happyhourmay14.mp3

Speaker1: [00:00:09] Oh. What's up, everybody, welcome, welcome to the @TheArtistsOfDataScience Happy Hour. It is Friday, May 14th. Super excited to have all you guys here hopefully got a chance to check out the episode released today. It was an interview with the legendary Dennis Rodman. That was a really fun interview for me to record. I hope these guys get a chance to listen to it. And he's a really interesting guy. We barely talked about their science at all. We just talk about a bunch of random stuff, but it was really cool stuff and go and check that out. Excited to have anybody here. What's up? We got a lot of a lot of good friends and has got Joe with the epic background. He asked me to go on the podcast and check out Joe's background. Then what's up, Akshay, toward John Sebastian. What's up, man? I'm super excited to have all you guys here. What's up, man? How's everybody's week been? How's everybody been? Oh, congratulations to John Sebastian. He just landed a role. A new role. Yeah. Yeah, man. Joe, how are you doing, man? How are you? Good. Good. It's this was the first week like yesterday was the first day that I was able to walk outside with just a t shirt on because when it gets cold like that. So summertime is officially upon us and I'm excited about that. Taking all of next week to go chill at a cabin by the lake. I'm super, super excited for that. I won't be hosting office hours next week. They'll still be going on, but our host for officers will actually be Vivian movie. It's going to take over for me next week. I'm excited about that. Vivian, how are you doing?

Speaker2: [00:01:45] And it's good. It's been really busy. Had a lot of surprise things come up this week.

Speaker1: [00:01:50] Good surprise things or bad surprise things.

Speaker2: [00:01:53] Good surprise things. Yeah.

Speaker1: [00:01:55] Tell us about them.

Speaker2: [00:01:57] Oh just.

Speaker3: [00:01:58] I don't know. I don't want to know.

Speaker1: [00:02:02] No problem. And then good to see you again. Yeah. If anybody has questions man definitely go for it. Go ahead and let me know. You can feel free to shoot yourself or go ahead and type in the chat that you have a question and we'll be happy to, to help you out. I'm just gonna continue to stall until we get a question that that comes in. Then my man has as we we've been

Speaker4: [00:02:25] This has been a crazy week, I think crazy chaotic because going around might be in the water or something. Yeah.

Speaker1: [00:02:30] Tell me tell me what happened this week.

Speaker4: [00:02:32] A whole bunch of new priorities kind of showed up on Monday and then more showed up on Wednesday evening. And it was just that kind of week where surprised you got work because you got work.

Speaker1: [00:02:43] Yeah, I mean, that's a good thing, right? It's better than not doing anything to.

Speaker4: [00:02:47] Yeah, it was interesting. I had two different clients do one eighties this week getting ready for next year. They're doing some kind of aggressive, call it digital transformation, but some aggressive upgrades to what they're working with right now and some of the machine learning workflows, a couple of them are working on automating a ton of their marketing workflow. And it's just kind of one of those. All of a sudden they got people to buy in on it and they don't to. So it's all hands down.

Speaker1: [00:03:19] That's what's up, man. I'm interested in that marketing use case. That stuff is always super fascinating to me. But I do see that there's a question in the queue for Mark, Mark, my man, what is up? And while Mark is asking his question, if anybody would like to hop in the queue for a question, please send me a message in the chat and I will add you to the queue. Mark, go for it.

Speaker5: [00:03:40] Awesome. So I have this new transition in my career, which is really exciting and kind of give context. When I was early in my career, you know, I was given a task and they told me how to complete tasks as I can't go on my career. They give me give me a task. I just figure out how to do it. I'm now shifting to a point in my career where I like. Here's these business objectives and goals we have to figure out will be the most impactful way to make that happen. You need to execute, which is really awesome, like really fun kind of thing. But the challenge is and the previous kind of aspects of work, I just had to throw more work at it and I was happy I got things done now, throwing more work at the situation, just running in circles. And so, like, you know, there's this periodization piece. There's this like when do you know when to stop peace for the day? Because I'm working myself to the bone because I want to work on everything. And so I guess the key question is, once you get further in your career, you have more rope to hang yourself with. How do you best prioritize things you're working on? Also, when you have these long term projects now, they're not really task based, but more so like long term vision, execution based. How do you know when to stop for the day? Because I'm I'm having a hard time being able to say, like, OK, today was a good day, let me stop, because there's always something else to work on, so.

Speaker1: [00:05:08] Kind of relate to that it's not necessarily the same thing for me, like at work, like I don't have a breath of responsibilities. It sounds like you have at your start up. But just in personal life and everything that I do in general, there's a lot on my plate. I tend to just focus on things that I can only uniquely do and try to pick them off, as you know, focusing on whatever the highest impact thing that I can work on in the given moment is, and I'll probably spend a couple of hours on Sunday thinking about what that is for the coming week and plan the week out accordingly. For that, I mean, I've got some nice little to do list. Right. And just kind of just kind of two big things I need to get done right. That's been my approach, but I love to hear what what some other folks are doing. Let's start with let's start with Greg on this one and then let's hear from Mikiko after Greg Harp.

Speaker3: [00:06:08] And thanks more for a question. It's it's interesting. It's a there's I don't believe there's a right answer for this because it's going to vary based on your your ambition, your situation, whether you're on your own, you're by yourself. You don't have a family and things that what you want in life, what you value in life. At some point you're going to have to be selfish, because if you really value your your wellbeing, your wellness, you're going to have to find what matters the most to you, where you're going to have to say, no, I can't take on more responsibility. I mean, sometimes for myself. So, for example, between five and seven p.m., this is my time, etc. But other than that, I think you should definitely to Harpreet Sahota point, you have to be able to measure the or estimate the potential impact of everything you touch and kind of trim out the noise or the ones that are nice to do for people versus high impact ones. So for that, you're going to have to start focusing more on the bigger impact once don't get trapped by the idea of becoming the noid or do it all and you become somewhat the more clues actually useful versus the mark, who focuses on a few things that are super high impact for the business. And when more delivers, he moves on up the ladder very quickly because you will have the approval of people at the top that you did move the needle by focusing on these big projects. So you're going to have to learn to balance all of these, figure out you're going to have to stop helping people or fixing everything, even though they're nice to have they feel good. You feel valued because everybody's asking for your help, but a lot of it, too. You'll find out sooner or later that the impact that you thought you were going to have, it's not going to be a needle moving kind of activities. You're doing so kind of move them out, focus on the big ones and be a little selfish and go for this

Speaker1: [00:08:11] Here from a MIKIKO and just a heads up for the cue. I've got to wrestle the Mikiko then. Okay, then, Greg, if anybody else has questions, let me add you to the Q, but for now, let's hear from Mikiko on this topic. And if anybody else would like to contribute, please let me know by reasoning. Had no excuse after Mikiko will go to Joe.

Speaker2: [00:08:31] Yeah, I mean to piggyback on Greg's wonderful points of advice. Right. Not everything matters working in your job or company insurgency. And I think you I think part of it is figuring out sort of what are your personal goals for yourself and aligning your strategy to that. So, for example, if your goal is to move up the ladder one hundred percent, it's you basically cut out fifty percent of the work that you do, focus on the fifty percent that the leadership sort of cares about, and then you deliver on those things. And that's pretty much how it goes moving up and being promoted. A lot of times it's not about absolute value because especially in the tech world and the tech environment, a lot of things kind of get experimented, thrown out anyway. But it's about understanding, like what are the things that the people are in power care about and zeroing on those things. If your goals become like a really strong technical contributor, it's sort of similar, but not really in the sense that that's where you can kind of like focus your goal on, like, you know, what are the projects that are projects and buzzwords and sort of things that the company cares about industry. So like, for example, if you use a real example. Right. So when I was over at Autodesk for like, you know, they kind of has broken out into, like, product lines, but there were certain things that would sort of get the leadership's attention a little bit faster because it was for self relevant to the company. So it wasn't just like it wasn't a way out of what the company's portfolio of skills and experiences.

Speaker2: [00:10:06] And it was. But it was. Also, something that is very relevant to customers. So if your focus is more be like a strong technical contributor or to eventually, for example, become a principal engineer, you you still have that sort of kind of politicking that you need to be aware of. But it really becomes like what are sort of like the kind of the big projects that you can or the big sort of like buzzword projects or whatever that you can kind of focus in on and you kind of execute. And there is when you need to start figuring out how do you pull in like additional resources, like how do you start being a connector? How do you start thinking more about, like, the architecture of what you're building? Right. So it's a little bit different. If your goal is to go up the corporate ladder, then you really you just cut out like half like half the work that figure out sort of the the map or the landscape of who matters above you that will promote you up. And then you work on that and you also build the relationships there. If your goal is to be a tech contributor, it's a very different sort of difference. So I think that's as far as understanding what's kind of important to you. I think the second part, too, is just being aware of hussle culture and Data science machine learning. It's like super real. You can burn out so fast. Like I know a bunch of people here on the call are like really strong, like content producers and creators and all that.

Speaker2: [00:11:20] But at some point, I think especially with covid and like for me, I made the personal mandate to like I will only focus on myself and the things I want to do. If I do something that I don't want to do, it better be in service of something even greater. And like for me, my personal alignment is I want more flexibility and freedom. I don't want to play the games. I want extra money so that we can kind of do actually like non Data science machine learning stuff. So, for example, I really love designing clothes and sneakers. So I want to do I want to have a company that does that like custom design sneaks. Right. So totally different. But those are the things I value, flexibility and all that. But it's hard, especially if you're like a super driven person. You know, sometimes the best thing you can do is you just said those like you set those boundaries for yourself. For me, I had to do that by essentially scheduling like boxing classes at five sneaker heads. Yeah, I had to I had a schedule and classify YOLO, as we call it, yoga classes, classes or things like that. Schedule dinner with family at like six or seven on Fridays because I'm like for me it's kind of like if I don't set up those other strong things, you know, then I will just keep sort of expanding out. You know, that's just kind of like, yeah, that's that's how I try to do things and still fail miserably.

Speaker1: [00:12:41] Next up is is Joe Mark. And any comments or questions at this point based on.

Speaker5: [00:12:47] No, I mean, there's all been excellent stuff. I think a really something in my I will say no, I want I want to do everything because I love Data. I want to touch my hands on everything. And it's starting to burn me now mentally.

Speaker3: [00:13:01] Yeah, I've been there before. I still there sometimes too. I think there's been a lot of good advice here. The other thing I would say is you kind of mentioned that you've been given enough rope to hang yourself. And I've been in situations that's happened to time. Right. So the only solution I found is you've got to be you've got to focus on like the one to maybe three. Most things you need to get done for the day. And once you get those things done, maybe you get a few other things done that might be sort of on the list. But after that, it doesn't matter. And the other part is ruthlessly prioritize. So my schedule is every beginning of the week, actually, Friday I go through all that to do is I haven't gotten done reorganizes for the next week. Right. Then it's out of my so that we of you think about it come Monday, I focus on OK, what are like each day. What's the top thing I need to get done for that day. Right. What's the topic going to get up for Tuesday, Wednesday, Thursday, Friday.

Speaker3: [00:13:47] Maybe there's a couple of things, maybe those dependencies. But by doing that, then you have your secondary list of like the miscellaneous stuff and you might need to get done, but it's not a priority. And I think by just getting those kinds of wins each day where you just focus on if I can get this done, that's all that matters. That's going to save a lot of mental capacity for just taking care of. Because I you and I, we talk behind the scenes. I kind of know what's going on. And I can I know that you're going to you're doing a great job, which means you're going to get more work. That's kind of how that goes. So congratulations. You get to do more work now. So now it comes down, you're going to get paid money by making the most effective results. Right. And that means you've got to be ruthless about prioritizing. Is this is this action each one of these days going to move the needle if it's not a good skip?

Speaker5: [00:14:34] And and another big thing in the context I'm asking this question is that with these long term projects that aren't really task based, it's more complicated. The really think. And so I've I'm working on other things. I don't have the mental you say the mental capacity to actually think through these really complex projects. It's just not there.

Speaker3: [00:14:54] So what metric that is on Fridays I always had to say Friday for thinking day to Monday is actually admin day where you get rid of, like, all the shit that just going to get knocked out. All the tiny stuff. Friday is better thinking if you can help it, because you've got to have that space. You. Just borescope, thinking it doesn't it has never happened. You have to set aside the space to do it and so it's going to be talking you're kicking ass, which means people you're popular now. That's what you wanted. But this is the paradox. Yeah. Make sure you put that in your on your calendar, too. Right. So I give myself 30 minutes an hour here and there instead of my calendar during work time. And during that time, I want to make sure I do something that's different. It could be something outside of work, could be reading an article that inspires me so that time for herself inside of working hours. And if you have to move it, you can move it. You can be flexible with that, too. But to Joey's point, I think in time, you know, you can do it throughout the week. Also, for

Speaker1: [00:15:45] Those of you listening at home, work is the kind of guy that will schedule a relaxation day down to the half hour with yoga and meditation are scheduled in. It's funny, when I was when I saw you do that, like Liz Phosphine was talking about that in her book about just religiously scheduling our vacation days and trips like that. And they just reminded me of that. It was pretty funny to see. But I mean, to to echo Joe's point, you need that space, a busy calendar plus a busy mind that completely destroys your ability to do anything great to raise your hand is up. So we'll hear from tour that after tomorrow. We're going to jump right into the other questions and got questions from Russell, the Mikiko then then Greg, if you guys do have questions, please let me know in the chat and I'll add you to the queue. Go for.

Speaker3: [00:16:34] I just wanted to say about this calendar that we were talking about that, you know, scheduling our time is important. But keep in mind that if you look at most people's calendars, they're completely booked the first week and after that are completely empty. So when you manage your time, start booking people's time in the future because they will always be available. And that way you can kind of control, especially on the long term projects, you can schedule meetings like quarterly or bi monthly. And if you do it early enough, your time, this book, they're not going to start moving it around.

Speaker1: [00:17:05] They will actually take

Speaker3: [00:17:06] All the bookings around that. On the other hand, when you schedule your own time, when I always get my morning kind as my time, I get up at six from six to eight. That's my coffee time. Nobody disturbs me. But when I came to work, I would have from eight to nine every day. That's my take. Nobody else takes that time and that's my calendar. And like Joe said, you can always move it around, but it is what some people will then try and poke around your time. So it's really about managing your time and burning out. Believe me, it's not a good thing you don't want to go there. So learn to read the signals when you start getting tired or you start feeling that it's not that the energy isn't the same. Take a break because you definitely don't want to get that long. Guaranteed.

Speaker1: [00:17:49] Thank you very much to our market. A lot of great advice there. I really appreciated everyone's input. Let's go to the questions now with Russell first and then Mikiko Russell go for it.

Speaker3: [00:18:00] And everyone could see some familiar faces here. So the question is related to something that's been in the news a lot recently. So cryptocurrency with Elon Musk's appearance on SNL and I've seen some reports, coverage that cryptos been investigated for money laundering, et cetera. So with the likelihood that some people may be using, you know, scraping techniques and models to try and help them choose when and how to optimally invest in crypto, how could they cope with such extreme things happening? I mean, you know, big, strong, massive movements in the market, those types of things. I'm guessing that that's such an extreme event. You couldn't really build that into a model at all. So the people that have huge amounts of crypto are able to manipulate the market quite

Speaker1: [00:18:50] Easily, I would say. Read the insert series by Nassim Taleb NLP, the blueprint for how to handle events like this. But I mean, I'm not a big investor by any means, but just kind of thinking about it. Put the bulk of your money, maybe eighty, eighty five percent in secure, safe type of investments and then just be reckless with the other fifteen percent rate and have that be your limited downside. That's all I got to say. But I'd love to hear from other people on this. So let's see what Vince got to say on this topic. And then also, Tom, good to see you. I just saw Tom is in the building. Tom, how's it going then? Let's go to you.

Speaker4: [00:19:27] When it comes to crypto investing, if you look at it, if you just zoom back and maybe shrink the swings that hit crypto, they don't look that different than any other investment marketplace. The swings are definitely wilder. Well, when you look at what causes each one of the swings, the sentiment types that cause them really aren't that different. So how crazy will the swings be? That's really hard to look. You have to figure out some kind of multiplier, you know, and that's the only thing you jack up your risk significantly with the swings themselves looked like the swings that are driven by sentiment. And, you know, and then there's the. Full of other strange things like Hoddle, where Hoddle actually grounds prices and causes crypto rebounds, and it's just this like cult mentality is the wrong word because they're all making money and no one's killing themselves. So it's not really a cult, but it's that kind of mindset where people are willing to put their financial self interests aside and collectively hold on to enough crypto that a little bit of selling eventually leads to a rebound. So they have the same type of control of crypto that your large institutional investors have over your more traditional stocks, because those institutional investors, for the most part, have the same kind of cult like mindset. Will they'll hold a stock, you know, after a month of just getting trashed because they know long term they don't care. It's going up. They see the value prop. And so you'll see all of these similar trends that are in traditional investments in crypto.

Speaker4: [00:21:01] It's just a much wylder cycle. And there's this scary cliff in the future of the price can only go so high because we only have so much energy to put into this. You can't I mean, there is and people don't talk about this enough. There's a cap. You can't. The more the higher the price, the more energy it takes to do all of the work that goes into maintaining the crypto marketplaces. And so at some point, the price gets to a cap where we don't have enough energy to do it anymore. And there are entire industries that like outsource to provinces in China where the cheapest possible energy is available to do all of this mining. And, you know, it's this really interesting, natural bound, I guess, of resources that even in the virtual world applies in the same way that in our everyday market there are caps on resources. It's eventually going to have to have some kind of giant leap forward in order for the valuation to go above where they are. So, like I said, a lot of these different, you know, a lot of these normal traditional challenges exist in the cryptocurrency market. There are some technological challenges in there masquerading as supply and demand side forces. But outside of that, like I said, I just put a multiple on whatever you're willing to deal with as far as risk is concerned. And it's fundamentally the same model.

Speaker1: [00:22:22] Hdl, hang on for dear life. That's what you're talking about, the being there. Yeah. Um, let's see if anybody else has any takes on this with respect to that economic and energy cost. You got to have more efficient consensus algorithms that can create a great go for it.

Speaker3: [00:22:40] For me, I'm I'm confused and not the best of luck to you. Right. But I'm curious. Right. So I follow things that Elon Musk say, and I'm not too sure. The last time he put something out there about Bitcoin, for example. And to me, I'm like, is this borderline market manipulation? Because he's now saying Bitcoin, of course, is spending too much energy to mine. And he's you would more likely focus on the ones that are requiring less energy. So in a sense, you know, he's kind of like setting himself up or in his company for the underdogs. Right. So to leverage that as a I don't know, some sort of source or digital coin for his business is now the way I look at this is in an investment piece, if you know very little and I do what I'm thinking is to Harp his point of putting that 15 percent in something you don't fully understand is maybe look at the these underdogs that are now mining very little energy level at some point and also or very affordable. Maybe those are the ones that if you lose a thousand dollars, you don't feel bad about it.

Speaker3: [00:23:52] But in two years, if they get to two dollars or five dollars, you get your biggest bang for your buck. And as long as you understand the underlying technology of it, it's maybe something you can have venture into. But it's still to Vince Point, he's going to be a big swing. What I'm truly interested about in Crypto in Block, Jane, is kind of like the underlying technology to in high 60s, a lot of our issues like transactions, contracts between companies and supply chain when due to a vendor in a in a user consumer have a contract and there are multiple transactions happening where there is price of goods. And I promise you that I will send you a million pounds of something. Can we trace back that you, in fact, sitting a million and not ninety nine hundred and ninety nine point two of it in your pain, you're charging me for the full price. How do we reconcile all of these? So that's what I'm interested in, to understand the power of lockshin the underlying technology. But the whole cryptocurrency kind of confuses me. And I'm just curious about blockings.

Speaker1: [00:25:02] Awesome. I think the technology is great. I don't really get crypto that well. But the underlying technology blog is amazing. I know she has a related question to this, so let's go to this question, then we'll circle back to Mikiko then after Mikiko go to break. So go for

Speaker3: [00:25:18] It. Sure. So we were talking about Swing's and I think it goes along the strategy as well. So Dorje started off as a joke in February, but I read about somebody investing all of their life savings for 250 K, and now they are holding two million dollars for Dogecoin, but nobody knows when the bubble is going to burst. Same thing is when Tesla announced that they're not accepting Bitcoin payments anymore because it poses an environmental threat. So people are raising questions like what's going to happen to crypto? Is it going to keep going on hive? On the other end has an interesting strategy. So they're going portfolio is thirty six million dollars and are crypto portfolio was 15 million dollars at the end of twenty twenty and now it's four times at 60 million. So now that this source of liquidity has segregated for them, it helps them have a baseline value for their stock prices. So that benefits investors that are holding on to the high block gene stocks. And tomorrow, if they decide to expand more data centers or invest in other infrastructure, they have two sources of liquidity in terms of their own sales, which has a strong baseline value and the four times multiplied crypto value that they have gained over a period of time.

Speaker3: [00:26:34] So the question here is, do different strategies, along with the sentiments that are playing around, affect how a certain crypto value is going to scale from time to time? And obviously, the biggest concern here is how much threat does it bolster the environment in terms of the emissions? So there's going to be a race in terms of which cryptocurrency is adopting the best strategy. But there is a strong bias, such as what Elon Musk did on SNL. Like if somebody tweets and people take that as a negative sentiment that, oh, no, this is going to fall no matter what the strategy is, it's going to affect the sales for that crypto. So, I mean, it's interesting to see how it goes. It's it's like a bubble that's going to burst. But I feel like it's a smart investment that needs to be really talked, driven, and it has to be back with enough knowledge about the market, like why certain crypto companies are targeting social value and what's their strategy behind that and how it is a great example for that.

Speaker1: [00:27:34] Yeah, if anybody has any comments on that topic, definitely just raise your hand and meet yourself and let you know. I mean, I don't know enough about crypto to speak intelligently about it. So I'm going to just try to stall until somebody unmuted themselves and just start talking about

Speaker3: [00:27:49] Stuff real quick. Let's it really and I kind of like Elon. I don't know much about him, but when people throw out that kind of koka, it bugs me to think, OK, let's compare that to how much it costs to run the lights and the AC and the computers at all the banks in the world. Now, let's add up all the gas that's used to get the bankers and the people at work, the banks to and from the bank. And we could go on. And now how about all the armored trucks driving around, including the ER. So if you're really going to complain about mining costs and crypto, why don't you start with the costs just to run things the way they're being run now and compare it instead of just throwing out some one off statement like that. It's just it's pointless.

Speaker1: [00:28:35] I mean, like that that with what you're saying, it's like for solar panels, right? Like we think about how much money goes into producing solar panels versus the energy it saves, it's a lot more that goes into producing them than than the benefit you get. It's the same kind of concept, I guess, here. But if anybody has anything to say here, Mikiko, go for it.

Speaker2: [00:28:56] It's funny that you mentioned solar panels because that was one of my jobs, was literally doing supply chain and financial analysis for Sunrun, which is the biggest American residential solar company. But I think in general, there's going to be a reckoning about the environmental cost. Right, in tech like. So, for example, I mean, I've seen this pretty regularly, but like, for example, like articles or blog posts on like the environmental cost, like deep models. The paper that I forgot what her name was, she was pushed out of Google. She was the head of like I like ethics and research. And I think one of the specific papers she was writing was about the large scale language models. And one of the sort of issues that she had brought up was the environmental cost. Right. So I think there's just going to be this reckoning, to be honest. I think it's it's hard to say, like, you know, what's the real reason that, like, Elon, like, is pulling back on this kind of prior policies? Right. Because he can say that environmental costs. Right. But at the end of the day, like, we don't really I mean, I think we can all make estimates. But at the same time. Right, he has this massive influence, this massive status. So and I'm sure he's also aware of that, so there's a question of what's being said, what people are saying they're doing versus why they're doing it.

Speaker2: [00:30:15] But I think in general, right there is this reckoning in terms of the environmental cost of machine learning, and that's kind of spurring some really nice innovation in terms of like how do we compress models? There's a lot of good research going on there. How do we I think part of what what's kind of spurring autosomal right isn't just the like, oh, we can cut out all the like the human Data scientists, all the book and just kind of get something better. But also like this understanding that like, well, if you go with the free lunch theorem, right. This idea that you can kind of predetermined sort of what model you use and what architecture and all that, hypothetically, it's something that could have been done by the machine. I kind of wish of our resident Data robot guy was here because you could talk more about like kind of what they're doing out there. So I think there is just going to be this reckoning, right? I mean, you can't have you can't have every single person on the planet trying to do Birte or like try to replicate three and and not have it sort of not have it have issues.

Speaker1: [00:31:15] Thank you very much, Mikiko. If nobody else has anything to say on this topic, we'll just wait till Carlos gets here eventually if he ever does. But we can keep moving on for questions then just knows that Shantanu is in the building. What's up, Shantha? What's up, Jennifer? Jennifer, some good news to share with us, I hope. Let's first go to Mikiko question and then we'll go to Greg's question. And then if anybody else has a question, please let me know a..

Speaker3: [00:31:40] Q Cool.

Speaker2: [00:31:41] Yeah. Yeah. So basically in two weeks I'm starting a new opportunity. I'm very excited about it. I'll be working as a machine learning year over at MailChimp. Man, this was this was a journey on on the job search. My God. I think I can talk about if anyone has questions and and commiserations, I can, I can talk about later but yeah. So superexcited starting in like two weeks time for a week ago. So essentially what I'm looking for is advice that top pieces of advice people may have or even the top sort of gotchas to watch out for. If you are joining an engineering team for the first time in your career and just for context. Right. Like I have seven years of work experience, like within science analytics, but of the seven years that's actually never been was like a real engineering team. It's always been like with business analytics or is ops or data science. So I'm just trying to kind of figure out, you know, aside from getting familiarity with the fact that these DCPI and I only, like you said, you so try to get broad exposure to some of the tools that they're using. If you have had experience kind of moving to an engineering team for like the first time. What are some things that you would do to set yourself up for success? And what are the things that you would like not do

Speaker1: [00:33:07] A good chunk for this one? Because I think you might have some good experience with this.

Speaker2: [00:33:13] Hey, yeah,

Speaker3: [00:33:15] It's it's a bit weird to give Mikiko any advice because you want to interviewed before,

Speaker2: [00:33:22] But you're too smart for that role. Honestly. No, no, no, seriously. You're too smart. Yeah. I mean, you crushed it. So I'm really happy that you have a role where you can really leverage your strengths because that yeah. That was not a good team that you're interviewing for it. I'm just going to be honest. So I appreciate it. Thank you. Thank you very much. I would say so. I always say the same thing about about tools is like GCP best. I think it really just doesn't matter if you have used one, you can scale that out to the others. So I went from academia to working on an engineering team, the normal, and I was doing data science and academia. So there are shifts because of that until I had to learn all of these tools on the job, because and then I also the shift was from certain to industry. So we were using in-house tools for everything. Our cloud was our own and all of these things. So but that wasn't it was fine. It it takes a little bit of time, you know, I don't even think that long story there, but yeah. So the tools I have, like, it was just totally fine. And then I don't know if you have if they have what scrum or whatever in terms of the process. So that might be an adjustment if your previous teams didn't follow that sort of stuff. For me, I think that was the biggest one because I was like I was so used to like experimenting and just sort of coming up, just like having my time and figuring out the solution as I went to move into a more structured oh, if you don't get to this. This one point deadline just you've got to move on from it because you have other things on your board. So that was an adjustment for me,

Speaker1: [00:35:15] Make you something I don't know for some reason why as soon as I heard the geese like that Flock of Seagulls song, I started playing. I have no clue. I mean, because I sound like seagulls. But let's hear from Mark and then after Mark, let's go to a van and then we'll see who we go to after Van Gogh for.

Speaker5: [00:35:31] Yeah, in my role specifically, my role is to bridge the gap between the people, scientists and the engineering team for building products. And as you've seen, a lot of my questions are how how the hell do I talk to engineers? And my manager really gave me some great advice recently that really kind of I felt like it it to the next level. Was that by your knowledge, you're going to be you are you're going to be congrats anyway. And my engineer was the definitely engineer. But like you have like this Data like domain expertize. And so, like, I thought I had to, like, punch up in a way where I went to the engineer. So, like, I approach it like like, hey, I'm trying to be engineer too, in a way. And I was like, bad approach because they're like, if I speak to it as it was an engineering problem, I'm trying to meet them where they're at. They're going to be like, oh yeah, totally. Is engineering problem? Why are you working on this? I can think about this and they're excited and passionate about it. But if I come to them as like, hey, here's this Data problem solving with engineering, they're like, oh, that's really cool. That's your domain expertize. And they really appreciate that. So I don't know. That's going to be the culture everywhere, but that's in the culture I've learned here. And once I just shared that with me, it's like a light switch and it just it splits. It was less like, why are my projects keep on being taken or depart from engineering to like, oh, that's a cool Data project to collaborate. And so that just a small nugget. My manager gave me recently that was really helpful

Speaker1: [00:36:58] Thanks to him. And if anybody else wants to chime in on this topic, that just makes your hand. And I can call any. But it

Speaker3: [00:37:05] Found

Speaker4: [00:37:07] The best thing to figure out in the first few days is whether each one of the engineers is scared of you, intimidated and scared of you, or has no idea what you do, because it's one of those three. That's if you especially if you're the first one in and they've never had an engineer before or they haven't worked very closely with the Data science team. It's one of those three. They have no idea what you're there for and that can either scare them or scare and intimidate them. And so the faster you can educate like this is the role that I do and put that box around exactly what it is that you can do for them. And these are the things that I do. And here's my box. And here are all the ways when you start having a conversation from there on and here all the ways we can work together, we're like my box in your box, kind of sit on top of each other. And here's the value I add by doing the things that I can do. And here is what I deeply appreciate and respect that you do. And I've had so many cross-functional roles working with engineers and devs where it's just that approach, like, here's the value I can add. But at the same time, I respect the living hell out of the value that you're bringing in because I mean, you're obviously building something. It's valuable. You've got a job. So that's always been my approach to it is here's what I do.

Speaker4: [00:38:21] And I'm educating from day one, because it's you know, it's scary when you have somebody that comes in. And look, what I do is automate people's jobs sometimes. And if you're in an engineering team like that's in the back of your mind, is this is this person going to take my job? And when you become a collaborator, that goes away, a whole lot of the laptop measuring contests that happen also go away when you start talking about here's my box, but our boxes overlap with each other. And I respect what it is that you do. And if you find somebody who's a barrier in almost every dev team, I don't know what it is. But there's always one person who is a crossover of extraordinarily smart and contributes in amazing ways. But also, any time anything new happens, it takes him a good six months to say anything nice. If you can get some allies early on to talk to that person for you, it's so much easier and I do not know why every team has one, but that's the person that's going to make your life miserable. And finding small AIs early is going to. That's the easiest way to get a couple of guys on the team to start telling that, hey, no, it's cool. Don't worry about it. Here, let me help you with getting through barrier rock mindset and it'll make your life easier.

Speaker1: [00:39:36] Jennifer, go for it.

Speaker6: [00:39:37] Yeah, one thing I always advise people in any job you go in to go in and focus on delivering something right away that's going to show your value, even if it's just something small that you think you can do in a week. And they think it's going to take a month or you think it's a week and they think two days deliver it, whatever it is, deliver, because that's what they need most, is to know that they can rely on you and along the lines of collaboration, like you were saying. Then I suggest people don't brag about being the best at anything, even if you are. Don't say that. Don't go in with the man. I am absolutely the best at this. Show them deliver it and they'll believe you. That's going to build your credibility, especially with the tough nut to crack over the six months because, yeah, every developer team has that. It's kind of freaking awesome.

Speaker2: [00:40:37] Yeah, that. OK, cool. All that's really good advice. Yeah. I think it's funny because when I was interviewing the first all my family was very unhappy. They took this job because I had actually three offers and they want me to take it. They're like take those other jobs right there. Like don't you know? But I think this one, it'll be good because I'm joining I'm not the first. I'll be joining a team of at least five or six. Half of them are women. It's got a staff engineer who's got at least like ten plus years. It's another two senior managers and someone like me who's not like junior but associate, I guess would be the term. It's like below senior. So there'll be really so that they'll be like I'm looking forward to. And that's something that the other sort of like offers didn't have was they didn't have those kind of guardrails of like kind of more like more senior talent, because I really want to learn best practices. But at the same time, I get like really super nervous because there's that whole, like, imposter syndrome. Right, that kicks in where I'm like I'm like informally I'm informally taught. I haven't really worked on an engineering team. I'm used to sort of being like the solo hero type, understanding that I do work, but it's like super messy. So it's all this is good advice. Yeah, super appreciate, guys. I'm like I'm excited by most I incredibly so.

Speaker3: [00:42:02] Yeah. I'm hearing too what I'm hearing it too is regardless of where you go when you're doing it sounds like a technology. Learning new tools is the easiest part is really forging new relationships and managing these relationships, maintaining them. That's what you want to focus on. And if you're if you know, if I can do anything, my two cents is those nineteen ninety days. Discover people, you know, let people discover you to what you're here for and that you're not here to step on their toes as Ben was saying. And you know, the sooner you can spot what they value, what their goals are, because they are also trying to maybe move up the ladder. And if you're focusing on understanding what you will do, who will be affected by what you will do, but also who will be working with you to deliver the things that will impact others. If you make that circle and figure out everybody who touches, you know, what you will do, then you start interviewing them in forging these relationships. I think that's the most difficult one to do versus learning tool that that you haven't been around before.

Speaker1: [00:43:15] So I'll second that man. That is definitely the challenging part, especially for people like me who are kind of more on the quiet disposition side. I don't see much. And it's hard to to try to make those connections at work, but it is very, very important. People in the chat are talking about the book the first 90 days. I know Mikiko talks about that book. She recommended it to me when I started a job. And it is a damn good book. I highly recommend that set me up for quite a bit of success. Any other points or things to say on this topic from Mikiko? If anybody does, go ahead, raise your hand. But while I wait for people raise your hand, I will say that the next question goes to Greg. And if anybody has a question, please let me know in the chat will agree to the Q Mikiko. Hopefully that was some good tips for you. Congratulations on the new role. Everybody join me in congratulating me. Keigo. I think that is awesome, Greg. It's all you know.

Speaker3: [00:44:11] Yeah. My question is this. Should data scientist act with more logic than emotion is very vague and you can interpret it in different ways. But what I'm looking for is I feel like lately when I spend so much time with, know, software developers, data scientist, and I try to take it back to business folks, my goodness, they look at me like I'm like I'm nuts. And I take a step back and I go, oh, crap, I didn't really explain that the right way. So I need to put a little bit of empathy into or simplicity into what I'm talking about. So what is that balance between logic and emotion? That's for data scientists.

Speaker1: [00:44:55] If it's a logic in the sense that your first gut reaction is, oh, my God, that fucking suggestion is so stupid. Don't do that logically, it won't work, then I think calm down that part of the logic, but. I don't think it necessarily has to be a trade off. You could still be empathetic and logical at the same time while finding positive some situations for everybody. But I know that Mark was in very similar situations might still be. So let's hear from him.

Speaker5: [00:45:24] Go for it. I raised my hand really fast because I literally had this conversation with my colleagues on the Data science team where he kind of felt that that he was too empathetic that that or too emotional in a way. And I and are kind of like we have like bi weekly one, which is a catch fire. And I was like, yo, dude, that's your superpower. Like, you should lean into things like like one of the most empathetic message individuals I've ever met. And and like the work that he does, like, yes, you can do the hardcore stats by it when he goes into the executive room and shares those stories, that empathy, that emotion is what the business people are latching on to. And then later on, when they have a slide deck are like, oh yeah, the stats. Yeah, this makes sense. And I think that really, really I love about my colleague because his most recent project, you know, one of our biggest, biggest customers won't stop raving about the presentation. And I think that's a key contributor was the fact that he's able to tie in the emotion from both the analytics but build the story. And coming from my sociology background, this is this this term called fiqh description where you have the Data. But the thick description is all the context around it, all the stories, the qualitative data around that emotion. And that's the thing that really sells kind of hard core logic. And so I think I don't think a Data science needs to have both. I think, if anything, a Data science team needs to have both. You have those logical people. You have this very emotional connection people. And if someone has both, that's great. I think there's like a unicorn in a way. I feel like people lean one way or the other.

Speaker1: [00:47:13] Yeah, some great conversations here in the chat. Let's go to Russell first. Russell got some great tips here.

Speaker3: [00:47:21] Yes, I was just saying logical, I think is essential to make valuable decisions on the information is before you try not to let emotion divert the decision making, make that purely logical when you're delivering the message, the outputs of that decision, then by all means use emotion where it benefits. Certainly use empathy, trying to adjust the message that you delivered to the audience to be optimally understood by that audience. So don't use jargon if it's someone that doesn't understand the acronyms, etc. you optimize it to to to make it best understood under whatever circumstance you're delivering it. But as far as the decision making goes, I think that needs to be fully, logically. If you if you start letting emotion crowd into your logical decision making, you are far more likely to make mistakes.

Speaker1: [00:48:08] I definitely agree with that. We do want to kind of give space between an emotional reaction and pausing, thinking it through and then making a way forward. Greg, I'm still so many comments.

Speaker3: [00:48:22] Oh, good. Those are all good, good responses, so I'm liking it.

Speaker1: [00:48:27] I love to hear if anybody else has anything to say on this topic. There's a lot of good stuff in the charter school to actually get some insight here as well.

Speaker3: [00:48:36] Just to that degree. Explain. I think like logic definitely is important in delivering any kind of Data solution. But emotional insight comes into play when you're trying to communicate to the executives. So you're not going to explain the statistics to a CEO or they're not. They have no bandwidth to understand all the depths of that technical aspects of that project. But that's where empathy kind of plays a super power. So if you can convince them with a real example that they can relate with as how it would help their business, I would how would it help them reduce costs, save time or retain more customers like speak their language and empathy, basically put you in their shoes to communicate that? I think it is hand in hand. You need both. But like Mark said, like it's a team effort rather than an individual effort. So you need a high performing team that delivers with logic, but also has people that are empathetic to communicate the results. And I think together that combo will be beneficial for any team.

Speaker1: [00:49:39] Mikiko, go for it.

Speaker2: [00:49:41] Yeah, I guess the two things that kind of help me out, I personally tend to be, frankly, a very emotional person, which makes my choice of careers and hobbies somewhat ironic in a number of ways. But one resource that helped me was crucial conversations. And it's when I read that book, I didn't read the rest of it. But I feel like the first step was kind of a crucial insight where it was like speak from the heart. And I think. Oddly enough, I feel like sometimes when you're having, like services, so if you're doing a presentation, obviously the language has to be very professional and formal and there's never a time where it should ever be inappropriate or should always be appropriate. But I feel like sometimes when I've had meetings or conversations, especially about sort of like key projects or kind of like hot button issues, a lot of times it made sense to speak very plainly, saw the whole like speak from the heart, which is like, what do I want to really sort of get Elle's situation, not sell the person, but like both parties and kind of just kind of going straight to it. Right. So I thought that book was really helpful for me.

Speaker2: [00:50:46] Yeah. And then the second part, too, is also sometimes I think there's there's kind of a delivery mechanism, but also there's like the delivery stage. Right. So what I have observed is that a lot of all the executives or senior leadership that I work with that were sort of very effective, especially, for example, in sales and revenue operations, that tends to be a sales, marketing and operations. All that tends to be a very sort of political environment. I feel like even within a company in finance, I don't know, maybe everything's politics everywhere. But I felt like where the ones I saw who were really effective to have those conversations, Pavi, was, they they picked the timing and the place. So, for example, if they're presenting an analysis, the meeting is not the first time that the executives are actually seeing that analysis. And it's not the first time they're seeing the hot button points. They would have seen a lot those hot button points, especially like in our investor talks, they would have seen it like in a informal coffee chat or like a side meeting that was more like one on one. And essentially what that did was kind of person that helped get you the present to help you.

Speaker2: [00:51:50] It helped you get the feedback early on as to what the hot button issues like where are you going to bring up at that meeting? Or they're like, well, I disagree at this point. Well, what do you mean by this? This you kind of get that serve earlier on so you can kind of like address it either directly or like in the appendix section. But it also sort of gave the other party a heads up, like, look, this is kind of like coming down like into the pipeline. I feel like a lot of times we have this natural tendency to like if something is going to cause, like sort of issues or politics, we kind of like want to just keep it to like as far away as possible and keep it so that, like, one presentation meeting with the ones I saw who are very successful, even dealing with like multiple hard personalities, that's kind of how they handled it was especially true when you're in the boardroom environment and there's like multiple people, a lot of times things can impact a lot more painfully than they would have if you were literally having the same conversation with the person, like on a coffee chat.

Speaker1: [00:52:46] Yeah, let's hear from shotted on this. But also one thing that's like to I don't know if I'm losing footing of the question here, but you just when you're talking to people, just have the assumption that this person did not intend to come to work today to fuck up like this. This guy didn't wake up this morning to say, you know what, I'm gonna do a horrible fucking job at work today. That's that's my M.O. And you got to realize that the other person on the other side of the table isn't thinking about you either. I don't know if that fits in at all, but it's fun to make that point. But shabbiness go to you. Yeah, I

Speaker2: [00:53:19] Mean, I love that we're talking about empathy and teched fulness, but I also want to want to draw attention to something else. Statistics is not actually a super hard science there. I mean, there might be folks here that are more hardcore and statistics than myself. But in my experience and studying, studying math and physics, it's there are lots of things that are left up to your decision, up to your choice. There are even like what kind of statistics you're going to do. Right. What kind of statistics is appropriate for a given type of data set or size of data set? And while it's true that like let's say I'm going to I need to make a decision based on data, and while it's true that I probably want to do some sort of confidence testing or hypothesis testing. Right. And there are set set knobs with the Data size, the the level of significance I want to reach and in the power of the test and so on and so forth. Those are like harder stuff. But there is still so much that is just so subjective to data. Scientists can work with the same data set, trying to answer the same question and make the same decision and come up with different different answers and different recommendations. So that's where sort of I see the and I don't really see it as a logic versus emotion, because logic is logic is not that great. Like logic is you convincing yourself or you convincing others through arguments that follow from one another. But if you start from your premise is wrong. If you start from a wrong place, then you can definitely go the wrong way with logic. So I just I think that it's it's more. Judgment and use what the right balance of of the two. There are other dimensions as well for the right situation then.

Speaker1: [00:55:16] Thank you, Charles. And by the way, that's a great response.

Speaker4: [00:55:18] Now, this one's kind of taking it a little different direction. But you have to understand that there's a lot of people that are like me that don't have natural empathy. I actually have. And there's a lot of us that inhabit the Data science side of the world and also the sort of senior leadership side of the world where empathy is not like a natural thing that comes to us, you know, and and so that's and when I open up about this, I get a whole bunch of nodding heads and it's like, oh, I'm not the know. And I started realizing about six or seven years ago, I was like, oh, there's a lot of us. So when you talk about you, you do bring in empathy to sort of address the 70 percent of the room. You have to realize there's 30 percent of us in that room. They're fake. And we have no idea how we should be responding emotionally to the majority of situations. And so, you know, kind of realize that that's a lot, especially in Data science, because we all we seem like we like leadership, you know, and we also like the hard core Data engineering science side of this. So there seems to be a lot more of us in these fields than most.

Speaker4: [00:56:21] And so when you're trying to speak for persuasion, when you're trying to sort of speak to mixed audiences, always think about there are some of us who are in that room. And the emotional portion of that doesn't make any sense to us. Like we don't get it. And sometimes you see an extraordinary reaction, like a reaction that doesn't make any sense from someone. You're thinking, oh, I was too emotional. I shouldn't have. But no, it really is. That person just had no idea what to say. They did not know what to say. And these are the absolute wrong thing. And they are now going home and spending the next six or seven hours going over in their mind exactly how wrong what they said to you was and how they are going to the next day either apologize to you or somehow cover it up. You have to understand that the you know, the emotional side of this works, the majority of the work, the way you think it will most times, and then it'll run into somebody like me who's just really faking it. And on the flip side, when I work with people who make decisions emotionally because I find that a lot of people who are really smart make their best decisions when they use just to show that emotion.

Speaker4: [00:57:27] When they allow that intangible piece to come into the decision process, they make a lot of good decisions. But I don't like I don't pick up those cues. And so I don't I don't have the same information to gather and to make the decision based off of. But they do really well because they're picking up that small piece of emotion. And so when you when I do a presentation, I'm thinking about how can I lead, how can I make it so that everyone leaves happy about the decision that they made today or happy about the results or how they're thinking about my presentation. How can I make everybody comfortable with the way they're feeling when they leave? And that's something that I've intentionally had to put into my presentations, because long ago they used to be called kind of cold and robotic. I didn't read the room well. I didn't connect very well with, you know, and so I had to do some intentional things that I think everyone wants to live happy about the way that they were thinking and what they decided. And that's always been the best emotion for me to inject into really heavy Data presentations is to leave everybody kind of happy then.

Speaker1: [00:58:27] Thank you very much. I six or seven hours after and I think giving people too much time and people are not thinking about you, they're thinking mostly about themselves. But that was a great response. As anybody else have anything else to say on this topic while we're on it? We'll open it up for any questions as well. Any last minute questions? Tom, I'd love to hear from you on this, Tom. Actually, that's one voice has been that's a missing today.

Speaker3: [00:58:56] Well, I love all of you guys. And I'm gonna be honest, I am so afraid right now I should be working on my book, but I'm like, it's not happening home to Frid right now. And I'm actually kind of jealous because a. who I deeply respect thank Mikiko for her answer. And I kind of wish I didn't listen to her carefully enough. And I usually Mikiko groupie, when she starts talking, I'm like, I got to hear this. So Harp brother, just so I can engage the last little ounce, my brain I have left this afternoon. Could you repeat the question for me? And I will try. I promise. Greg's laughing because he knows why I'm tired. We this is kind of when Greg and I put together a presentation and we've given that between the two of us. We've given it three times now. And it was a big win. It was funny because it was for a conference that I came up with the name for and then I didn't get asked to speak at the keynote speaker dropped out and Beverly asked me to in our group and she asked me to jump in. It went really good, but I'm so afraid because I got up early.

Speaker1: [01:00:03] And go ahead and

Speaker3: [01:00:04] Tell me, Greg, tell me the question. This is related to the things that we talk about. I would say and should it Data scientist create balance between logic and emotion, should data scientists have more of one than the other? Now, I remember quite well because when you first answer the question, I remember thinking that overwhelms me, just thinking about it. I'm not sure I have anything. But one thought that did come to my mind. And I'm really interested if anyone kind of feels the same way. It's this I would always lead with logic, but like Santana said, it might send your name right. Santoya Chandana Shantanu. Thank you. I liked what she was saying. We as humans, we misuse logic a lot. It's because we start from a wrong premise. But if you start from Data, if you start from facts, I think it's OK to add emotion on top of facts. But I'll confess too often when I get emotional. I have failed in emotional intelligence and I think I've grown an emotional intelligence more in the last five years than in my whole life. But it's something I still have to work on a lot. And I think this really comes down to the area of emotional intelligence. And thankfully, there's been some outstanding books written on this. And I think it comes back around to Mikiko. I think it was you that said a crucial conversations. It's written by a group called Vital Smarts. I love these guys. They've written four books and one of them is influencers. And Greg, I think this comes full circle around to your point. If you read influencers and you apply emotion appropriately in the framework that they're laying out, then emotion or empathy or how we want to put it becomes very powerful because it's then it's well targeted.

Speaker1: [01:01:55] Thank you very much. Before Jennifer leaves, Jennifer to want to share some good news with us. Is she still here? Yes. Jennifer, share some good news. Go for

Speaker6: [01:02:03] It. I got a new job recently. I was dancing literally like you did, Mikiko. So I'm moving into Intel's corporate strategy office. I am super excited because I've been in engineering for about fifteen years and we have a lot of questions in this group about where to go next and career and how do you know you guys. So I come here because I love Data. You are my people. I love you've got the skills in the foundation that you have are a part of every business at every level. You can take it and experiment with it in a lot of different places. Strategy Office, there's Data group in there. They're hiring specifically for that right now. Wherever you want to go, find something fun and go chase after it, because you guys have the skills and the abilities to impact every organization in a business, every type of business that there is. It's a really unique proposition that you have. So I wanted to encourage you guys and then I got to go because I'm late for a meeting. But thanks have. Thank you, guys. Have a great

Speaker1: [01:03:19] Weekend. Jennifer, thank you so much. Appreciate having me here. Everybody join me. And Jennifer, I got guys question here will make this the last question. This is coming from Marc and it's a question about Java. I'm looking at this live in because when I was on my podcast, I asked him, Python or this is back when I didn't know how to ask questions on podcast episodes. And he said neither Java. So this will go to him.

Speaker5: [01:03:44] And that episode is exactly why I'm asking this. So my friend recently reached out to me like I want to learn how to code. He's super passionate about Minecraft and Minecraft built off Java. I was like, awesome. I heard Ben talk about Java and therefore I need to learn it myself. That is an awesome opportunity to hang out with my friends, learn Java and it's going to be really fine. I have a few ideas. I've learned how to code and SQL are in Python, so I've have some approaches to learn how to code. But I'm curious because Java is very focused on object oriented programing and it's just a different way of coding a little bit than than Python. And so I guess like at a high level, what would be your game plan to, one, learn to also teach it to someone else who's new to code? And and I'm teaching it because because he's completely new to coding and also because it helped me learn faster.

Speaker4: [01:04:39] You got to. Wow. How did I learn Java? It was like trial and error. I'll be completely honest. Java was trial and error and a whole lot of really smart people beating up my code. That's how I learned it is a whole bunch of code reviews, but that's how I learned, like each one of the versions of Java and all of the upgrades and all the new functionality I learned because somebody else around me. John is one of those weird languages where I didn't I wasn't actively going out and learning like latest greatest, there is always somebody on the team that was 40 times smarter than me and a hundred times better at software engineering best practices. And they would bring it into the team and say, hey, so here's this new cool thing like lambdas somebody just brought in lambdas is like, dude, you're writing this, it's this. And that's how I learned Java was this incremental sort of learning thing. So I would say honestly, teach it the same way, start with something dumb, simple, and then add functionality. Have like a roadmap for a project where you're actually building functionality that's going to take increasingly complex work to do and have milestones where you're going to have to do redesigns where you actually have to go back and re architect. The solution, because that's where I learned a ton from too, is you build it, everybody builds like the first thing badly anyway. So why not just nod your head and build that first project as first practices? Here we go.

Speaker4: [01:06:10] And then that second project that you do make it one where you got to re architect. Look where all the stuff you did wrong, all the stuff you forgot to document and all that, like in the real world, comes back to bite you. And now you have to rebuild the whole thing, plus new functionality and you have to architect it. This time you actually have to use your patterns and practices. You have to use software engineering and you've got to figure out like what is object oriented for real, not like memorizing it, like you're answering an interview question. But why do we do any of the object oriented stuff? Why is it important, you know, and like don't beat people up over interfaces and stuff like that because those are kind of like, come on, you can do it five ways. I get it. There's people that love this and hate that, you know, so don't go down those rabbit holes of like Stanning different types of almost. It's almost like there's a ton of different ways you can do tab versus space. And if you kind of get stuck in that sort of rigidity, it makes it you lose track of the more important best practices. So that would be like my pitfall is, as you're architecting like don't re architect with that sort of rigid mentality, that there's only one way of building in Java because there's eighteen. And whatever you do, don't forget that after I think it's Java eight, you got to pay for things.

Speaker4: [01:07:26] So teach that which had got expensive after a particular version. So, you know, there are other considerations in Java that can teach you a lot about software engineering at an enterprise level. And so don't miss all of those lessons, too. Don't just make this about learning syntax and learning how to slap classes together and that sort of thing. Don't make it all about encapsulation or any of that. You don't make it about building because Java is its enterprise for a reason, like it was built this way. And people hate it and call it ugly. But it was built this way because companies build stuff this way, like every thing that everyone hates, except for no pointers. I don't know why those are there, but every other thing that everyone hates about Java is there for like an enterprise reason. And it's got a lot of the same ecosystem that Python does. So there's a lot of those components that you can also kind of pull in from Python. We can say this, Honan's like this or definitely teach how to actually build a project and ship it like in a deployable way. Don't ignore that, because I didn't learn that until like five or six years after I'd first started coding in Java. So don't forget to teach that early on. It was like embarrassing for me that I couldn't deploy a project on my own. And so that's like my long winded answer. I'm sorry I took a lot of time there.

Speaker5: [01:08:42] That was that was great. And just a quick follow up question to that is like for that starter project we're thinking about, Prussians type got opensource kind of project to add to, kind of like an easy first ticket. Would that be a good approach or should we try to do our own kind of project and then do that stand out? Or it's kind of like pick your poison, just do do something. I feel like the latter.

Speaker4: [01:09:01] But if you're doing like an open source, make sure you're a software engineer. Best practices are in place, your codes clean. If you don't write clean code, don't submit to an open source project early, because that can be depending upon how understanding the people who are doing the reviews are that can that can hurt a little bit and mess up your pride. So I would say if you are if you write some clean code and that's a great, great place to start if you're if your codes not clean at first. Do you like a toy project? Don't dove straight into open source because that can get like I said, most projects are pretty open and they're kind of understanding. But there are several open source projects out there, especially in the more hardcore programing languages like Java and see where and syntax gets you murdered. You can get Murcutt in the comments

Speaker1: [01:09:50] Shop and I saw some good tips from you in the chat. Would you like to share some of those?

Speaker2: [01:09:55] I was mostly disagreeing with Vince Point about standing up the framework or the bare bones version and then adding functionality to it and also iterating on it and making it cleaner and. And that's just, you know, just a good, good way to

Speaker3: [01:10:10] Live your life,

Speaker1: [01:10:12] Right. Thank you so much. There you go, man. That's how you learn Java. Any body else here do Java that wants to help mark out. If not, does not look like there are any more questions. So. Oh, Tom, one recommendation.

Speaker2: [01:10:29] Yeah. Readhead first Java. That is a great book. It's fantastic. I would yeah I would read that book and maybe grab one of those udemy like toy project classes, one where they're like oh yeah. Build these 12 apps with like oh my God. But try to find one for, for Java.

Speaker3: [01:10:46] No I, I actually will likely for people for getting involved in the R vs. Python debate. I don't agree with it. But Mark, I just can't help myself right now. Why Java. What is wrong with you. And it's funny I shouldn't say it, but I'm actually being honest. I'm feeling it. Why Java?

Speaker5: [01:11:08] Why blame then? Because I heard on the podcast and that just stuck in my head. He felt so passionate about it. And then to Minecraft is is is built off Java and my friend's passion about Minecraft and like my selfish kind of goals, get my friend to coding. And that's like a perfect project. I'm hooked on

Speaker2: [01:11:28] C++ or Acceptance and Iot. Come on, man. Come on, let's go. Robots. I cannot agree with C++ being a starter language. It was for me and no.

Speaker1: [01:11:40] So it was in addition to it then. It was also Andrius Kretz that responded shava as well. But I need to learn something. Right. Might be might be fun. Might be interesting. I made my pick that up this summer as well. Why not at least get familiar with it? I was doing some nogs earlier this year, the first month of the first couple months of the year just to play around with. It seemed pretty interesting

Speaker2: [01:12:05] Because C++ is as much of a star language as Java. So you're going to start with help. Start with the you know, the the debauchery side of El @TheArtistsOfDataScience.

Speaker1: [01:12:17] I'm no, I cool. Well, guys, thank you so much for coming in and out today. Check out the interview I released on the podcast with Dennis Rodman, the one and only enigmatic Dennis Rodman. It's a really interesting conversation. We talked about a bunch of interesting things. So go check that out. Make sure you guys all tune in to the podcast world, rather, at the happy hour next Friday, because Vivian is taking over for me. It's my wife Romy's birthday next Friday, so we'll be hanging out. Romi, happy birthday to you. I'm looking forward to just being on the lake in a beautiful cabin and just chilling out next week, not even bringing many books with me, like last time we went out to the lake. I like literally seven books with me for a three day trip. I don't know what the fuck I was thinking this time. Just one one book, because I have to interview this guy, Jordan Ellenberg Shape, and I'll probably bring a fiction book, but mostly just notebooks to write. I'm going to you see my aphorism game strong on LinkedIn I come back, I would just be thrown fucking aphorism that you guys you can be like, damn, this guy's insightful or crazy on the other.

Speaker3: [01:13:26] Tom Oh. I was just going to throw out an encouragement. I'm going to try extra hard to be here next week to support Vivian's first podcast

Speaker2: [01:13:34] Mean everybody show up, bring questions. I'm so nervous that it's just going to be like me sitting there staring at myself, being like, well,

Speaker3: [01:13:43] Yeah, Vivian, I'm worried for you because you don't really know how to ask this group challenging questions. So you probably won't know if

Speaker2: [01:13:50] Nobody shows up. I'm just going to be asking myself like, hi,

Speaker6: [01:13:53] Vivian, how are you today? Oh, I'm good, thank you.

Speaker1: [01:13:56] I guarantee you more people to shop for you than they will for me. It'll be awesome, by the way, you know, hanging around afterwards. And we send you a link. Let's talk right after this for a few minutes, OK? But yeah, man. Yes. Thank you so much for hanging out. Um, looking forward to seeing you two weeks from today. And be sure to come hang out with Vivian next week

Speaker4: [01:14:17] Before you drop off. I don't have the zoom link for next week. I'm looking at looking at it and it doesn't look like it's scheduled.

Speaker1: [01:14:23] It said I might have left it off. So for everybody listening, I might have just deleted it just in case from the thing. But it's the same exact link that you use this week. It's always the same link, even though I tell you guys it's a personalized link, but it's not. It's the same like all of you guys have the exact same link to to get here. It's just one link, but it's still next. We just click on the same link that you used to get here. But yeah. Cool. Guys, thank you so much. Take care. Remember, you've got one life on this planet. Why not try to do something big? Cheers, everybody.