 Well, good afternoon, everyone. Thank you for lasting this long and making it through the couple of days that we've been talking about for data science. I am now going to be talking about the career aspects of getting an advanced degree in data science. My name is Jeff Fouts. I am the associate director of career services and data analytics here at the iSchool. And I'm going to be sharing with you some information on where the industry is going, what the future is going to be, and what statistics say for the different types of jobs that you can get with a applied data science degree. If you do have any questions or comments throughout the presentation, I do encourage those. So please feel comfortable to ask them or say them. I have a presentation prepared, but we can certainly talk about anything that you'd like to discuss as we go through the presentation. OK, so this is a slide that I'd like to begin with talking about data science, because I found this quote, and I really liked it. It's a matter of polling data, devising a solution, and then implementing that solution. Basically, that's what we're talking about when you're getting into this industry. Quick history of data science. In 1962, there was American mathematician John Wilder Target, who used the term data analysis for the first time to describe what we know as modern data science. However, only after two decades in the 1980s, the Chinese statisticians and the Chinese Academy of Sciences in Beijing used data science for the first time to mean statistics. C.F. Jeff Wu, a Chinese statistician in the year 1997, emphasized the need to replace statistics with the word data science. While statistics was sometimes considered to be limited to reading data and accounting, data science, according to Wu, was a much wider, dynamic, and interdisciplinary field. This field of data science involves statistics, mathematics, computer science, information science. And in data science, one not only collects data, but designs it and analyzes it as well. So I thought the history of where the industry has started and where it is today was relevant to our discussion. OK, so the way that I see the field of data science is just a matter of demand, supply, and growth. And we're going to talk about each one of these aspects. To be a successful company in the 21st century, you have to use data to your advantage. 10 years ago, many companies were performing data-driven decision-making by using Excel to analyze data. But now, companies have access to and use data crunching tools, such as Google Analytics, for example. Google Analytics is digital marketing cloud-based services. We have Tableau and Power BI, which is data visualization tools for business intelligence. And we have Python and R, which are programming languages used to perform complicated analysis with a few lines of code. The need for skilled data scientists cuts across nearly all industries with particularly strong demand and opportunities in such fields as health care, for example. And the gentleman on the panel who spoke was talking about pharmaceuticals, which has a dotted line into health care. The health care sector represents one of the most important industries for data scientist involvement. Not only does an estimated 30% of the world's warehouse data come from the medical field, but the opportunities for improvement could save the industry as much as $300 billion annually. Working in the health care industry as a data scientist means more than just efficiency improvements. It can mean lives saved because of this data scientists are flocking to this industry. Another industry is transportation. In transportation, data scientists can make life-saving improvements from fully autonomous vehicles to internet of things sensors, improving the driving experience. Data-driven solutions are necessary for safer, less pollutant transportation practices. In fact, 60% of surveyed experts in the industry say the application of IoT and transportation, internet of things, will boost health and safety outcomes. These outcomes are the results of machine precision that only data scientists can provide. There's also supply chain management. When the COVID-19 pandemic struck, it illustrated the need for adaptive and transparent supply chain features. Across all kinds of industries, suppliers, manufacturers, and delivery drivers struggled to keep up with who was still operating and where the product needed to go. The result was a confusing, often delayed mess. But for supply chain managers operating with the help of data scientists, supply chains can function more smoothly. So this is just an example of the types of industries that are looking for and utilizing data scientists. The largest companies in the entire world are data science-fueled enterprises. Take a look at Google, Amazon, and Facebook. Each use data science to create algorithms that improve customer satisfaction and maximize profits. Google uses the ranking of web pages to ensure the top links have an answer to any desired question. Amazon has recommendations of products based on consumers' past behavior and interests. Amazon is a prime example of just how helpful data collection can be for the average shopper. Amazon's data sets remember what you've purchased, what you've paid, and what you've searched. This allows Amazon to customize its subsequent homepage views to fit your needs. For example, if you search camping gear, baby items, and groceries, Amazon will not spam you with ads or product recommendations for geriatric vitamins. Instead, you're going to see items that may actually benefit you, such as compact camping high chair for infants. Facebook has targeted ads. They know the sports you like, preferred price range, food, et cetera, to increase market success. In the end, the main reason demand is still high is because if your competitors are relying on data-driven decision making and you aren't, they will surpass you and steal your market share. Therefore, companies have to adapt and employ data science tools and techniques or they will simply be forced out of business, meaning data scientists are a must in 2021 and beyond. So the demand is there for data scientists. There's no doubt about that. The supply of data scientists, however, is low. And it's because the field of data science is still relatively new even in 2021. 20 years ago, it was impossible to learn data science because of slow internet connections and low computational primitive programming languages. By 2025, experts predict that 163 zettabytes of information will be generated, an incredible number prompted by the explosive growth of connected devices and enhanced networks. For data scientists, this means new avenues for streamlining business processes. This exponential growth and interest in the field weren't possible to predict and traditional education was not ready to meet the needs of those who wanted to learn this growing field. Very few programs were created to educate aspiring data scientists. This shows, as research suggests, those who get into the field usually transition from other fields such as business, psychology and life sciences. Most who transition learn their skills through self-preparation by reading books and taking online courses. Those who get into data science have the advantage of starting a career path in which there are more open jobs than qualified candidates to fill them. In fact, data science jobs remain open five days longer than the average for all other jobs. I find that very interesting. This points to the fact that there is less competition which results in recruiters needing extra time to find the correct candidates. These correct candidates are in luck as most will only need a bachelor's degree to get hired. The low supply has resulted in 61% of data scientist positions that will be available to those with a bachelor's degree and only 39% will require a master's degree or a PhD. So now let's talk about the growth. So we know that the supply is low, the demand is high. So what does the growth look like? Per LinkedIn, there has been a 650% increase in data science jobs since 2012. Glassdoor gives evidence to this claim as they had about 1700 job postings with data science being the primary role in 2016. That number rose to 4,500 in 2018 and sort of flattened out in 2020 at around 6,500. COVID-19 was the big story in 2020 and presumably the reason for this flattening out. Overall though, the tech jobs have proven to be resilient during the pandemic, which is now in its 10th month. All right. Demand for data scientists is still high while supply is low. According to IBM, this tendency will continue to be strong for years to come. Another credible source that agrees with this statement is the US Bureau of Labor Statistics. The US Bureau of Labor Statistics sees strong growth in data science field and predicts the number of jobs will increase by about 28% through 2026. To give that 28% a number, that's roughly 11.5 million new jobs in the field. Okay. So that talks about the demand, the supply and the growth. And now I'm gonna share with you information about iSchool specifically. Sure, what's the question? Sir, you know, for me it would be much higher whereas for you it would be much lower. Sir, do you think the data is being used in a negative way in order to do this often to people who need it so much? So, or do you have any comments on that? So I'm certainly not the expert on how healthcare is using data scientists in the field. So basically what you're talking about is using data science for the purposes of making money, profiting, right? I would say that that would fall more along the lines of, I mean, because healthcare is a very general field. So I could see that more along the lines of the, specifically in the pharmaceutical field, more so than in the healthcare in general field. I mean, let's face it, why are we in business to make money? Of course they're going to be searching for ways to make as much money as they can in the fields that they are. And that's just the nature of the business. But I also think that there is, that data science is also being used in positive aspects as well as I was alluding to in what I was talking about in trying to help to save lives by being able to apply the data that's collected on patients and how they're able to better use that data to better more efficiently help the patients that are in need. Data science experts are needed in virtually every job sector, not just in technology. In fact, the five biggest tech companies, Google, Amazon, Apple, Microsoft, and Facebook only employ one half of 1% of US employees. However, in order to break into these high paying in-demand roles in advanced education is generally required. Data scientists are highly educated. 88% have at least a master's degree and 46% have PhDs. And while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist. And this is reported by Katie Nuggets, a leading site on big data. 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 here is some data on our master's degree program in specifically applied data science. Keep in mind that this is a relatively new degree and we just started collecting statistics on it in 2019. So that's why you're only seeing 2019 and 2020. 2020 is asterisk because of COVID. So it's kind of a skewed number. It doesn't really represent what the industry is saying is available out there. So you're going to see on the left hand side the categories and then you'll see the numbers show up for each year, class of 2019, class of 2020. Placement rate means those students who got some type of job, whether it be employed full time, maybe they got were in the military or one of those two things. Response rate means how many students actually filled out the survey. So we are, you can see that the response rate is really high, which means that the numbers that we got are really solid. So this isn't just from a very small population of students who are collecting the data from. So you'll see that in 2019, 80% of our students who were in the data science program got employed full time in some type of job. And I'm going to share with you what those jobs were. In 2020, it was just 57%. But I also want to share with you when they obtained employment. Most of them got employment zero to three months after they graduated. Same thing in 2020. The salaries, the average salary in 2019 was 82,845 for our students. And in 2020 it was 86,625. And then you'll see the two year rolling average. So this gives you an idea of what's happening least in Syracuse at the iSchool with our applied data science students. Okay, so here's top employers in 2019. Amazon, Deloitte, ZS Associates, EY, Morgan Stanley, PWC, and Stratech Global Incorporated. What constitutes a top employer? Any employer that has employed more than one of our students in our program. That's what constitutes a top employer. And then you'll see the job titles that they're getting, advisory consultant, business analysts, business intelligence analysts, business intelligence developer, business development engineer, business technology analysts, data analysts, data scientists, risk assurance associates. Those are the types of jobs they got in 2019. In 2020, once again, it's Amazon, Tata consultancy services, and ZS Associates as well. All this information is coming from students. Top jobs, analysts, business technology analysts, data analysts, data scientists, software engineer, solutions engineer. So you can see the types of jobs that they're getting and types of companies they're getting jobs at. Okay, now I'm sharing with you the data for our most recent crop of students from 2021. So this is both face-to-face students on campus and students who are online as well in our applied data science program. So you're going to see a total of 215 students that are in the program. Right now, as of today, roughly 83% of them have full-time jobs since graduating in May of 2021. 3% decided to go back to school, 1% are in the military. I think there was one student who said they're not looking yet, they're not ready. And there's 12%, 13% who are still looking for applied data science jobs. In 2020, the average salary is 89,984. So we're starting to get much better. The industry is starting to pick up again after 2020. So that's pretty damn good. Thank you. So you'll see the minimum salary is 25,200, which I'm taking numbers from what the students are giving me. I have a feeling that that was a mistake. I think they put in something wrong there. But the maximum salary is 208,000. The median salary is 76,000. Here's our job titles that our students are getting in 2021. Associate data scientist, business intelligence, data analyst, data engineer, data scientist, machine learning. Now you're starting to see some of these titles that the panel was talking about before I came on. Senior analysts, senior software engineer, systems analysts. So there's a whole range of jobs that you can get with this type of degree. There is a, I don't even think the industry really understands how to define a data analyst from a data scientist, from a data engineer, which is why we tell our students don't focus on the title, focus on the requirements that they're looking for. Because a company who's looking for a data analyst may call it a data engineer and just not realize what they're calling it. Or they'll call it a machine learning engineer. Who knows? But always look at the requirements versus the job title. Okay, here's our top employers. So you can see Amazon's very good for us at the iSchool. Amazon Web Services specifically is getting into the picture. Blend 360, Boeing, Deloitte, EY, IBM, KPMG, Tesla and ZS Associates. So you're starting to see patterns of companies who come back year after year and are looking for our students, especially in the data science program. Okay, the last thing I'm gonna show you is in the industry, the different types of careers that you can get with a data science degree. And you'll see the average salary as well. So you have data scientists. You have machine learning engineer. And these are things that we share with our students as types of jobs that they can look for while they're searching for jobs and internships. Machine learning scientists, and it talks about what it is that they're doing in each one of those jobs as well. Applications Architect is another one that we haven't seen. Enterprise Architect, Data Architect, Infrastructure Architect, Data Engineer, Business Intelligence Developer, Statistician and Data Analyst. Finally we get to that one as well. And for my last slide that I'm going to share with you, this is from the US Department of Labor as I had alluded to earlier in one of my slides. So this is the percent change projected from 2020 to 2030. And this is the employment change projected in thousands from 2020 to 2030. And I wanted to work us all the way down to data scientists here. And you'll see that there's a 31.4% percent change showing you the growth in the industry over the next 10 years. And then you have this 19.8, remember this is in thousands of employment change with the median annual wages of 98,230 dollars. So once again, showing you the explosive growth for those of you that were thinking about being in the wind turbine industry, that's really the best one to be in right now because of the explosive growth of wind turbines. But data science is still one of the top 10 which I thought was pretty good. All right, there you go. That's my presentation. There was a question in the chat about kind of overall asking what your thoughts on how somebody coming into either a master's or a PhD with prior job experience would change any of those salary amounts. What kind of prior experience would be the question? They didn't get into specifics about it. Sorry, are you able to generalize it? Well, maybe if they had related experience versus if they were doing something unrelated. I can tell you even without experience, just getting a master's degree can get you another 10 grand in salary. So your experience should be able to get you more than that. So the master's degree is worth that much more and then your experience should account for a little bit more than that. And that's what I would try to negotiate for when I got my offer. Oh, good. Yeah, very good. So thank you. So you're just justifying my numbers. I appreciate it. What's she giving out? Yeah, yeah. I do have information on that. I don't have it with me. But it's so the areas that our students go is normally West in the California area, Chicago, Boston and New York City. We are trying to go into the South more. So actually next year, we're looking at going into Austin, Texas, which is becoming a huge area for companies. A lot of them are leaving those cities I just mentioned and they're going to Austin. But right now, that's where our students are going. And I'll tell you too, still a lot of them are remote. So I love when students have remote on as job experience because a lot of the jobs or internships they're taking now are still remote. So the fact that they have experience in remote only helps them. Yeah. Yeah. Yes. Well, the one thing that I was trying to illustrate was it's not just those companies that are hiring. There's all types of industries that are hiring. But these are the companies that have been hiring our students. So, and it's, they've hired more than one student, right? Could be two out of all of those students that they hired. But it is competitive to get into those but I can tell you that there are literally thousands of data science jobs available right now that if a student comes into me and talks to me about jobs and internships, I just pull out the jobs and I send it to them and say, here, just start going through the list and finding things that are interesting to you. Like there's not a problem to find jobs opportunities. Even if you want to get into one of those major companies, you know, depending on your background, what you've done, what you're studying and what you're fluent in as far as those skills, you have a really good shot of getting in there into one of those if that's what you want. As far as the numbers go, there's a reason for that. So you'll see we went from 131 to 56. Most of our graduate students on campus are international students. So in 2020, they had a real hard time getting permission and come to the US with their passports and getting their H1 visas. So we didn't bring on as many students in this class as we normally do. So that's the reason for these two discrepancies. For 2021, I just recently started, I added in the online students population as well. I didn't do that for 2019 through 2020. So you're seeing a combination of both online and on campus students here. And going forward, normally what I would do is break it out and I would show online and on campus. But for this presentation, I just put them together. But you'll start to see, are you here on campus? I'm not a student. You're not a student. But if you come back and just walk around, you'll see our stats up on the board and you'll see it broken out by online and on campus and how it shakes out. So who's going where? Who's getting jobs? And Jeff, there was one more question online. Do you have any data about summer internships? So we don't track internships. So I don't have any data on that. And the reason why we don't track it is because not every student tells us that they got an internship. So the data is kind of skewed. We do have someone in our department who does know students as long as they tell her that they have an internship and they want to get credit for it, she tracks it. But for our data science program, they're not required to get internships. So it all depends on if they want one or not. Yeah, that ain't. You guys good? Can we do one more question that just came in and then we'll end it? So the question was about, if you can explain a little bit more about what services your office provides, both the current students and then also alumni? Yeah, sure. So for current students, we provide services that help students with their resumes and their cover letters, their LinkedIn profiles, and services such as networking, how to interview, how to negotiate, basically any aspect of finding jobs and internships. So whatever you need help with, we are able to help you with those things. Even if you are confused or you're not sure what it is that you want to do, we can help you with that as well. So we, I always say that I would like to be as much a part of your career journey as you would like me to be. So include me in as much as you want me to be involved in. We have a separate career services division for our online students. So who we speak with on a regular basis and we work with each other and try to communicate with each other to make sure that we are also supporting that student base as well. Alumni, excuse me, are more than happy. We are more than happy to support our alumni as well. So alumni still have access to Handshake, which is the tool that we use for all things career related and they are still able to make appointments with us and come in and see us. And they have access to all of our resources also online. So those are the different ways that we support those different audiences. Yeah. So those are companies who, they are larger companies. I will say like EY and the Deloitte's, they like to lock in the students early so they like to fill in in the fall semester to get ready for the crop, for the summer jobs. So they try to lock in our students early so that the students can't go out and keep looking for jobs. So when you see that, that's the reason. A lot of our companies are aggressive and they wanna lock our students in so they'll stop looking and they got them. So they are interviewing heavily in the fall and they always tell us, we wanna have our group filled by Thanksgiving. That's their goal. So all those students that you see and a lot of them stemmed from internships that they had with those companies and just got full time offers in August. Yeah. All right, well thank you very much. I really appreciate you taking the time to come in here and listen to what I had to say. I appreciate it.