 Hi, everyone. My name is Emilio. I currently live in Seattle, Washington. I'm in my last course for the program actually. I've been a data scientist for three years now. After my undergrad, I started as a data scientist even without data science experience. Yeah, I learned a lot here and I've used a lot of it at work. I've previously worked at Western Digital and now I'm working for a consulting company on a project with the federal government, which is pretty exciting. Yes. Looking forward to the question. So next. Yeah, you all should be able to speak. Hi, I'm Rafael Hernandez and in the information management program, but I'm getting the certificate of vast studies in data science. So I've taken quite a few data science classes. I sort of gravitated towards it because the information management program I felt like it's a little bit more theoretical and I felt in the data science program I could have some skills that were sort of measurable. And I mean, my background comes from teaching and also from coaching and so I've also dealt with statistics and with data, but I never really knew what to do with it, you know, besides look at it in a spreadsheet. And so once I was able to actually start learning how to manipulate it, not manipulate, but work with it and transform it and shape it and examine it and analyze it. I just kind of fell in love with it. Here I am. Hi, I'm Courtney. I am also a second year ADS student in the high school. I did my undergrad at Syracuse in the Maxwell School, and this is now my last semester. Can I go. Yeah. Hi, everyone. I'm Dixita and I am the second semester of my master's in applied data things, but in my master's in applied data friends of the high school. I am currently working as the faculty assistant for one of the core subjects that is intro DS, and I'm helping the other grad students with their concepts and data science and machine learning and coding and art. I've been interested in data friends ever since my undergraduate so I did take the steps of person master after thinking a lot and then thinking about how I could help contribute to the world of data. So great. I think probably J myself have a bunch of questions, but folks that are listening in if you want to type them into the chat. Jay will keep track of those because he's better at that than me and pass them on and then when we ask questions. So you don't all have to feel obligated to answer but if you know if you want to that's fine. I want to start with 21 and two questions. So maybe the first one is as you've become more knowledgeable about the field of data science, and you're all kind of a different levels of where that is. But I'm curious for each of you. What's been your biggest surprise, or kind of thing that you didn't expect as you kind of have come up to speed in this field and are coming up this field and obviously will continue to be learning as you kind of answer that if we can hear you. So the most surprising thing for me was honestly, how new data scientist, scientists in the industry, and how many people still don't know how to manage it well. So this actually comes from both a class that I still have this quarter, technically, where, you know, data science projects are essentially research projects, you don't know, you haven't seen the data yet. You know, everybody else around you is also, you know, not knowing what is happening how to make the project more efficient and it has helped me a lot, just learning about, you know, the stuff you need to take for a data science project and I know it sounds a little bit boring, but it has actually changed the way that I go about projects and it helps efficiency. What's the most surprising thing for me? I would say the next time. The most surprising to me is really, it's a bit of a contrast to Melio, how old the foundation of what data science is right it's it's statistics. And I feel like in the age of the technology that are now the rapid response to be able to crunch data now with the technology we have basic statistics that have been used for years are now being able to apply to multiple different fields in a rapid response due to the technology explosion in the past couple years and being about how that has exploded and really I'm not, I'm not a very big statistics person so that was definitely a tough challenge to me and more of a technological technological person. So that was a bit of a challenge but really was surprising how much statistically base it was on on the front of that so really getting my chops up and that was was very helpful in that piece too as well. Maybe one last person want to kind of take this one and then we'll jump to some other questions. I think it's just people can go that's fine both of you. Okay, I think it's I think about this this morning so I did my undergrad in history and I'm just sort of thinking like what is the historian going to do in terms of how they analyze history, you know, you know, from 2000 till today or you know 40 years from now and they're going to have to repent on data science they have to, you know, everything's digital now and there's so much information so much records and I think that you know that I'd never thought about it from that perspective my in terms of history we saw it as like you know stuff that was in the books and it was just sort of two dimensional or just flat and now it's just all around us and it's just, you know, it's just everywhere. I was surprised when I saw how sensitive the data science and machine learning algorithms could get, especially with the recommended systems, they are very specific when I started understanding how they work. I was genuinely surprised by that. Interesting. Courtney. You know what surprises me most is the divide between people who understand data and those who don't. And so a very large part of data science and learning it at a master's level is beyond just learning the code beyond just learning the mathematics behind the algorithms, but being able to be a storyteller and really convey the results that you have in the insight you gain, because very few people understand them so a very large part of the program. is being able to articulate those insights that you find so that other people can understand them and make decisions based on them. So there's a conversation going on in chat. Joe basically asked, if you're not looking at chat now that's fine. He asked a recommendation for someone who doesn't have, you know, data science or AI experience. Joe has IT experience. So maybe can you all talk a little bit about your different background experience, because obviously you're buried Raphael volunteered this is this classical training in history. So maybe talk a little bit about your background and, you know, both your background and as you think going forward did you think students with different backgrounds kind of have a, you know, better transfer success or something like that. I really think that data is going to be the next language right like coding and data and kind of go hand in hand but I think anybody in any field has been doing it I have a wife who's a school teacher, and they're trying to teach her how to utilize the data right data rich elementary school teacher right where there's tons of data being collected and what that can be really used for right with it correcting it for the standard testing that they're doing but how she can utilize that and how they can find their uses in that I think it's going to be table stakes for our children and for our next generation to be able to speak in data. I mean, there's no science or data literacy, you know, there's many different ways you can call it but I think it's really the next, the next level of human interaction is how we share our data and how we communicate our data. And how about, how about Joe's question about like, should he be worried if he starts to try to get into this field without having kind of a data science experience. I was a media stage major I made movies from my, from my undergraduate I did, I did Final Cut Pro and all that kind of stuff and weave my way found my way and I saw a niche of being able to solve problems with data. And then I get, now I call it get by the data bug so from my perspective, now there's no barriers from any, any perspective any career coming from. So you've, you're, you're kind of just starting in the flow. So maybe you could talk a little bit about both yourself and maybe some of your, you know, your, your friends that you've met in the program and, you know, just having a certain background helpful or is it pretty diverse. I want to talk a little bit about it. That's, that's kind of, that's, that's very interesting for Joe to ask that because I asked myself that same question when I decided to study ADS because I, I come from, I studied international relations and international business for my bachelor's degree. So no computer experience no mathematics for about 15 years before I came to Syracuse. And despite that, I think it's just one of the most fantastic things I've studied. It's very intuitive. It's, and to add on to what Courtney said, it's, it's not just about the mats or the AI or the algorithms, it's about the connections, the patterns, and how everything fits together with domain expertise. So for someone like Joe with the experience he has in, in ID, I don't see that being a barrier for him at all if anything that's just something that he can use and leverage. Just to follow on to that, you know, you or somebody else can kind of add context so Diane has a kind of a follow up question which is a worry about not having a strong math or or it background so Joe at least had it. How about if you don't have either math or it background. That defines me to a T. I was, I was really bad at maths and computer science at school. And I'm doing really well over here so you don't need maths or it. Everyone does an excellent job of teaching it to you so that it is so intuitive, and you can see it's interoperability with all kinds of different problems, because data science is beyond just coding beyond just the computer science. It's about being able to support quantitatively, and then also deliver insights any problem using different algorithms so I think it'll become really intuitive with the program but you learn so much to the point where it does it really does not matter what background you come in with. The beautiful thing about data science is you can apply it anywhere. I mean you can take data and biology and, you know, whether how fast the elevator goes or what algorithm I should put in the elevator, right. You know, I've worked in finances, I worked in it, I worked in travel. It's applicable, literally anywhere. And if you have a passion for anything, right, you go through the SU program, you can, you know, if it's movies, if it's history. You can base all your projects on that subject, right, your final projects which you work out throughout the whole program. And honestly, it makes it so much better to learn it that way. If I am passionate about soccer, I can have a my project be on, you know, how to choose a player whenever I'm looking for a center back or a forward. Find the best case nor the cheapest one. There is so many applications of data science and, you know, there's no, I feel like you're the only one that can block yourself from learning it. I mean, you have different programming languages. And there's some of them that are easier to learn than others, especially when your needs programming. But honestly, it's up to you. If you're passionate about something, you can apply it there. If you want to learn something new and you can apply it there and learn something new. It really is a really beautiful career. So Genevieve asked, when you started the program, did you know what you wanted to do when you were going to graduate that you have a clear vision of kind of what you wanted to do maybe, you know, I'm adding to what Genevieve said but maybe a clear view of what you wanted to do like three years after you started or was it more of, you know, start and explore and kind of figure it out as you go. That's what I want to do after my master's or with my master's. So I definitely want to become a data scientist and three years from now I do see myself working with a good firm and dealing with the big data that's coming in. So, and then that was the reason why I chose to pursue master's in data science specifically because of the mad, the fat and all of the insights that go into it. Others. I thought it'd be something completely different. Not completely. When I first started it, I thought it'd be, you know, doing more software or more coding and it's really the core foundation of how to apply it. I think the applied data science that this curriculum is really speaks to what it is. Because you find ways that it goes in your everyday life. It's actually kind of ruined me a little bit because when I'm watching TV and I'm watching, especially going through this time of COVID and elections and going through my my career. The process it's been like, oh, that's not right that you're just saying these specific pieces that selection bias that's this you know you start to really like realize what you're consuming in the outside world. So it's really, it's really applicable on what you do and how you see it in your everyday life and then you realize how you can apply it. I think that's one of the things that is one of the best things about the program is is that right. The ability to apply it to your everyday scenarios. So, going back a little bit of dual asked a question, not to stir the pot but maybe do wants to start a pot. What professor or class had the biggest impact and how you want to shape the next chapter in your career. And I think maybe why oh thank you very much yes. Maybe talk about the class, maybe not the professor but talk about the class you took, and why it was like interesting or useful. I actually took a class with Mark and Courtney managing a data science project is, you know, as I mentioned at the beginning like this, it is surprising how necessary it is to have to learn this. Right now, many of the data scientists come from different backgrounds, meaning they can come from chemistry, computer science. And this is that there's people from everywhere. And this is new stuff, right this is a very new career and there's the item or in my undergrad there wasn't majors for data science yet. And when you work with some with a company with another team, and they're like, Here you go here's the data can you do this for me. Right, it's a lot harder than it seems right. There is so much that they don't understand about our process that we have to go through. And, you know, I'll just say the scientists we need to communicate that. And this class changed my approach, and whether it is with, you know, ethics. It's a kind of science right now I'm working with the government on projects that have to do with race and ethnicity. Right. And, well it is very applicable here. Right. And, you know, I talked to other people that are data scientists with me they don't know any of this. They will kind of just been thrown into the project and just expect the results from there. I think it was the intro to data science class for me because I think I had a lot of apprehension that was definitely intimidated for applied for the master's program I was deciding what to do information management or data science and I think I was like myself out and went with the information management. And so when I was able to do the data science into the data science class I was, you know, it was sort of the hands on approach and dealing with the code and finally just wrestling with it and going through some frustrations but then also realizing that there's a lot of solutions out there and figuring out how to find out those solutions. You know, it's just really, really kind of guided me towards it and now I'm taking the visualization class and so, you know, now I get to sort of create some of the cool techniques that you see and it's just added a different dimension, you know, I was just actually doing work for it right now and I was kind of getting lost in time just, you know, playing with the data and the visualizations and so those two classes. Anybody else? I also agree that intro to data science definitely gives the best seamless introduction to our coding and then also shows you all the different applications of data visualization and quantitative analysis on so many different data sets. And then when you take harder classes like now I'm taking applied machine learning and big data analytics, and you're using those fundamentals that you learned an introduction to data science, everything is very seamless and that transition from just learning how to import the data set and visualize it to make an actual model and evaluate the results. But I would say that I think the transition in the classes are really seamless and very easy to learn. Yeah, I had to second, I had to second Emilio and, and this not as I got the results is the data science one, it was a pilot, you know, really first woman didn't really really enjoyed that class. Because I think it being in the business being not you know being in a career right now, and having to actually apply what I'm learning here. That was the big barrier right is being able to manage project and understand that was huge and I think the other one honestly and then this speaks to the program is the balance of a with women school studies that I took a business analyst class, and I've been looting my my mind right now the precious name but he was very knowledgeable and really really had been in the business for such a long time and is his enthusiasm to help the teach have the kids understand what was going on in the business and what's how really apply that to a business aspect. I mean being a person in the career is it was very helpful to understand that and that I frequently hung on after him is like hey I got the scenario at work. How do I breach this right and even first thoughts help me with this couple times to like how do I get past this barrier organizational challenge, besides just a data problem. I think that's what the school really offers as well as that mentorship capabilities well. So, somewhat related but a little bit different Diane asked. What's been the most challenging course in the program so far, or if you're pretty much done what's been most and any advice with how to get through it. I am currently taking the subject 77 to 277 quantitative reasoning for data. That subject has been a little challenging because of the statistics that's involved and we start thinking about pattern one direction. But then this beautiful course it teaches us how to think about a particular that concept in different ways, and how expensive it is. So, to get through it I would say the beautiful book that's recommended, it has everything in it, going through that and actually understanding everything from scratch. And using your index analysis for it is helping me a lot and it will get you through the course. Any other video to talk about a hard class. Oh, the hardest class that I've taken so far was the visualization. Actually, I'm not going into the technical classes that I've had experience before in data science, but the visualization class was tough for me because it's different. And maybe think more about the way that I go about doing that analysis, talking to the customer. Mainly, you know, the visualizations that I create, they have to have meaning you have to tell a story and you know you can for someone, your client, because both data and the analysis that we do is very hard to explain to somebody that's not familiar with data science. Whether it's machine learning or just creating the pipeline, it's a very tough subject to present to somebody else and having to think twice before you just come up with a pie chart. And one of the things that you'll learn is never make a pie chart. It's, you know, it was really hard because it was different than something that I didn't really think about before going into SU. Anybody else. I'll give, so I'll actually also answer that obviously I hear from a bunch of different students. I do think it depends a little bit on your background. And one of you mentioned that as well. So like everybody comes in with strengths and weaknesses, and that's okay and I think that kind of makes different courses maybe a little harder, a little easier. One of the classes I often hear is a challenge is our big data class IST 718. So, but again if it comes in your background so if you have an IT background and then Python and other things is probably a little easier but if you haven't, that can definitely be a challenging course as well. So but definitely depends on the background. I would say if anything the biggest advice I could say is just start learning some Python and R, like, it's, it's going to be one of those tools that you have to use throughout the entire course if you have no computer programming background there's so many courses out there nowadays. So if you do Demi and, you know, all these Coursera courses that you just take one of those you'd be you get a good jump start on on what you're going to be approaching in your designs things and that would take a big edge off the piece of that and there's so many good ways to learn it too so that's probably the best advice I would say for that piece to overcome those hard classes. So just to add to our max marks that if you're not part of as you yet, you do get access to link, LinkedIn, learning, which so many courses there. If there's anything you're interested in, Python, or any machine learning things or courses that we can take and get a certificate on if you want. So Jay you've been watching all the different chat questions. I've been scrolling by so I might have missed some. Are there any questions either from people that have directly messaged you, or that I missed or maybe questions that you want to ask that haven't even come up yet. Yeah, so nothing has been asked in the chat. It's mainly people who have to drop off thanking everyone for, you know, for their, their time. So what I might, I might ask is, you know, in terms of like, you know, to both the students who did the online program because obviously it's different for situation than the students who did the campus program. Just kind of like what their, their favorite takeaway was from that aspect of it on God I know you're a little more new here but you know you're still, I mean, times flying you're about 10 weeks and now so if people can just kind of touch on. And then that would be a little more specific to the Syracuse program, then, you know, maybe more broad data science focused. So I'll tell first, for me, a lot of it was the social aspect of learning on campus. Like, it's, I'm not just learning from professors, I'm not just learning from my tears. I'm learning from my housemates I'm learning from people who are in my course I'm learning from my project mates, and in turn I'm also kind of teaching them things that I know that maybe they wouldn't know. So it's the cross cultural aspect of being able to work in teams on something that is completely alien to you and still being able to complete a task. So it's an important takeaway for the on campus program, please. Yeah, with the online program what I like is I can work with people in different time zones in different parts of the world. Sometimes I've had professors that were in other parts of the world as well. And it's always kind of interesting to sort of to try to organize how to how to get a group project done when, you know, I'm on the West Coast and people are elsewhere so that's, but it's cool in the sense that, you know, I would never meet these people otherwise and so I enjoy that very much. But I think that speak to the online aspect I think that really honestly helps nowadays because if you're in data science and really into this technical field like this and times, you chances are your team is spread right so I work I work with an India team. I was working on Australia team. So, doing this is, you know, if I hadn't been in this way this is really good practice from an online perspective to be with a manager time effectively. And so a lot of data scientists are kind of left to figure out the manager on time when they need it, as long as I need that deadline. That's really, that's like the core of like two SU part of it is being able to manage your time getting done effectively and I wasn't able to do the two, you know, two courses a semester just a funding and be just time to young ones and I think that's the really benefit of the online course is I was able to take one a quote one course a quarter. And it took me a little longer than most people, but it's really been great to be able to have that ability to stretch that out and not have it feel like I'm left behind or not completing what I need to complete in time. And just going to four more marks that I would second that. You know, one time actually was traveling for work to the Philippines night to a class at 2am over there. Right. The professors are very understanding or situations, you know, they've been through it. So they, they will, they will help you accommodate to whatever you're going through at the moment and, you know, through COVID I know some people were not able to take some classes and the professors were very understanding about this. Now, if you get to me, I was mentioned you get to meet people from different time zones and you know I've met people from the city that I live in. That is on the opposite side in the US. So it's very cool to make contacts and you see what they're working on. You know, there's people that were working Microsoft to talk to and they helped me get an interview. Right. You just meet a lot of people that are very cool. You can sit in touch with. I would also just want to second that and say I think my classmates have been one of the most defining features of my graduate experience in particular, Mark Emilio and I were in a group for the managing data science class that we took. We actually copied our combo board that we had made for a project I'm doing this semester, so I reuse everything that I've learned I learned so much from everyone I work with. And whether you're online or in person in but like either scenario I think I've made really. I've met incredible people and it's one of the best parts of the program. So it looks like a question did come through I think we kind of touched on this a little bit with like the course error linked in learning but are there any additional resources for somebody who wants to learn a little data science just on their own. So, very much. What I would say is Kaggle is a great source. They have the data, they have the code. They have a problem if you want to try something new. You just find your own data set. I think that would be the best thing is you find your own data set and you play around with it. One, you know, if you're passionate. As I mentioned earlier about another subject and you can find that data set that reflects that passion. That's the best way that I've learned is that through the projects that I've had here and as you, if I'm passionate about something I'll put a lot more attention to it and it's a lot easier to understand and, you know, learn. I would say that same thing right like Kaggle and not just Kaggle for the data sets but even Kaggle for the notebooks. If you look at there like you can see people attempting different ways and really it's seeing the thought process of how people approach the problem, not necessarily the code and you can copy all the code all you want but if you don't understand their approach and a lot of people you find that person that really comments well and does a good notebook and a good markdown section where they really explain what they're doing. It could be the smallest data set in the world but just having them see how they went through the process and how they approached it. There's as many different ways to do it. That's one of the most valuable things I learned a lot of those things. Besides just the random ones that teach you the syntax of coding, those are the some of the most important things is seeing how people approach the problems and what they came up with the resolution. And there's another question in the chat. Did anyone get any sort of certificate before pursuing your master's degree. I think the question was specifically in like the data science realm. Yeah. I don't think anybody got the CIS and data science before I think you all just jumped right in to do the program right. Yeah, I mean I got some certificates for an NLP natural language processing, but I can tell you that it doesn't compare to the real thing when you actually see real data. It doesn't compare to just click and play method. Yeah, I'm getting the cash right now in data science. And so I am thinking about doing the master's program is something I kind of grapple with every, every night, discussing with my wife so so you know my do that. Okay. The old classic question. What other schools did you think about and why did she use this here. I'm not going to be honest with that one, right because honestly g re is actually one of the, I think was my marketing studies client one of the things we're, we're doing Google analytics and searching why Syracuse comes up on certain searches on things and being able to see that part of it. And one of it is because the GRE and being able to kind of overcome that and I think Syracuse is smart to understand that it's not about just to sit you know just a number that a test gives you. You know I was able to kind of lobby for my, you know, abilities and what I'm doing at work and how I want to apply it. I think that's really good about Syracuse to do that is was that piece of it. I know it seems kind of cheap like maybe I'm getting in like a, not with the academic basis of it but it's important I mean I'm a professional in a career I have young kids and have time to go back and do a GRE and do that whole thing but I really wanted to advance my studies so really was something you know, helpful for us to choose me and my wife as Raffia said to choose that this is, this was the place for me to afford and go for it. I would say the applied aspect of it, because it's very different to learn something on a textbook or on a class online and actually applying it. And when you get the data it's not that there's always going to be the same tab. You know you're always going to read the data to process and model it put in a neural network. That's not always going to work. And if applications you know trying it on different data sets, getting the experience of the professors they've worked in many different fields and you just hear some of them work with the military doing analysis and how to find them. Right. There's others of working healthcare and getting their experiences and how they approach a problem. I think that is the most beneficial thing as a data scientist. Because, you know, if you think one way. You might have a problem but not as good as it can be right or it's not a lot just learning from each other's experiences. And the professors surely have many stories to talk about in the class, which I think is the most valuable part. So I was looking not only at different universities but different degrees even. And for me what what what SU did that a lot of universities didn't do is it gave me the opportunity to speak to Dr salts and understanding what the course was about understanding that it was not just computer science or stats or business. It was somewhere in between all of those courses while also being hands on was was integral to me making my decision to come to Syracuse. So it's it's the program is the people that that I had the opportunity to study under being able to work with Dr salts. For example, those are the kind of things that I think made Syracuse special for me.