results of your project and you’ll do a presentation, uh, and I’ll add that the presentation is done by a webcam. Because this is an online program you’re never required to come to campus.
Let’s see what other questions we may have here. Dr. Mobasher sometimes we’re asked what’s the difference between Data Analytics and Data Science? Can you give us an explanation of that?
Dr. Mobasher: Yeah, Data Science is the more general term and Data Analytics are the tools and the, uh, the, the different things you need in order to make sense of the data.
Ted: Okay, thank you very much.
Dr. Mobasher: Yeah.
Ted: Feel free to elaborate if you like.
Dr. Mobasher: Like, for example, statistical tools or machine learning – these are the tools, techniques which you use to make sense of the data, like data, to extract information from the data, these are Data Analytics. The Data Science is basically the, the – is the Big Picture.
Ted: And, do we have any other questions? Yes, we’ve got a couple more just came in. How different is the teaching from other schools like Georgia Tech or Berkley?
Dr. Mobasher: Yeah, uh, I don’t how the teach – how the teaching is different but what I do know is that the course is different. The course here is basically designed to address especially the management and the professional development and these are the core courses which students from our experience have found it very useful. That’s not offered by those institutions. Secondly the variety of the courses here, which is organized so that, uh, you could, depending on your discipline, you could choose what you want to do and, and go on and do it. It’s not, uh, as wide-ranging as I would like it to be but, but it provides the opportunities for students to choose what they want to do and in what area they decide they want to be.
Ted: Okay, thank you. Uh, we do have a question. Is this degree offered on campus? Um, to my knowledge, we don’t offer a Data Science degree on campus. Is that correct, Dr. Mobasher?
Dr. Mobasher: That’s correct. Yes. Not at the Master’s level.
Ted: Right. So at the Master’s level we have the Master of Science in Engineering with the specialization in Data Science, uh, and the hybrid program, the interdisciplinary program that we’ve been discussing is only available in the online format. We have a question, uh, working full time, is it still possible to finish this program in one year, 13 months? If not, what is the estimated time to finish while working full time? Uh, again, this is something that’s going to depend very much on your personal situation. Uh, we try to give a rough guideline that in order to finish in one year to 13 months it does require that you’re taking two full four-hour courses plus one hour toward the project design course for a total of nine credits each quarter in four successive quarters. That does translate to about 30 hours a week on your, uh, course work during the program. Though take that as a rule of thumb and if you have 30 hours a week that you can dedicate, uh, to this without, you know, ruining your family, uh, then you can do it in one year and we do have a number of students who are doing the program over the course of two years because that translates to about 15 hours a week. You do have up to five years to complete the program.
And then we’ve got another one. What’s the admission rate for the program? Uh, that’s not something that I have hard statistics for. It’s only offered online, it’s only been offered, uh, since last fall. It’s just now coming up on its one year anniversary and we’ll be having our first graduate. Uh, David Chambers, my colleague and I, we both have a pretty good idea of what, uh, we’ve looking for in Data Science applicants and so, you know, if you have a specific question about that, uh, please feel free to e-mail or call David or I. We’ll be happy to go over those details of the, uh, you know, with you and talk about your specific situation and we’ll give you a very good idea of whether or not you’re going to qualify for the program. Uh, because David and I do that and we have those types of conversations, the vast majority, probably around 90 to 95 percent of the students we put forward, uh for the Data Science program are offered admission into the program.
Dr. Mobasher: Well, we have, as I mentioned, uh, a free background course online, uh, which would bring you up to the level that you could start at the Master level, understand the course and, and that’s, that’s another thing which the course has and, and, I, I, if you register, strongly advise you to, uh, get into that. It’s provided for you, it’s in Python, our programming languages that this is what data scientists and big data, basics of Data Science. So that would be very useful and helpful in following the course.
Ted: Okay. And let me just check, see if we have any additional questions. I’m not seeing any.
Dr. Mobasher: It’s very sensitive – we are very sensitive that students who enter the course find it useful and could use it in their work and, and get to where they want to go to. For that reason, if there is, is any shortage, any problem as far as some of the students have because we understand some of them might have left college 10 or 15 years ago, enter workforce, if there’s any problem, we try to work with them to, to make the course as useful as possible for them.
Ted: Okay. I’m not seeing any additional questions. I do want to give our audience another minute or so. If you have any additional questions, please get those posted so we can get those answered, uh, and if I don’t have any in the next couple of minutes we’ll go ahead and we’ll end the presentation. Um, I do want to thank Dr. Mobasher very much for his time and we do have a question just popped up here and the question has to do with transcripts. Transcripts we would get at the end of the course, does it mention that it’s an online course? Uh, no, the diploma that you’re gonna receive is simply going to say it’s a Master of Science in Engineering, uh, and will show that it was earned at the University of California Riverside. Uh, your transcripts are going to show what courses you took, uh, but those courses are also offered on campus and you would not be able to tell necessarily by looking at the transcript itself that the course was an online course. Uh, we go to great lengths to make sure that the course content is identical between the course that’s provided online, uh, and the course that is conducted on campus. We actually record the lectures live and then post them in the online classroom so that you’re getting the same exact content, the assignments, exams, everything else is going to be identical, uh, you know, and we follow the same calendar as the campus. So the educational we, we protect very much the intellectual content of the courses to make it identical.
What is the work requirement for a GRE waiver? Generally speaking, uh, David and I would discourage you from requesting a GRE waiver with less then five years’ work experience but that does vary depending on what your work experience is, what field it’s in, uh, but if you’ve got less then five years’ work experience, uh, I would recommend you take the GRE because a strong GRE score combined with a strong, uh, GPA makes your application that much, uh, stronger though we do take a holistic approach when we look at your application. We look at the personal statements, we look at the letters of recommendation, we look at everything that’s included as part of the application package.
Can we take the pre-class without being enrolled? Do you have information on that, Dr. Mobasher?
Dr. Mobasher: By pre-class, uh, my guess is that is the background material, uh…
Ted: Yeah, I think he’s talking – the person asking the question is referring to the, the C programming or R or Python.
Dr. Mobasher: My, my knowledge is that you have to be enrolled for the course to be able to take that.
Ted: That would be my expectation as well.
Dr. Mobasher: That’s right. So that that – once you enroll then you get access to that and you just go through that, use it.
Ted: Okay. Uh, do we have any other questions from our distinguished audience today? I’ll give them just another minute. We do? Uh, the question is can we take course on campus and online? Uh, at this time that is not something that we allow. If you’re in the online program you take all your courses online. If you, uh, you know, are enrolled in a campus program you take all your courses on campus. Uh, and of course, since this program is only offered online you wouldn’t be able to enroll in a campus-based program, uh, that would result in this degree.
Dr. Mobasher: Basically optimized for people who work, uh, or want to change a discipline and in one year try to move into Data Science and become data science…
Ted: That’s an excellent point, Dr. Mobasher. Uh, the whole purpose of the online Master of Science in Engineering was designed to provide people, uh, who want to increase their technical skills but also advance their professional skills so they, you know, can advance in a working environment. Uh, that’s the whole reason the program was designed. So it’s really been designed around the needs of working professionals. So it is designed in such a way that you can do it online, get an excellent education that is second to none, uh, and do it while you’re still working. Do we have any other questions out there? Okay.
Dr. Mobasher: If you have any questions, any technical questions, academic questions, please feel free to send them as well. I will be happy to answer them.
Ted: Okay. I thank you for that, Dr. Mobasher. I want to thank you very much for taking time out of your schedule to be with us this evening and to go over the program. It was very enlightening. Uh, I enjoyed it very much and I hope our, uh, our viewers did as well. So I thank you very much and I wish you a good evening and to all those who attended, thank you very much for joining us this evening. As I said earlier, this program has been recorded and it will be provided to you if you’ve registered for this, you’ll receive an e-mail letting you know when the – when you can watch it again online, so at that, I’ll end the webcast. Thank you, everyone. Have a good evening.
Dr. Mobasher: Thank you.