Based in Los Angeles, Leon is a data scientist, professor, and lecturer. Over a decade, he has delved into the intricacies of business risk assessment, sales projection, and growth forecasting, spanning diverse domains such as finance, education, personal development, and healthcare.
Channeling his vast expertise, he has played a pivotal role in enhancing these sectors. Leon’s classroom isn’t bound by walls; he’s had the privilege of collaborating with multiple universities. There, he’s taken on the mantle of guiding students, emphasizing the importance of presentation skills, especially in the domains of finance, and applied mathematics.
For him, this journey transcends mere knowledge sharing; it’s about nurturing the next wave of thought leaders
Here’s a quick summary of key takeaways:
In this episode of TechGuide, host Ryan Atkinson is joined by guest Leon Shpaner as they discuss Leon’s journey from odd jobs to becoming a data analytics expert. They delve into the importance of communication in business, the challenges of working with data, and the significance of taking initiative and thinking outside the box. Here are 7 key takeaways from the episode:
- Seek opportunities for growth: Leon’s career trajectory changed when they were given the freedom to pursue their ideas and develop their skills in reporting and analytics. It’s important to seize opportunities that allow for personal and professional growth.
- Learn coding languages like Python and R: While many companies may not use R in their analytics processes, learning coding languages like Python and R can create excitement and build confidence. However, practical experience is also essential to truly understand the complexities of working with data.
- Understand the business context of data: Working with large and messy datasets requires more than just technical knowledge. It’s crucial to understand the business context, filter data appropriately, and seek guidance from stakeholders to identify relevant columns.
- Embrace statistical concepts: Simply producing numbers is not enough. To work effectively with data, it’s important to have a solid understanding of statistical concepts and methodologies.
- Take proactive steps in your education: Continuous learning and seeking out opportunities to enhance your skills are essential. Taking notes, researching unfamiliar concepts, and asking questions can help deepen your knowledge and effectiveness in your field.
- Create a positive work environment: Both managers and employees play a role in fostering a constructive work environment. Good managers believe in their employees, recognize their work, and show respect and kindness. Employees should also look for opportunities to contribute positively and exercise good judgment.
- Take action and start working on projects: Overthinking and fear of rejection can hinder progress. It’s crucial to take action, work on projects, and build a portfolio, even if it’s not perfect. By starting small and taking chances, you can gain valuable experience and open doors to new opportunities.
Listen to the podcast here, or find if wherever you get your podcasts:
Ryan Atkinson: Welcome everyone to the Tech Guide podcast, where we give actual advice to those wanting to break into tech or looking for their next gig. We have Leon Spanier on the podcast today, who’s currently working as a data scientist at UCLA Health. Welcome, Leon. Super, super excited to have you on today to talk a little bit more about your background.
Leon Shpaner:Thank you, Ryan. Happy to be here.
Ryan Atkinson: And one thing that I love about you when you’re like talking beforehand or like the form so you’re currently a data scientist at UCLA health, but this isn’t what you initially wanted to pursue. I’m curious, what did you initially want to pursue?
Leon Shpaner: I gotta tell you the truth.
I had for the longest time, I had absolutely no idea. And I mean this wholeheartedly. I had no idea what I wanted to pursue. And I hated having been asked that question from time and time again, and not having an answer for, but I definitely have an answer for it. Now.
Ryan Atkinson: Yeah. So yeah. So what did you want to pursue then?
Leon Shpaner: Coming into UCLA, there weren’t at the time in the early 2000s, there weren’t a lot of options as far as what majors were concerned unless you’re going into stem. And for me at the time, that was absolutely out of the question. So. I picked economics because it seemed like a very sexy major, if you will, and it involved math.
And I thought, okay, this is as close to STEM at the time. It wasn’t STEM nowadays. It’s been actually recently designated as STEM. As close to STEM as I [00:02:00] can get without having to really… Dip my feet into chemistry, physics, and all those very difficult sciences. And that’s something I’ve been trying to avoid, even though my brother is a doctor and that’s a whole separate story.
Very proud of him. I think I modeled myself after him in so many different ways. But yeah, like everybody else the thing to do was to go into investment banking right before the 2008. Market collapse, but I, there weren’t a lot of resources out there like we have now. YouTube and all these different websites, social media channels, everything’s being thrown at you at once now guiding you where to go, right?
At the time, no one told me, Hey, you got to come in as an analyst first through two years, then there’s a certain trajectory you got to follow. So I missed the boat on a lot of things. And my GPA wasn’t exactly up to par with their standards at the time. So, and then 2008, what happened at that time didn’t help either.
So I got kind of pigeonholed in accounts receivable. And that’s where I ended up for many years before I really decided what I wanted to do.
Ryan Atkinson: Yeah, that’s super interesting because then you’re in this accounts payable role and yeah, very timely time to graduate with an economics degree right before the financial crash.
But yeah, so you’re in this accounts receivable role. And then you got, you went to the university of San Diego to get a master’s in September of 2020. I’m curious, like what led up to that decision?
Leon Shpaner: So, let’s back up a few steps before that from, I think I was very lucky in 2013, so I had a lot of different odd jobs from 2007 all the way through 2013.
I had a law firm, did accounts receivable, I worked at some workers compensation, every little thing you could find under the sun pretty much. Just taking on jobs to make money, but in 2013, I consider myself [00:04:00] lucky because I was hired by someone who truly believed in me and it gave me a lot of free reign as to what I wanted to do with on the reporting side, and they never shot down any of my ideas.
And so I really kind of just use PowerPoint to make different analytics trajectory, animated trajectories with. Tracking our collection rates and things like that. And I started to feel because I was treated very well by my manager and they kept promoting me and giving me raises. I started to feel this.
Hunger and this itch, like this fire inside of me saying, keep going, keep going and I always say this, it all started with the V lookup formula in Excel, and when I saw what that simple formula could do in terms of matching data, I said, gosh, if you can do just this one thing, this one formula, what else can you do?
And we’ll just put, put in an equal sign on one line. You’re basically writing code. I want to do a lot more than that. And so I developed this hunger for data and. I saw this ad on Facebook from Cornell University. It’s a Get Your Data Analytics Certificate. And I never click on those ads because everybody knows they’re all drunk and but something inside of me says, click on it.
I clicked on it and I took the program and got my certificate. And I was talking to my counselor and he said, you sound really interested in teaching because you keep asking me all these questions about, well, who’s doing what in the back on the back end? How are they grading and assessing your work? Do you want to teach?
I said, well, now that you mentioned it, sure. And it took a lot of persistence, but he guided me to the right place. I emailed a lot of people back and forth and just with a bachelor’s degree, they let me teach the certificate program and now I’m a teacher and I got promoted to financial analyst at that company.
I started doing more analytical work and then I realized, well, this is fun when you do [00:06:00] it for the first 20, 30 times, but there’s only so much you can do in finance on the analytics side, right? You got your income statement balance sheet, cash flow, and there’s so many iterations of that you can do. And at least that company, that’s what we were doing month over month, year over year until I decided the pandemic hit and what’s next?
That’s the big question for everybody. What’s next? So I had some contacts and I already got into a few programs, but they were so expensive, before 2020, the average price, if I had to estimate from the top of my head, the average price of a solid master’s program was 60, 000. That’s a conservative estimate.
Yeah. And I said, I’m not going to I’m a, I’m a math. I’m a finance guy. Good money. Everybody’s everybody’s saying, keep doing. You can finance it, take out loans. Yeah. I’m not doing that. That’s just, there’s no way you’re going to get an ROI on that. I mean, you can, you may, it’s possible, but the chances are slim and a lot of it still depends on you.
It’s not a magic pill. Long story short, I contacted one of my counselors that I’ve been speaking to at different universities in San Diego, and he said to me Leon, listen, you’re not just trying to get a master’s degree. I know you’re calling me, you’re interested about different master’s degrees, but I know you have talked to you for a long time.
You love analytics. So I’m just going to tell you straight. We’re launching a master’s in applied data science, and I think you should be the first to apply and never look back since it’s the best decision I’ve ever made. And I love everything about it.
Ryan Atkinson: Yeah, I think what’s so cool about that too is like that really just started from like your manager back in like the early tens to like basically let you work around in Excel and like you get interested in VLOOKUP and next thing you’re the first student to go through this program.
Leon Shpaner: Sorry. I [00:08:00] don’t want to get too emotional, but he passed away. He became a mentor. He was a great guy and I owe a lot to him because it’s funny. Everybody talks about getting a college degree. And here I am with a master’s degree. The guy never really went to college, but he’s one of the smartest guys I’ve ever met in my life.
He’s been working Excel spreadsheets. And he, and I’ll never forget it to this day, even to this day, when I need to learn something new, he says, what do you mean? You don’t know? Don’t you know how to use YouTube? That’s good. Yeah.
Ryan Atkinson: Can you talk about like the role that like a good manager, a good mentor has played on like your career development and like why it’s important to find a good mentor?
Leon Shpaner: I mean, I’ve been on both sides of the fence. I’ve had destructive environments and of course, constructive ones. To speak on the destructive ones. If I had to give any advice, if you’re in that situation and I’ll come around for what you’re asking, right? If you’re in that situation, I have no real advice.
I feel for you. I’m sorry it happens. Do your best to get out of that situation. I mean peacefully, gracefully, professionally, but, but that kind of segues into what is, what, what are the makings of a good manager? My opinion, like I said earlier, someone who believes in you, someone who’s not going to shoot your ideas down.
Someone that recognizes your work. For me, I’ve been through so much that for me, just basic human respect, basic kindness, basic acknowledgement. And. I don’t need to hear that I’m doing a good job all the time. I kind of know I am or when I am and I know when I’m not, but someone who doesn’t destroy you doesn’t destroy your thoughts and ideas or questions every step that you make doesn’t micromanage you.
Someone who’s can stand up for you in a meeting and speak not just for you, but for the team, like a coach would for a basketball team, for example, in my opinion. I think that’s what makes a good manager. [00:10:00]
Ryan Atkinson: Yeah, and you really can feel when you do have a good manager compared to like, a bad manager, because a bad manager it’s a lot of pointing fingers and whatnot, but a good manager is hey, let’s figure this out together and find a solution to this problem.
And the supporting thing, I think, is like the biggest thing when it comes to good managers. They want to support your personal growth and also your career growth.
Leon Shpaner: Absolutely.
Ryan Atkinson: Yeah. Yeah. I’m curious a little bit more about like data science on the job versus what they teach you in school. So you’re the first one to go through this program.
You do really well. You learn a lot. Of course you do. Yeah. But you also now have had a lot of roles data science, like actual roles. So can you talk, talk to us a little bit more about what is the difference between starting school? What do they teach you in school and what do they teach you really on the job?
Leon Shpaner: What do you learn? Well, they expose you to a lot. Yeah, they expose you to Python as a programming language, more so as a scripting language. They also feel if it’s a good program, they want to be competitive, they expose you to R as well. But what they don’t really tell you is that most companies don’t really use R, but it’s good to know it because you’re practicing a dialect, the language, if you will, right?
And you start to pick up, especially if you’re new, all these methods, all these different methods. The lines of code, especially for data visualization, or just calling a data frame, you’re using pandas, NumPy, all that stuff. And in the very beginning, especially when you’ve never done this before in the past, if you’re new to it, it’s very exciting and it’s easy to say, Hey, look, I know how to code, right?
Watch me. I can build a model in two months and you become very confident and you start thinking, Hey, look, I can do this. But the more you go through the motions, the more knowledge you pick up, the more experience you pick up, the larger the data set is, you start to realize there are a lot of things that you [00:12:00] just cannot know unless you have experience.
Like for example you may very well overfit the data without realizing. That’s one thing that can happen. You start to notice trends. So for example, if you’re working with a large data set, it’s nice and clean, which is very rare. That’s not going to happen very often. Yeah, you’re going to have to kind of figure out how to either join the data from disparate sources or how to at least filter it down.
Filtering is very important. It’s in school. They give you. A lot of clean examples to work with and kind of guide you and the homework problem set, set it up in a certain way, whereas in the business world, the long and short of it is it’s up to you to figure out what is the inclusion criteria, right?
Especially if you’re working in a field that’s kind of forward to you, like medicine, right? The name of the columns might not be very easy to digest or read. So you have to kind of put your thinking cap on and go to the data dictionary and see what’s well, in this data dictionary, this, there’s 40 pages of that.
And so many different. Columns that look the same and you have to kind of talk to the stakeholders or your boss to figure out Well, is this one relevant? Is that one about which one’s relevant? Yeah, i’ve never worked with this one before There’s we have the same column for multiple years what it’s a it’s a lot.
It’s it’s more involved So that’s just one small example. And it’s not just about getting results. They teach you in school how to get an agency score, how to report performance metrics, but no one really teaches you how to maybe. I don’t know. Maybe there are programs that do, but I don’t think there’s 1 program that does at all.
There’s always some every program lacks and something and winning something. So it’s hard to find that balance. Yeah, that’s the bottom line.
Ryan Atkinson: Let’s say you’re opening up your own master’s program for data science, data analytics.
What are like the top three things that like Leon Spanier is going to include like in this program?
Leon Shpaner: If we can afford it, it definitely have. Talks from people in the industry, not just talks. I mean, this is what really I’m not trying to, I’m not biased here. I went to the school, but I’m telling, I’m telling it like it is.
University of San Diego actually has classes that are taught by industry professionals. So I have more of that, , include more data and I know it’s tough. Everybody thinks you can just get data anywhere, but you’re limited by ethical standards guidelines. There’s so many restrictions that you can’t really bring data from your company, but if there’s a way to Anonymize it, re anonymize it, make sure that it adheres to strict guidelines and standards that can pass Mustard, by all means, work with some data that’s more real world and just Kaggle or the UCI machine learning repository, which is a great one.
And practice more statistical concepts like performance assessment. That, that one’s huge. Really understanding how to sweep through the receiver operating characteristics curve. Not just, hey, this is my AUC. It’s really high, right? The area under the curve. Why is it high? How, what is the relationship between the true positive rate and the false positive rate?
And why does it change as you sweep throughout the curve? Understanding how all that works is more important than just spitting out numbers. And I would really spend more time on those concepts than just kind of making it like a boot camp where it I, I can only speak for what I know, right?
Some schools are better than others. Like I said, I don’t think any one school is ever going to be the ultimate. It really falls upon you as the student to independently go out there and learn what you feel you’re lacking in. And I don’t, I think not enough people do [00:16:00] that because they expect a program to define.
It gives you even a master’s program only gives you a foundation. It’s still on, on you to take it to the next level.
Ryan Atkinson: Yeah. And are there ways that you would recommend people to be aware enough of what they are lacking in? Cause I just think that’s like a, it, it can be a challenging thing.
Once you’re in the day to day, every day, you’re not really thinking from a 10, 000 foot view of what I’m doing. I mean, do you have any advice to understand the areas that you do like in?
Leon Shpaner: Absolutely. I mean, for me, I found out the hard way. I was told you don’t know this. You don’t know that. I got put up one of my first jobs in front of the whiteboard and said yeah, they said to me, do you know how to do that?
So on and so forth. So, yes, I don’t really want to show us in front of everyone on the whiteboard. And that was the most humbling experience ever had. That’s 1 way to find out. Right? Yeah. The other way when you’re in a meeting. Thank you. Take notes, like really take notes, listen to what everyone’s saying.
If there’s a term you don’t know, write it down, go study it later that day or have it planned on your reading list for the week or something like that. Because chances are it’s going to come up and you’re going to be asked that question about that. You know like if you’re in the medical field for the first time and you don’t know what a nasal cannula is, for example, write it down.
That’s the thing they put it in on. Right at that part of the term and next thing you’re talking like like a doctor far from it, but you’re talking kind of starting to speak the language and later people start asking, wow, what’d you learn? All of us. I listen to you. That’s one way to learn through experience and Google.
I hate to say it, but that’s the truth. You Google literally what you just asked. What are the most relevant topics and data science in 2023? That’s one way to find out. Write that down. [00:18:00] And talk, talk to your mentors, talk to your boss. Don’t be afraid to ask that question.
Ryan Atkinson: That’s I think that’s like going right off of that thread to what I like to do.
If I want to identify an area that like I’m not strong in, it’s like Googling something like what makes a good digital marketer. And then just see 10 bullet points that are out there, then like actually having a real assessment to honest assessment being like, okay I’m maybe not paid ads or like the best things.
Oh I know copy. I know social media, something like that. But it’s having that honest assessment with yourself to actually identify those weaknesses. It has to be an honest assessment.
Leon Shpaner: Yeah, no, yeah, no. To your point at the time, I did feel like it was a bit harsh learning the hard way. And I have certain feelings about it, of course, but.
Looking back, it’s funny to say it, but it’s probably the best thing that’s ever happened to me. Because I got the ultimate lesson. If you’re lucky, they’ll tell you. Don’t take it personally. They’ll tell you what you don’t know. They’ll have that heart to heart with you. And it may hurt, but at least you know what you gotta do.
And you go and do it, and you don’t give up. And you have to really want it. You have to ask yourself, is this what I really want? Is this what somebody else wants? And if you want it, you just go and learn. It’s simple as that. Are there
Ryan Atkinson: other, are there other examples that come to mind where you learn a really hard lesson and you’d like to share that?
Or what, does one or two other examples come to mind when it’s oh, that was a really hard lesson I learned?
Leon Shpaner: You can think of one communication. It’s so easy to say communication is key, learn how to communicate. It’s so easy to throw that around. But until there’s so many different one offs that can happen in business that you cannot control for, you cannot plan for, you cannot learn.
It’s just. It happens, right? So sometimes [00:20:00] people are difficult to communicate with. Try as you will you’re the nicest guy or the most professional person in the world, but they just will not give you the time of day, but your responsibility is to talk to them. Now, if your boss is friends with them, good luck.
That’s all I can tell you, because it’s like back, back to high school. It really is, honestly, sometimes like that. So, one time I was working, I’m not going to say for which company or where this happened, but… I have to do a project which was like fitting a square peg into a round hole. So taking financial metrics and.
Or rather the opposite, taking marketing metrics that were really meant for Salesforce and fitting them into this highly proprietary CPM software that I tried to explain to them, this is not the best solution. We already got it to do what we wanted to do. We’re automating the financials. We’re saving a lot of time every month doing that.
I did that for you. Maybe we can add some more visibility on it, some more use cases, some more different financial reports that are more nuanced. But at the end of the day, why use Salesforce when that’s what Salesforce is for? Why use it here in the software? And they just weren’t hearing. I had to go talk to marketing and a person there was never available for me.
And I felt like a complete fool just waiting outside the door or even after emailing that person. It was just constant tag. You’re it type of thing or. They just weren’t there. It was tough. And, and then when I talked to my boss about that it’s good luck, your job is to talk to this person for you.
If you can’t talk to this person. Yeah, it’s very upsetting for me, but I think the lesson ultimately was, and it’s hard for people to admit it when something like this happens, you have to ask yourself okay, how much are you willing to put up with? What’s your breaking point? And I think that’s when I started thinking [00:22:00] maybe this isn’t the best place for, because this, this is, Becoming a bit tossed.
Ryan Atkinson: I’m curious to I kind of want to shift segments. I, the toxic workplace is I, it’s really hard to be in a toxic workplace. Luckily for me, I’ve never had to be in one, but I know people that have, and it’s oh, it just sounds so miserable. And really the next step to doing that, a lot of people come on this podcast and say is make connections with people network.
If you want to get into a new role network, get out of it. One segment I want to talk to you about is like thinking outside the box when making connections. I’m just curious so what are some of the ways that you think people could think outside the box and make connections in a non traditional way?
Leon Shpaner: This is totally random, but I’ve done something like this, and I think you just have to be open to… Anything you have to be watching a documentary and they’re interviewing somebody in your field, right? What you do is you pause you write down the name you go to LinkedIn you look it up That’s thinking outside the box.
You send a message. They have Chad GPT now for that I mean to help you kind of write things advise things That’s one way. Just make a list of companies where you want to work. If you’re applying for a job, right, as you’re driving down the street, see a bunch of big lead banks, write those down, come home, apply.
But if you want to know who works where, there’s always LinkedIn. There’s always just networking events is a standard. I think they started doing those again recently. Don’t be afraid to talk to people even at the coffee shop to see somebody wearing a badge and they work for.
A lot of times it’ll tell you what their position is and just you always risk rejection, right? That’s a fear that people have, but my opinion, life is short. We only have so many, so much time and as long as you’re not doing anything to hurt anyone, then by all means just be polite and professional about it and [00:24:00] take, take a chance because great things happen.
I mean I’ll give you one example of what I did one time when I first started teaching. I had no teaching experience, but I know what I learned in Excel. Right? And I was introduced to students at UCLA when they invited me back to judge certain presentations for economics classes that they had and I was a panel judge and I saw certain deficiencies and I really felt and truly believed, hey, I can really help these students.
Maximize their potential using Excel at the time before I was more advanced, before I knew how to code and they kind of just brushed me aside, but I was very persistent. I kept coming back to these competitions and judging and I kept asking and I started emailing and I wanted to see how far can I push this.
Until they really taught me, hey, you can’t do this, just, just stop. And the funniest thing happened is, it’s like they say, be careful what you wish for because it just might come true. And they said, all right, here we talked to this professor. You’ll be teaching a seminar after his class is over.
Here’s your assigned room number. I was like, Oh my God, this is real. Now the work starts, I got to develop a lesson plan. And so good things can happen if you’re open to it, to them happening. And if you’re persistent up until a point, of course, you have to remain professional and exercise your best judgment at all times.
There’s so many different ways and I, unfortunately, I’ve seen it time and time again. Some people just, they’re like robotic. They’re very smart. Mathematically, they’re geniuses and they can solve a lot of complex problems, but for the life of them, for some reason, I don’t know what it is, but they just can’t think outside the box.
And I really wish. I mean, hopefully maybe this podcast will wake that up in them. I don’t know. [00:26:00] Yeah.
Ryan Atkinson: I think it’s really interesting because I feel like some people, this is just a feeling. Some people are just, they’re so smart where they don’t want to take the risk because they’re doing all the calculations in their head and it’s so small where it’s like, why would I take that risk?
Cause it’s so small, but great things happen when you do take risks,
Leon Shpaner: right? Yeah. And I don’t even see it as a risk anymore. I mean, I think after you’ve taken a few hits in life, people, no, I say take the hits. Get hit. It’s like jumping into the way I see it is like this. It’s like jumping into that cold pool.
It’s freezing, right? You jump in and you’re like, Oh my God, what happens after two minutes? It’s warm. That’s you don’t know how to do it. Just jump into that cold pool and learn how to swim. I don’t know what else to say.
Ryan Atkinson: Yeah. And that’s what I always think too is if you do really want to have a great career you have to be able to take those risks and be able to just strike up a conversation with people.
If it’s through LinkedIn, just saying, Hey I’m. Love your company, love your position, something like that, but you have to be able to take those risks and be able to strike up a conversation. If it goes great. Great. If not great as well. You took that shot. You took that chance. I’m curious. I’m curious, too, with, like, all your teaching positions.
We’re coming up here. The school year starting up here, probably less than a month for a lot of colleges and places, which is crazy to think about. Are, is there something that students can do if they’re entering like their senior year, their last year, whatever, to really set themselves apart to get a job after graduation?
Leon Shpaner: Start, start working on a project and don’t tell me I have no project to work on because it’s Kaggle That’s an excuse. You could take that same Kaggle project, that same UCI machine learning or whatever you find online and read a paper. On that same topic we actually do this, believe it or not in research, you take a paper, you read it, make sure that it’s related to that area of interest or that data set.
And then go [00:28:00] to the results section, study that result section and see if there’s a formula. There usually is with the coefficients and things that you can plug in. Now, put that on your data set, apply that to your data set, multiply those same coefficients, get those probabilities and replicate their results on your data set and see how they stack up to what you have.
That’s one way. To feature engineer new information and compare perform their performance to what you’re looking at. There’s so many different ways you can iterate through different examples and come up with some of your own logic and it’s better to have at the end of the day, five Kaggle projects and a GitHub repo than nothing, because that’s what ends up happening to a lot of overthinkers.
They end up with nothing and they’re still looking for a job because they can’t find that perfect project. But they’re just so afraid to do what others have done. There’s no shame.
Ryan Atkinson: Yeah. Yeah I think that is really important to Get projects and like people listening if you listen to like past episodes like people always say projects projects products Projects and data analytics data science that is the one thing that can like truly set you apart from other candidates when applying for roles. Are there other, are there like some projects that students did just for some like inspiration here? Are there projects that students have done in the past where it was like, Oh, like that was so amazing. Like people should take inspiration from.
Leon Shpaner: Oh gosh. Yeah. Yeah. I mean, I’ve seen one, a buddy of mine in my master’s program.
He managed to secure a relationship with some PhD researcher. Forget exactly what the topic of interest was, but they [00:30:00] We’re able to furnish the dataset and they did their whole capstone on that. And it was. Not from Kaggle, it wasn’t from UCI, but it’s, again, they knew how to think outside the box. They knew how to talk to people, they weren’t afraid to take that chance, and the results were amazing.
You were able to build an app, they deployed it. Yeah, absolutely.
Ryan Atkinson: Then they sold to Facebook for 50 million, I’m just kidding. But last question for you as we wind up, this has been an awesome episode, just general advice here. What general advice do you have for someone that’s young in their career, let’s just say, they’re just entering the workforce.
Workforce what advice would you give to them to really stand out in their career? Whatever’s
Leon Shpaner: in your, okay, when you wake up, whatever’s in your mind, write it down. Your mind is trying to tell you something, especially as it relates to your career. You’re asking your habit. And a lot of people’s habits are like this, right?
They, they want to ask others, they go outward instead of looking in a lot of the end. And it’s okay. It’s okay. Ask questions, talk to people. Not everyone’s altruistic, not everyone’s nice, but I hate to say it. Look, you’re just going to have to deal with it. I’m sorry. , there’s no easy way to say it.
You’re going to have to learn to take the hits, take the rejections, because that’s the only way and take, take whatever comes your way. But at the end of the day, write down what your thoughts are on the career. And right, just keep applying. I know it’s tough out there. I know there’s a lot of rejection.
I’ve been through it myself. I can’t tell you when it’s going to happen. I can’t tell you how it happens or why it happens, why it doesn’t happen, but iterate through different projects, have a process in place, you’re not doing the same thing every single day. You have to have a Google sheet.
It’s like an Excel sheet, right? Write down where to apply, track that stuff. You want to be a data scientist and learn to track it and start building graphs and bar charts and pie charts off of that. See [00:32:00] how many companies are injecting you. Get the exact percentage so month over month what the average rate is.
Just be aware. You’re putting that out into the universe when you start doing something like that. You create awareness within yourself and it projects out. And don’t be afraid to talk to people in your own program. Talk to your professors. Ask them for help. Ask them for a cup of coffee. Because people just want to talk sometimes.
That’s how you make authentic, genuine connections. Hang out. Get a cup of coffee. Talk about work. Talk about what they do. And don’t be afraid to volunteer and do projects on the side, right? So if you’re interested in health care, for example find a research paper on the topic that interest you, for example, kidney disease or heart failure, find a researcher on that front from any university.
How many universities are in the world? I don’t even know. And reach out to them and see if maybe reading the paper, have they taken a machine learning approach to solving problem or building a predictive model and predicting heart rate, sorry, heart failure or kidney disease and stage kidney disease, right?
And if they have great, then you could just help with the next iteration of that in some way, shape or form, or if they haven’t even better, it’s your chance to sell yourself on that front. And so that’s just thinking outside the box. You can always reach out to me. I’m on LinkedIn. Just be passionate about what you do, and don’t think so much about when it’s going to happen.
As long as you’re doing something to stay relevant in that industry, you’re already doing it. And before you know it, you’ll be in it, and you won’t even realize that you’re already working as a data scientist, you’re already getting paid, and maybe you’re teaching data science, and doing a whole combination of all that.
But it’s the journey. It’s the process. It’s not. Yes. The results are important, but what matters is falling in love with the process.
Ryan Atkinson: And we’ll end it right there. Leon, thank you so, so much for joining us.
Leon Shpaner: Thank you, Ryan.[00:34:00]