 Hi everyone, nice to see you this afternoon. Thank you for joining me on this very last session that we're having. I know that it's between me and you getting to leave, so I will try to make this interesting and helpful for you to see what types of jobs are available in data science. And I'm going to be able to answer your question on what skills employers are looking for for data scientists. There's a perfect setup for me, so I appreciate that. My name is Jeffrey Krauss. I'm the Associate Director of Career Services at the School of Information Studies here at Syracuse University. I'm also in charge of data analytics at the school as well. I handle all of the student data when students graduate from the iSchool. I track them, if you will, and find out where they're going. And then I keep track of that data and then I share that data with the school and with the dean and all of the directives. So that's my role that I play. I speak with students on a daily basis. My specialty is graduate students who are interested in data. So I talk with students all day long on preparing the materials to get ready for data, analysts, data scientists type roles, helping you with interviews. And when you do get an offer, I then help you devotion. Awesome. So those are all the different types of things that Career Services has to offer. So as I go through the presentation today, if you do have any questions, if you have any comments, I encourage you to ask them or to say them. This is a workshop that I just opened up for you. So if there's anything you'd like to talk about as we go through, please feel comfortable to do that, to say that, you're welcome to talk about anything you'd like to talk about. Okay. All right. So having said that, we are talking about the current and future state of applied data science. That's going to be our focus. What is this thing we have up here? Is this online? Yeah, because I don't know anything. All right. So data scientists, they create business value through informed decision making and effective product management. The role encompasses different activities, which vary depending on the business needs, the business size, and the business resources. And as you can imagine, I'm going to be sharing a lot of data with you today. It's always helpful to show you graphs and charts versus actual numbers. And I encourage you when you do get into these types of roles, data visualization is very, very important. And you're going to see it's much easier to see it in a graph than it is to see numbers. Now having said that, I'm sharing numbers with you. So we'll start there and then I'll show you some graphs. So what do these numbers mean? The employment rate for data scientists is expected to grow by 36% from 2021 to 2031. So I've done some research on the industry and I'm sharing what the research is telling us. So 36% is pretty robust. Does anybody know what the average growth rate is for the average job? That's a great guess. Yeah, it's actually 5%. So 36% growth over that time span was pretty amazing. So that's something to think about. Yes, please. That's just like AI. So we're going to actually touch upon that, but yeah, we're focusing on applied data science specifically. Yeah. 39% of the job ads I'm linked in are in the IT and tech industry. So that's pretty interesting as well. Almost 50% of the jobs that are available are in the IT industry. The 6% of the data scientists job offers I'm linked in are in anybody in other state. Good guess. Not in New York, California. That's right, California. And that's the highest, by the way. And that's been true now for a couple years. So California, as you can imagine, has a lot of the data science jobs that are available. However, there's a lot of companies that have left California recently. Do anybody know where they've gone to? That's in New York. It's not New York. It's Austin, Texas. Yeah, they went to Austin, Texas. That is true. The most independent technical skills for data science experts are Python and SQL, which I will show you. So in general, on average, most people who are looking for data scientists in their organization are looking for people who have skills in Python and SQL. And I'm going to show you that it's actually Python, but I would argue that SQL actually is probably more important than Python is at this point. Entry-level positions accounted for 55% of job offers. So that's interesting too, especially for U.S. students or most of U.S. students. 55% of those job offers were entry-level positions, just starting out positions. The average salary for a data scientist in the U.S. is $125,242. So it is a pretty robust industry. As far as salary goes, keep in mind this is the average. This doesn't mean you're going to make $125,242, but the potential is there for you to make that. So let's take a look at this graph. Now, there has been a mountain play in jobs recently. And if we were talking, having this conversation last year, I would tell you that we're in trouble. The economy is bad and a lot of employers are not hiring. In fact, a lot of employers were laying off at this time last year. The problem is that they over-hired for COVID. They thought that everyone was going to continue to use technology to the rate that they were using it during COVID, and that wasn't the case. A lot of companies are calling people back to the office, which means that they don't have to use technology at home. So they find themselves having to lay off a lot of people. So what's happening in the industry is not only are students going up against other students when it comes to finding jobs, but they're also going up against now professionals who lost their jobs, who have multiple years of experience trying to go for the same types of jobs. So we've struggled through that. I think that there is an uptick in jobs that are available now, and as you can see this graph just pointing out, the jobs are getting better. There's a tool that we use called Handshake, which is a tool that houses jobs and internships specifically for Syracuse University students. And if you go into Handshake right now and type in data analyst as a position, you're going to find over a thousand opportunities available. So the opportunities are there, and we're finding that the market is getting a lot better than it was just a year ago. Okay, so I have some data to show you for even as recent as August of 2023. Looking at hiring year-over-year shows just how far the industry still needs to go. Year-over-year openings are still much lower compared to 2022. Data science and data engineering goals haven't even come close to rebounding from the late 2022, early 2023 mass layoffs in Silicon Hong Kong. The one standout is machine learning, where positions managed to increase by 16 percent year-over-year. However, this could just be a variance given the relative rarity of this position compared to other job openings. This is the relative new position that's opening up. And if you look at the hard numbers, you might be thinking 16 percent is pretty high, it's pretty good. It actually went from 618 job openings to now 722. So it's a little over 100 more jobs, which boosts it up to 16 percent, right? So it looks impressive, but when you break it down by number, it's really not that many more openings that are available. Now, let's take a look at this chart. You might be seeing that there are data scientists, data engineer, and data analyst and machine learning jobs. The one thing that I tell students is when you are looking for a job in the data field, I encourage you to look at all three of those job positions, because companies don't know the difference between those three positions. So a lot of times, they're going to advertise for a data engineer, whereas if you look in the requirements of the job, what they're really looking for is a data analyst. So you're better off if you search all of those jobs and don't judge a position by the title, judge it by the requirements. So I'm going to encourage you to check out all three of those positions if you're looking for a job in this field. Looking at this chart, a return to P3 2022 layoffs isn't immediately in the cards, but the situation isn't proving. However, the class of 2024 should keep an eye on the rearview mirror. There's been huge growth in business analytics and data science programs. As the funnel continues to get more crowded at the top, job competition will only grow, especially if the market doesn't bounce back progressively. So basically what that means is there's more and more colleges out there that are realizing the value of having a data type program in their school. You're going to see more and more students getting into those types of growth, into those programs, which means there's going to be a lot more competition as well. So what we're hoping is that the job market keeps up with the amount of talent that we're going to have available. I will share that with you. It's a combination of resources that I'm using. So let's look at the top 10 companies based on number of openings that they have. So if you're in the market and you're looking for a data job, these may be some companies that you're interested in looking at. You're going to notice that the IT software development and recruiting sectors are predominant. DiverseLinks is a staffing and consulting company and right now they have 200 job offers available. Recruiting from Scratch is a recruiting service provider in the IT sector with 31 jobs available. Accenture is an IT company with 27 jobs available. Symmetric is a software development IT recruiting and upscale firm with 24 jobs. Discover is a finance with 23 jobs. Deloitte the largest professional services network with 14 jobs. Oblastone Energy and independent trading firm with 10 jobs. Server is a peer-to-peer payments technology business with 7 jobs. Aditi Consulting is a recruitment company with 5 jobs. And Pacific Northwest, one of the US Department of Energy's national laboratories with 5 jobs. Just to give you an idea of the companies. Now I will tell you, if you've never thought about getting into the energy sector for data, to me that's the most exciting sector. That's my opinion. And the reason why I say that is because I had the event, the availability of a energy company and they were showing us how they use their data. And one of the things that they showed us was a grid of a neighborhood. And on that grid you could see there's one pocket where there was like a red light showing up. And what this was showing was someone had just purchased an electric car and they were using more electricity than everyone else around the neighborhood was. And they were able to determine how much electricity they were using and they had to determine how much electricity they had to give. So they're trying to determine if this is becoming a trend, they're going to have to build up their network to be able to handle the electric output that people are going to need. And they were showing us how they were using that data to determine that. And it's just fascinating. Like if you're into data man, that's the place to go. It's really, really exciting stuff that they're doing with data. I encourage you to check it out. See for yourself if that's something that you're interested in. And you can see that there's a couple companies in this list that deal with energy. So that's a good sign as well. Okay, so here's the number of job offers for industry. So you're going to see that job offers span 50 seconds, 50 different sectors, but they're not evenly distributed. The most significant demand is in the IT and technology sector, which according to LinkedIn is 49% of the jobs that they have listed is in the IT field. The financial services and staffing recruiting sectors are far behind accounting for only 14% and 11% of open positions respectively. 4% of job offers are in the industrial sector and 3% are in healthcare. With 18 openings, defense accounts for only 2%. Biotech education, logistics, oil, entertainment, retail, energy and real estate only have about 1% of job openings each. So you can imagine that they are just getting into the field of data. They're just now realizing the value that the data can bring to these types of organizations. So in my view, you're going to see more and more jobs opening up in these sectors. Once they start to get more mature like other companies are, you know, realize what data can do for them. One of the industries looking for data scientists shows that more businesses recognize the benefits of data science. The best industry for your data science career might not be the one with the most job openings. So consider other options too. So that's why I give you the example of energy. Maybe you never thought of working for an energy company before, but I can tell you they're doing some cool things with their data. This chart is a little hard to see, so I'm just going to briefly explain it to you. It's basically showing you where the data science jobs are and we already talked about California has the most. The percent of all of their ads for jobs, 13.6% of them are for data science and there's currently 110 jobs as of my data that I found that are available right now in California. Texas is next, Virginia is next, and New York comes right up after it. Are you going with that? Got New York. New Jersey, North Carolina, Massachusetts, Illinois, Washington, and finally, we end with Georgia. So compared to the 2020 data, nothing has drastically changed. California has been at the top since we've been looking at the data. Next came Virginia, Washington, and New York. The top 10 states for data scientists are the same just in a slightly different order. So hopefully you're interested in working at one of those states because those are the states that consistently have the most openings for data science jobs. All right, now let's get into what can employers expect from a data science candidate? So these are the types of degrees that employers are looking at for people to have when they're getting into this field. Data science is a relatively new field, although it's becoming much more mature now than it has been in the past. It's been around now, I would say, for a good five years at least. So employers don't expect every person may hire to have a data science degree. As a matter of fact, when companies were first realizing that they wanted to use their data for more than they were using it for, they would just pick people in their organization and they would say, you're a data analyst now. We want you to work on our data. So the problem that the companies were having, and this is what they tell me employers, is we have a lot of data people on our staff. And what they're able to do is pour a lot of the data from our versus that we hold data. And what they do is take all that data and throw it on my desk and say, here's your data. So I don't know, I'm supposed to fill in that data. That's what they tell me. So they're looking for people that can do three things. And this is what I encourage you to focus on if you want to get into the data field. And around the data, they need someone who can look at the data and figure out what the data is telling them. Where are our best products, worst products, best areas, worst areas, best time of year, worst time of year. They need someone to visualize the data. As we talked about, they might try to see it and charge them numbers. And finally, they need someone who can present their data. It's very important. They need someone who can present to executive level employees what the data means. So analyze, visualize, and present. If you can do those three things, and you can do those three things, well, you're going to be fine. And those are the types of things that I encourage students to highlight on their resumes, because that's what I know employers are looking for, because that's what they tell them. So even though they don't require a data science degree, they do refer candidates with higher education in a related field. So you can see they're also looking at students who are going into statistics or computer science, engineering, mathematics, even economics, data engineering to a certain degree, and even architecture, too, which is interesting. So really, when looking for, I mean, if you're doing an architecture, but you can still show that you can analyze, visualize, and present data, who cares what degree you're in. You can believe that you can do those things. They're going to want you on their staff. So here's the skills that employers are looking for. So as the graph shows, Python and SQL lead the way 100%. There are must for every data science role, even if they are explicitly mentioned in the job description, which, by the way, most job descriptions have either Python or SQL or both. The number of job ads mentioning Java and other software engineering tools is increasing because of the growing importance of data engineering and machinery in the data field. So my computer, from what I've seen, if you have, I always tell students, if you come away with five skills and five things, you're going to be fine. So Python, SQL, and R, those are the three things I'm working with data. You have those skills you're going to be fine. But also, to have low and power BI, if you have those five skills, you're fine. Like, I'm going to be able to get you a job. But you've got to be able to have those five skills, and you've got to know those five skills clearly. Because in an interview, you're going to go through what's called a 10 up around, and the employer is going to expect you to open up your laptop and start coding in Python. And if you can't do that, then you're not going to be able to go through the employee. So you've got to be able to do things on the fly. You've got to know that well to be able to use it. So I always tell students, you've got to be able to use it without using Google. That's your focus. Now, I know that Python is equally important as I mentioned. Also, to have low and power BI, and you can never get away from Microsoft Excel. Yes? Microsoft Excel is still very, very important for you to know, which is why a lot of schools, even here in Syracuse, offer the Excel certification for free. And if you're in a high school and you're not certified in Excel, you're crazy. There's no job that you're going to take. I'm telling you, when you're not using Excel for something, you're going to be using it for something. You need to know Excel. Like that's baseline. Everyone should have that on their resume. Everyone should have Microsoft Excel certified on their resume. So iSchool is out there. It's my iSchool. Raise your hand. All right, I'm looking at you. I have a photographic memory. By the way, I see all of you. You've ever been Excel certified by the time you graduate. It's free. Check this out. This one should make you happy. salaries. Let's talk about money for a little bit. Naturally, scientists salaries depend on their seniority level, but even if you have the experience and expertise, obtaining a high-level position is challenging because there's just not a lot of high-level positions available. And as we talked about over 50% of the jobs that are available is entry-level, right? Pission to the high requirements and expectations, they are fewer than junior positions, so the competition is tough to get one of those positions. But man, if you can get into it, it does pretty well. Good. Good salary. Make this salary in Syracuse. You are king, queen. You have anything you want. Here. Pay off your house. Pay off your house in Syracuse. Now, my house. All right, so let's talk about iSchool specifically now, since when you raised your hands, most of you were iSchool students, not all. Data science experts are needed in every job sector, not just in technology. As a matter of fact, the five biggest tech companies, Google, Amazon, Apple, Microsoft, Facebook, only employing one half of 1% of US employees. Think about that. One half or one percent. So when you people go to Google, Amazon, Apple, Microsoft, Facebook, you can see why it's so hard to get into those companies. Everybody wants to get in there, but it's only one half or one percent, right? However, in order to break into these high-paying and demand roles in advanced education is generally required, data scientists are highly educated. 88% have at least a master's degree. 46% have PhDs. And while there are no more receptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. This is why the School of Information Studies has invested in not only a master's degree in applied data science, but also just recently got approved to offer a bachelor's degree in applied data analytics as well. So that's pretty exciting for undergrad students. Definitely a good thing for our grad students that we have that as a degree. All right, talk to you about data for our students. So here's a four-year overview of how our students have done these. This is just for data science. Our students who have graduated with a applied data science degree. So placement rate means that they either got a job or they went back to school. So if you got a graduate degree, most of them got jobs. There's one or two that went back to school. So you can see that we're in the high 90s, especially in 2022. We did pretty well. 2020 was during COVID. So it wasn't as great as we had hoped it would be. You can also see that the average salary for our data scientist students, it has drastically gone up as well. So these are stats that our students have told me that they have got. They told me they got a job. And then they told me this is what their salary is. Okay, so this isn't something that I made up. This is something that students are telling me. And it's the average, the average salary. So you can see the green line is the average overall for all grad students. And then you can see the orange line is what data scientists are doing or have done. Employers just showing you the top four at Amazon, one 360, the one EY, and Goldman Sachs. Those are our five. And it's been the same for two years already. These are the companies that are hiring our grad students. Now keep it on, you guys, that these companies do not hire like 10 grad students. It's like we have a lot of companies that are onesy twosies. They hire one student or two students. So it's not like I'm going to say Amazon hired five of our students last year. They hired one or two students. But overall, these are the companies that we consistently see that are hiring our high school students. And we data science used to be EY and Deloitte was solid, solid grad, cross the board, 20 grad students, no problem. And then a couple of years ago, they completely flipped the script. And they said, we're not hiring grad students anymore. We're hiring undergrads. That's really weird. So now we, if you look at our data, it's now it's 20 undergrads. And it's one grad that they hire each year. It's really weird. So we start telling enrollment, stop saying that we're going to go to EY and Deloitte because they only hire, they only sponsor certain positions. They won't sponsor a lot anymore, like they used to. Okay. So finally, we're perfectly hiring. Man, we're going to design this specialist. And as you can see, there is a growth chart from 2021 to 2030. When the decision-making is becoming the norm, it's more and more companies realize the importance of big data for business growth. But this realization comes to need for specialists to help leverage the informational goal line, small, mid-sized and big businesses sit on. The amount of data and the need for someone to generate meaningful insights from it is likely to continue. So the data scientist job will continue to be in demand. In fact, the demand is still growing. The US Bureau of Labor Statistics estimated that the employment rate for data scientists will grow by 36% from 2021 to 2031. This is a mind-blowing increase compared to the average growth rate, as we said, of 5%. And for all these skeptics out there, show rapid technology development will inevitably lead to the automation of some of the data science dysfunctions we see this happening for many roles. People use chat GPT to write code, generate marketing and business strategies and so on. But behind every new technology is a highly-skilled team of tech and big data experts. So the much-enriched jobs will be those involved in creating such technologies. The skills required to succeed might change, but the future of data scientists is bright. Data and business analysts, machine learning and data engineers and other data science roles will continue to be in demand. In my opinion, that is the current and future state of getting a job in data science. Thank you very much. Appreciate it. You thank the outlook for the city of Syracuse and the surrounding area. I know that there's Syracuse surge and microns supposed to be coming and all that stuff. What do you think it looks like around here in the future? So I think the future is just as bright here as it is in any other place. I think that companies here are also realizing the value of data. We are establishing a lot of relationship with local companies who come into our career fairs and they come in and table in our lobbies. I'm looking for data scientists. So we're noticing more and more that they are getting more interested in especially what the iSchool is doing. Unfortunately, the iSchool doesn't have as good a reputation as engineering or Whitman does, not in terms of anything we did bad, but in terms of just they don't really know what we do. So once we can educate them on what it is that we have to offer, we're getting a lot more interest. And I can tell you that micron for us is going to be huge. So when micron comes in, they're going to be looking at engineering and the iSchool to help them with a lot of the jobs that they need to build. We're all very excited about that. I think we are in the, I have to call it right now, some sports like sports. Is there increase in sports analytics over years? Is there been a downfall or the specific areas in sports, which are like more exciting, like more jobs are coming, whereas in other sectors it's not that much in sports. Let's say, maybe we have more than other ones. So from that point to now, still, they're realizing the value of data, right? Every organization uses data to that extent today. So you can go to any team and they are using data to their sports extent. So there's no, there has been no drop off when it comes to that piece. It's huge. It's very popular. And as a matter of fact, we just had an undergrad who went to the New York Rangers as a data analyst. He just got it this summer. So he's very excited about that. So the opportunities are there. And if you're interested, like me, can I do to him? I think this gentleman had a question. Yeah, that's a great question. I'm going to give you my opinion. So I have 24 years of experience in the IT field. That's where I came before I came to Syracuse. I also talk to recruiters on a regular basis. You don't need a graduate degree. You just don't. You can, you'll be just fine being a bachelor's degree and being able to be successful, especially in the data industry. So the students who are getting graduate degrees is because the programs weren't offered in the areas that they're in. But if an undergraduate data science program is available, you absolutely want to jump on that 100%. Now, I would say for the higher level levels of maybe data engineering, data scientists to a certain degree of PhD can certainly help you in that role as far as making more money goes. But you can still reach that level just from your, the base degree that you get and the experience of doing that job. That's my view. I guess great minds, because I kind of like what I was going to ask, but it's more specific to machine learning law. I find that I'm, I'm in my last class before I go to the flight in science. And so I started looking at jobs, you know, and first of all, it seems like you have to have all those skills in one person. Because if you have there, plus more, that's the impression that I get. That is that, you know, I felt like the entire idea of science course was at least 50% focused on machine learning. So, okay, I'm going to get out of here and get a job in machine learning. But while looking for the job, I got the impression and I couldn't go on and that's why I'm asking the question, violation. Who do you feel like there's a preference for PhD graduates when it comes to working with machine learning engineering and, you know, just thinking of the models, working of the models in general, or I'm asking the supplies. I mean, what's the real point of doing that? Yeah, so I have not seen the need for PhDs. I haven't had companies clamoring looking for PhDs so at all. So my answer would be absolutely not. I would not invest in that. You don't need to. My argument to you would be I can take the skills that you have and I can help you get to where you need to be. There's a way to do things that's going to help you. So oftentimes it's not but the skill that you have is how are you translating a model so that in four years I recognize those and those are things that we can help you with in career services. All right, we good guys? All right, have a great weekend. Enjoy today. Let's get a good call. If you don't have any of your coat, make sure you go out by one. You guys know who I'm talking to and I will see you at the ice boiler on campus. Thank you very much.