Today, I switched it up a bit and took some anonymous questions from you all! If you want to ask more, use this link: https://ngl.link/kenjee_ds! I cover topics around data science, my lifestyle, and content. If you like this one, I will do another next week, then back to regular content!
Transcription:
Ken: What's your salary? Do you need to have experience as an athlete to be proficient at sports analytics? If you had to start your content journey over again, what would you focus on? Do you find your work in data to be more creative or more logical?
This episode of Ken's Nearest Neighbors is powered by Z by HP. HP's high compute, workstation-grade line of products and solutions. Today, I decided to try something a little bit different. I haven't done a Q&A in a while and thought it'd be really cool if this time you guys interview me instead of me bringing in a guest.
So, I took this idea from Mark Freeman, who has been using an app to essentially allow people to anonymously ask questions of him. I did the exact same thing, and we're gonna have a little bit of fun with that today. We're doing this definitely because I'm interested in the Q&A, and not because I've been busy and forgot to schedule interviews for this weekend next week.
So either way we're gonna have a really good time. I'll probably get to half the questions today in this part one. And then I'll do another part two where I answer a couple more, I've looked at a couple of the questions I'm gonna kind of do more of a raw react to most of them, but a lot of them have been really good and really interesting.
And we might get some, some spicy content here today. So, let's jump into it. Let's have a little bit of fun with these right. So the first question is, do you need to have experience as an athlete to be proficient at sports analytics? And I want to be clear on this one. So I do have experience as an athlete.
I played golf in college. I played professionally a little bit, but that is by no means at barrier to entry to the domain. A lot of people who are breaking into the sports analytics field are doing it through Kaggle competitions. And to be perfectly honest, a lot of those people, or some of those people have literally no exposure to sports.
So, if you do really want to break into that domain, I recommend doing the big data bowl for football, which is one of the biggest competitions on Kaggle. They've been hosting it almost every year. There's also a lot of other similar projects that you can do related to that. The biggest thing is project experience.
So if you want to break into sports, if you have projects that are already useful to teams, that'll be the lowest hanging fruit for you. Alright, let's check out the next question. If you had to start your content journey over again, what would you focus on? This is a really good question. I don't talk quite as much about the content journey as I do about the data science journey, but for me, my biggest thing right now is I'd probably have started the podcast sooner.
I love talking to people. That's one of my greatest passions, even probably more so than making videos and. If I had realized that there's a strong medium for that. If I had realized that that was a skill, I could cultivate more. And if I'd realized how valuable that was for me met networking and meeting more people, I probably would've pursued that a little bit further in the beginning than I did.
Just the more traditional YouTube content. Honestly in retrospect, I probably wouldn't do anything different. I really love how things turned out. I love the friends I've made. I love the things that I've produced. I loved the ability that I can create sort of side income streams and things like that as well.
So I don't think I would've done too many things differently, but for me personally, I would've focused. A little bit more on the conversational side and the spoken word side, because I do really, again, enjoy this medium quite a bit. If someone is looking to start content creation, there's a couple things that I would generally recommend.
So I think early on consistency is key and experimentation is the key. A lot of people think that you need to know exactly what you're gonna do before you start producing. And that's. Simply not how it works. You just have to put stuff out there, see what you like to produce, but also what other people resonate with and to continue to use those as sort of a barometer to figure out what direction to go, or maybe a compass is a little bit better of an analogy.
Alright. Let's knock out this third question. So it is to post some thirst traps, my king. So maybe in my next video, I'll have some, some footage of me working out or doing jujitsu or something like that. That could be fun. No, no true Instagram thirst traps from me these days, but I'm sure there, there are some, some suspect footage and some of my past videos, if someone really is looking for.
The next question is, do you find your work in data to be more creative or more logical? So I wouldn't say my work is an anomaly, but I would say that my work is very largely or at least half creative and half logical. So I think logic lays the foundation. So if I'm looking at a problem, what are the logical ways that we can come up to a solution.
And the creativity comes in with what are some other logical ways that are outside the box that we can come to the same thing right now, I'm driving a lot of my own project work. So I'm really focused on how do we answer the questions, not as much as doing the hands on work of answering the questions, which I am still doing, but the fun for me comes from thinking about how to solve problems in elegant ways, more so than to hands on actually solve the problems.
So I would say the creativity for me is higher than probably the average data scientist. But I think in anything you do in any step of that process, there's absolutely creativity involved. And that's what separates someone. Who's a good data scientist from someone who just is a data scientist or a practitioner, is they're constantly thinking of ways to improve the analysis that aren't just the traditional, Hey, let's you know, tune the parameters a bit more.
Let's do some feature engineering. You can get a lot more creative beyond that. So which language do you use in thinking and interacting with people in the office? Do you think working in native language helps things to be better understood. So I only speak English. I mean, you could say that I speak Python and Java or something along those lines.
I don't think I can really answer that question to the, to the grayed extent. I mean, I think a lot of people. if you can communicate well with your peers, is that that's a benefit to everyone including yourself and thinking more in the language that you're communicating in could be a very effective thing.
But again, this isn't something that I think I could truthfully honor, you know, answer to the best of my ability. I'd love to bring in a guest and figure something out and see what they. Alright. So the next question is, can I transition into data science with BCA, which is a bachelor's of computer applications degree?
What extra coursework do I need to do? And if not, I don't know what that means. So I don't know specifically what a bachelor of computer applications is. It seems something related to computer science, which I think is a really good starting place for learning data science. So in my opinion, to break into data science, you need a couple things from coursework.
There's other things from projects, but you need a foundational understanding of programming. I prefer Python, but R is generally fine. You also need some understanding of basic statistics. Linear algebra and calculus. So those are the groundwork for the data science, I guess, skill set. And from there, it's all about application and trying to build things and projects that give you the prerequisite data science experience.
Also of course, internships or volunteer work play a really strong role into that. So if your coursework covers those bases, I think you'll be fine. You can obviously supplement those bases. You can learn those things online. In my opinion, it doesn't really matter where you learn those. As long as it, you can showcase that you've learned those things through your project portfolio, and you can showcase that you understand how the work actually goes about within the domain.
Let's look at the next. So I've been studying machine learning for some time now. And while I did learn a ton, the sheer volume of stuff to learn in the field makes me feel like I've learned nothing, any advice on how to deal with such feelings as a newcomer in the field. So this is something I totally felt when I remember when I was learning, I was like, how can I possibly figure out all these things.
You know, you have the math side, you have, all of the programming side programming in and of itself is an infinite game. And within each language there's essentially infinite different libraries to learn or different ways that you can do things. And while that is really intimidating, it's also a very cool thing.
If you think about it, if you can have a strong programming skill, you can do quite a lot of things with it. The best way that I found to really narrow the scope is to just focus on specific projects. So rather than looking at the whole domain, your. Creating a finite project or a finite problem for yourself to solve when you do a project.
So the scope goes from this way, big thing to, Oh, I'm only working on this specific thing with these specific libraries and I'm trudging a very clear linear path towards that goal. So to me that helps you to eliminate a lot of the noise and focus on sort of doing this depth first search, rather than this breadth-first search, which can be really overwhelming.
So that would be my best thing is just focus on a very specific problem. If you're just beginning, you could do one of the basic datasets. For example, the Titanic dataset. I have a video tutorial walkthrough of how I would go about approaching that problem. And on Kaggle, there's all sorts of different projects where you can sort of get started.
You get some starter quote and you can feel it out and experiment with it. So I would, again, really focus on narrowing the scope. That's probably the most important thing that I would recommend. So, hi, Ken, who's your favorite subscriber? Thank you. I know this definitely. Isn't my friend Ibrahim. So I'm just gonna leave this one.
I will leave my favorite subscriber anonymous as well in the spirit of this podcast. So, hi, Ken, what is your relationship status, asking for a friend? I guess I don't really talk about this, that much. I've been dating my girlfriend for about two years.
Things are going really well. She hasn't shown up in my videos or anything along those lines because unfortunately I've seen how kind of rough the internet can for women. You know, I'm great friends with Tina. I read through her comments and I'm like pretty disgusted, honestly. So I'm trying to spare my girlfriend any of that and any issues we might face there.
I mean, obviously it's not like I'm in a super controversial content space or anything like that, or it's getting a ton of viewership and it would, you know, change her in my life. But you know, it's one of those things I think parts of our lives can be a little bit private, while the vast majority of my life is very public and I'm okay with that.
But it's not my choice to make for her to be more in the scene, at leaston YouTube and some of the content platforms. So did you think it's do I think it's important for celebrities to voice their opinions on social issues? This might be controversial, but, or maybe it won't be controversial.
I don't think it's important. I don't really care what celebrities think about social issues. I think Dave Chappelle, maybe not the best person to reference in this circumstance. He has this really funny bit about how, when things are going wrong, we're just waiting for Ja Rule to say what he feels about it.
And it's like, why do we really care what these celebrities think? Are they any more informed than we are? Are they any more important than we are in like the everyday life. I don't think that's the case. I mean, unfortunately celebrities do have very, you know, loud microphones. They reach a lot of people and when they're not informed, they generally cause a lot more harm than good.
So to me, it's almost, it'd almost be a better world if celebrities didn't, didn't share their, their political opinions or social opinions, but unfortunately that's the world we live in and it's just a shouting match. All parties and all social stances. I think it is important for people to be able to share their opinions and their thoughts on any social issues what's going on in the world.
What's going on in the economy. I mean, I'm a big first amendment believer, but do I think that again, these celebrities really have more information than I do as, as a general person? Absolutely not. And would I expect anyone to listen to me to talk about the social issues and the things that I care.
Absolutely not. So I will leave that to the people who are doing significantly more research on the, any issues at hand. Then for me to share things that that are, that are half bake I will talk happily about data science. I will talk happily about what's going on in my life, but things that are far more important and larger than me, I will try not to touch on too much on the internet.
So, where do you see the future of sports analytics going in 10 years time in 25 years time, I've touched on this a couple times in different podcasts. Probably never on my own. I think sports is gonna be significantly more data driven that maybe that's like a super thing, but we're gonna have access to very different types of data.
So right now we have performance data. So how do people actually execute on the pitch on the field, on the golf course in the stadium, wherever that might be, the next level of analytics is gonna get more focused in my opinion, on bioinformatic data. So what is their blood pressure? What is their heart rate?
What is their, you know, calculated strain on the cord on the field, wherever it is in real time. And that way we'll be able to better help injury prevention and a lot of these different things that we're getting diagnostics on. I also think that this could be a challenge with athletes themselves. So athletes because of the lobbies generally don't want this information.
Collected on them because they think it can be used against them. When they're renegotiating contracts, they don't look at it like as, Oh, this data can help me prevent injuries. This data can help me with this. This data can help me with that. They think it's not completely wrongly. They think it's there to help people make a case against them.
So, Oh, he is not working that hard. He's not pushing himself enough to really excel. So I think that's the big barrier that we're gonna face, especially in the short. I also think that sports just in general, there's gonna be a divide between the haves and the have nots. And the haves will be the ones that embrace analytics players and teams.
And those are gonna be the ones that really take off and do things more efficiently and frankly make a lot more, more money. Whereas the have nots, we're gonna see this. Kind of falling out at the bottom half of a lot of the leagues that aren't adopting the analytics and a lot of these more progressive systems.
So do you feel that there are areas in sports or adding too much technology or overanalyzing can destroy the purity of the game? So I think that there is 100% a point where you can overanalyze, so the like analysis paralysis and to be perfectly honest, that's what happened to me when I was playing golf at a high level.
There's this technology called TrackMan. And it allows you to track all of the ball spin data of the golf ball. So you can see like the spin rate, like the different side angles, you can see the angle of attack of your club and a lot of these different things. And I got so obsessed with that. I love the numbers.
I would try to just hit numbers and I totally forgot that I have to go out there and actually play 18 holes of golf. I was so focused on dialing in the numbers that the art of play completely alluded me. And I started to play really bad. And that's, you know, I took almost a year, two years off of golf because I was so frustrated with that, like, divide in my, in my mind between golf and playing, essentially playing TrackMan.
So these TrackMan also in baseball and a couple other sports to get the get the ball flight and spin information. Really cool technology. That is to say though, that there are some issues with that, right? I mean, to me, it, players, individual athletes can fall into this trap of where they just overanalyze everything and they don't improve.
They don't actually execute the skills that are relevant for the sports that they're playing. I think at a management level, there isn't such thing as over analysis, right? There's this sort of funnel that you see in sports. So you have all this data, you have this analysis, it gets delivered to the coaches, the coaches choose what they want to use.
And then what they want to use is distilled in very finite nuggets to the players. And I think that that's a very important thing, right? It's not the player's job to focus on all of the analytics, right? They're supposed to be honing their craft executing as well as possible. If they have good advisors, if they have good coaches, if they have a good analytics team, they're getting exactly the information that they need to excel.
And so I think a lot of organizations fall into this trap of like, what's the right amount of information we don't have buy-in across the organization. How do we digest this all? And that's the biggest problem. All these teams face is how do you get the right level of information to the players, the right level of information to the coaches and what do you withhold?
Because it's kind of white noise from everyone. If you're the analytics team in terms of art of the game, we're talking about basketball probably. And you're talking about the three point shot. I think that improving the like optimization of play makes the game more interesting because it allows it to transform.
You start to see different things, you wouldn't have a Steph Curry. If the three point shot wasn't valued how it is. And I think a lot of people would say, Steph Curry is one of the most exciting players in the game to watch obviously a very dominant player. And I don't know to me, I love it. Someone can definitely disagree with me on that front, but I think that that's a matter mostly of personal opinion.
Okay, let's do a couple more questions here. So do you feel an advanced degree is required to enter the field of data science? I've been pretty vocal about this. I don't believe an advanced degree is required. I've worked with many people who do not have advanced degrees. One of my really good friends who I'm doing my course with coming up, Jeff Lee, I mean, he's a senior data scientist at Spotify, a really big company.
He got jobs from, he got offers from quite a lot of FAANG companies. He does not have an advanced degree, right. Quite a few people who I've interviewed on my podcast, some, you know, got into at least data analysis without even having a college degree. What I do think is really important is first building out portfolio.
So you can showcase your skill set and second, building communication skills and thinking about this job process in a more intelligent way. The biggest thing for me is if you don't have a graduate degree, you probably have to try slightly different channel, right. So rather than just going and applying on Glassdoor, applying on LinkedIn, you might have to go directly to recruiters.
So you can say, Hey, this is what I've done. This is specifically relevant to this role in your company. And you might stand out more that way than if you were to apply again in one of those more traditional routes. So to me, applying in the traditional routes actually can work. If you have really good experience, right?
You will rise to the top. If you're ready, a senior data scientist, that's a great fit for the role. I mean, it's just how works, but if you don't have something that really separates you from the pack, the best way is to try alternative channels. So my, the next question is...
This episode is brought to you by Z by HP. HP's high compute workstation-grade line of products and solutions. Z is specifically made for high performance data science solutions. And I personally use the ZBook Studio and the Z4 Workstation. I really love that Z workstations can come standard with Linux and they can be configured with the data science software stack. With the software stack, you can get right to work doing data science on day 1 without the overhead of having to completely reconfigure your new machine.
Now back to our show. What's your salary? So this is an interesting one. I'm actually probably gonna make a video on this pretty soon, but I don't mind talking about it. The biggest thing for me is that I took a pretty aggressive pay cut from my last role to work at my current company, because it allots me a tremendous amount of freedom in my work.
As well as freedom to create content and produce another income stream. So I work realistically, probably 30 to 40 hours a week on my main job. I can work at whatever time I want. I can work in Hawaii, you know, when everyone else is on Eastern time. And that to me is really cool. So in that role I make between, I'll give you a range, you know, between like $115,000 and like $140,000 a year. And then outside of that with content, I do fairly, fairly similarly. So, you know, frankly, I'm very grateful for what I've been doing, the opportunities it's allotted me. And I also looked at it in that way as well. You know, a lot of people are really focused on a specific number when they're looking at salary.
I just talked in this podcast a little while ago with Breylor Grout, who is doing competitive jujitsu and something really important to him is time, right? Can he go train? What's the flexibility in that? And to me, it's the same thing. I wanna be able to live where I live. I wanna be able to travel where I can go, you know, wherever I want.
And I also wanna be able to produce content like this, that I can also monetize and have opportunities through. Honestly, my situation is perfect. I, you know, love how it's set up. I love both my job as well as the podcasting schedule. And that's the important thing is, you know, I had to take that pay cut a while ago where I wasn't even making what I am now, but I knew that this other stuff could grow into something really special. And I was willing to take that personal risk associated with that.
So thoughts on a bachelor's in data science programs like UC San Diego, they have a data science Institute or Berkeley. So I don't really look into specific schools. That would be a lot of effort on my end and I have no desire to do that. But on the other hand, I think we can evaluate if masters in data science, I mean, just general bachelors in data science programs are worth it.
So I think first they're not, there's nothing wrong with them. Schools are working very hard to create data science curriculum. And I think by now they've probably started to get a little better at it. My problem is very new data science programs, especially on the bachelor side. From when I looked at 'em earlier, a lot of schools were just throwing together resources from other departments and not doing a good job of keeping up with what's relevant.
So I saw a lot of schools have a stats class where they were using like SPSS, right. Or they were using tools that data scientists wouldn't use because that's what they had professors available to teach. And I think that that's not necessarily a great thing. You'd probably be better off doing a double major in computer science and statistics.
On the other hand, some schools who have done this for a while have gotten feedback and they've really cultivated a program and a culture around data science. I think it's totally fine. My best advice is always to look at the curriculum. If they're teaching Python or R, if they have, you know, some actual programming classes, as well as actual statistics, linear algebra, and calculus classes. I think it's probably, probably gonna be a a totally fine thing.
And again, it's less important about the degree you get. It's more important about the internships and the actual experience you get when you're in school. So I would put a massive premium on those. Let's do one more question and then we'll call it a day. So have you ever thought that you will stop being a YouTuber one day?
It seems like you're constantly progressing and trying to improve what you do. You know, in the future, I think it's totally possible. I personally love making video content, whether it is podcast or whether it is on the YouTube channel. I think I'll probably slow down. I've already slowed down my YouTube production a little bit.
I've wanted to focus more on making really high quality videos rather than trying to pump 'em out every week. And again, fortunately, you know, like my finances don't rely completely on YouTube or any of these other things. So I'm able to like pump the brakes or dedicate specific time to what I want.
This year, honestly, I've been do, I've been very busy with a bunch of different stuff, whether it's work, whether it. You know, the content and the podcast or the you know, some of the side projects that I'm working on, or the agency that I started for a lot of the other content creators, and I'd love next year to be able to really narrow things in and focus on creating more content again, because I feel like I haven't been able to improve as much as I would've liked to this year relative to previous years. So for me, it is this fun challenge of being able to explore and create. And I don't think the creative side of me or the exploratory side of me is ever gonna change.
Will it be expressed via the YouTube medium? Maybe, maybe not. I'm just gonna enjoy the process as I go and feel it out. So I think that's a really good way to end this episode a little bit shorter, a little bit sweeter, and I hope you got your question answered. I will be doing again a part two of this for next week as well.
And I'm looking forward to hearing from you next time. Until not until then. Good luck on your data science journey!