happyhours-may28.mp3

Speaker1: [00:00:09] What's up, everybody, welcome, welcome to the artists of Data Science, Happy Hour. It's Friday, May twenty eight. Super excited to have all of you guys here today. Today is brought to you by Data Science Dream Job, the platform where I am principal mentor. If you guys are interested, check out this Jayco forgood free Dasch training to check it out. Hopefully you guys got an opportunity to tune into the podcast. Had a really interesting episode. Will all my episodes I feel like very interesting but this one was really cool. I talked to Dave Gray, author of several best selling books, including Liminal Thinking. I thought it an interesting conversations about belief systems and trying on new beliefs, and I really enjoyed talking to him. I feel like the conversation got off to a rough start, but we started to warm up to each other and I ended up in a really good episode after that. Speaking about belief systems, let's talk real quick. What was one thing that you believed you had to do or had to know as a data scientist when you first started your career? Looking back now, you're like, oh, man, that belief is totally incorrect. I'd love to hear what that is. I mean, for me, I think the one belief that I had when I first started out that is now completely wrong is I had this belief that I that I had to have an answer for everything. Um, I believe that my job as a data scientist was to know everything about data science under the sun. And I had to have an answer for everything, even if the answer was wrong. I just needed to say something and have something come out of my mouth so I didn't have to look like I didn't know what I was talking about. That has since changed because I've learned the power of simply saying, I don't know. Um, so I love to hear what that one belief you had at the beginning of your career that you now look back and say, do that was a completely incorrect belief. Let's start off with my friend, Christian Christian.

Speaker2: [00:02:15] Go for it. That's funny. I was just thinking about that. Your your stories are relatable to a lot of people. Beginning of my career, I was definitely more like Data, not Data focused Data was later for all business. Right. And so you're sitting in a room with people who have been doing it a lot longer than you make a lot more money than you. And they look at you and ask a question you don't feel like you should say, I don't know. But what you find out is it's worse to make something up. So I don't know less. Well, early on, obviously, with regards to Data, it's like when I first started really messing with it, I had this idea that there's always an answer there and I just have to look hard enough to find it. But sometimes that's just not the case. There's nothing to expect from certain data sets. And it's just it's either not enough or there's no message there for what you're looking for. And again, it's just another way of saying sometimes you got to say, I don't know what the answer is, not here.

Speaker1: [00:02:57] Yes, that's an interesting point. Right, because we're so used to having Data. So it's kind of handed to us when we are learning the contrived data sets that have some bit of predictive power in them. But we go into the real world. We believe that there should be some type of predictive power in every data set that we have. And sometimes that turns out that that is not the case. I'd love to hear from Mikiko on this. Mikiko, I don't know if you got the gist of what the question is all about, but we're talking about incorrect beliefs. We held at the beginning of our career that, upon reflection, we now know are not correct beliefs. And in the meantime, if anybody has questions, all are kind of getting warmed up to go ahead and put your question right there into the chat and I will put you into the queue. And also, I do need to give a shout out to Vivian for doing such an amazing job of hosting the happy hour for me. Last week. I had a opportunity to listen back on that and I'm not going to let those hands down. My favorite happy hour makes me wonder if I should be here at all anymore. I should let you guys take over more often for me. But yeah, let's let's go to Mexico. And I've been a un-American for so long that I forgot that it is Memorial Day weekend. I was too busy celebrating the queen last week. But yeah. Let's go to Mikiko.

Speaker3: [00:04:15] Oh, that's right. You're in Canada territories

Speaker2: [00:04:20] With your very

Speaker1: [00:04:21] American background. I love that.

Speaker3: [00:04:23] I know, right? Yeah. You know, I honestly, I'm not traveling. We got double vaccinated, but yeah, we're not where we're going for the tier one, you know, friends and family. Right. And then we'll expand out to tier two, which is, you know, adjacent friends and family. And then tier three, which is coworkers, although I'm not going to see my call because they're in Atlanta. Someone see them for a little while. But yeah, I think the yeah, I think the two probably the two, the two beliefs were like, OK, three. But the first one was that Crenshaw's matter. That's something that, you know, even nowadays I kind of struggle with sort of that imposter syndrome and like machine learning distance science, just because so many people are kind of like pushing their degrees and like, you know, education cloud. And there you see Lifestream. About that, and now I actually feel a lot better, I think part of it is because I think the science machine learning is a little bit closer to academia, but now that I'm sort of mingling with a lot more like engineers and developers, I see that like for them it's you know, they're like if you if you have like like a bachelor's in computer science, but you can code really well. People are kind of good with that. So it's definitely like a total culture mentality shift. But I think in general, it sounds going in today's science machine learning, they should worry a lot less about the credentials and whether or not they could do the work.

Speaker3: [00:05:39] The second one was I was all it's all about technology and less about soft skills. And I'm going to be really honest, like, you know, now that I'm on like an actual engineering team and an actual engineering where there's like a senior machinery engineer, there's a staff machine ranger, there's principle. And, you know, I'm seeing that, you know, the reality is that, like, I talked my way into most jobs, so I'm just going to be real. I couldn't pass code to save my life or to pay myself, you know, so soft skills really matter, especially when you're dealing with a lot of the like the architecture side of Mellops. Like even within some companies, they're using sort of different stacks or pipelines. And you really have to speak well with people. You have to understand the complexity and you have to, you know, even like now getting all the different channels, you just got to know, like who to ask questions and you got to be comfortable asking questions. So soft skills, I think, matter a lot more at this point. I think the third major one was should I? Well, yeah, no, I think actually it was just those first two is credentials and, you know, sort of not valuing the soft skills part. I think those were the two big ones for sure.

Speaker1: [00:06:51] Shout out to some friends in the audience. What's up to Greg? What's up to Albert Bellamy? Albert is a service member considered veteran of much Albert. But my friend, thank you for your service. Oh, shit. We got the one and only Benjamín. The Seattle Data guy is in the building today. How freaking awesome is that? And what is up, my friend, if you'd like to take the floor on this bed, we're warming up with the question here. And the topic of the question is, what's one belief or a couple of beliefs that you held at the beginning of your career that looking back now, reflecting on that was just a just an incorrect belief to to have. So if you'd like to take over, then go for it

Speaker2: [00:07:34] And think about like I mean, I think the biggest focus and maybe it's a belief, but like my like my personal biggest thing was like setting up like a design with my goal consulting company only to find out one. Personally, I like Data anything more. I think it was one thing like, you know, I think the article's hype you up for like Data science and then like going to work. It's like, well, but I like doing engineering work more than I like doing analytic work anyways. So I think that was kind of like one that shifted and then also just the fact that there's just so much data and work to be done. So I think I think those are like the weird things that that shifted. One, just when it comes to consulting, it's a lot easier to find projects that are doing engineering. And do I like doing that work anyway? So I go do it for an answer. But I just jumped on.

Speaker1: [00:08:21] Yeah, no, that's all. Good men. Appreciate having you here, man. Thanks for taking time out of schedule to come hang out with us today. Joe, what about you? What's the belief that you are a misbelief that you held in after Joe? Let's go to Greg and guys. If you have questions, let me know. I did the Q I know that Al had a question, so we'll we'll go to Albert after Greg. So we'll go Joe, Greg and then Albert in

Speaker4: [00:08:43] The Data space for a really long time. Think going on over twenty years now. So, you know, and I think the things I always have to remind myself that what's old is new. Again, this is really nothing. That novel, actually, I'm realizing actually just chatting with a friend of mine about this because we're going to some brand new architecture that is coming up with this is actually one of the guys who wrote Facebook back in the day. He's got rich coming up with a brand new architecture that we were kind of driving on. And it's it's interesting because he's going back to the classics to like, you know, classical architectures and articles from like the nineties and crazy stuff. And and I've been doing a lot of the same thing recently, like researching old stuff and reviewing old books. I have what I realized. There's not a lot of new stuff out there. It's maybe repackaged so Data science machine learning that that's a repackaged thing that's kind of actually been around for a really long time. And so I think that for me, it was just a realization that I need to spend more time understanding the history and context of things rather than trying to chase after the latest shiny object. You know, something that over my career I just had to constantly remind myself of. There's probably some precedent that's come before. And like what you're trying to do, it isn't actually that new at all. Sometimes it is, but for the most part, the wheels already been invented to go find that. We are so otherwise you have a cold start problem every single time.

Speaker1: [00:10:12] So it's interesting. I like that point about kind of returning to to the old sort of returning to the classics. I've been reading this book this week. It's the book from

Speaker4: [00:10:20] Almost as if that is such a good

Speaker1: [00:10:22] Book. Yeah. How to Solve It by George Polya. And I don't know if I'm seeing this last name right. But oh my God, this book is helping me think about how to solve problems in a very refreshing way. If you guys are part of the newsletter, I sent you a PDF copy or a link to PDF copy of this book. So definitely open your email if you

Speaker4: [00:10:42] Especially if you're learning, if you're doing anything with math. The book is

Speaker1: [00:10:46] Fantastic. There's a lot of geometry in that book, which came in handy because I was recently reading Shaped by Jordan Ellenberg, which is all about geometry. I actually interviewed him for my podcast yesterday and it was just get lots of geometry in this book, has a lot of geometry and yeah, it's it's it's good. It's a good book. Greg, what about email? Let's say, you know the topic already, so to go for it.

Speaker2: [00:11:07] So for me is more related to my degree. I'm an industrial engineer and the way I looked at it, I guess, is because of the program, I had to learn a lot of the cross engineering functions like what is mechanical chemical material. I have to know a little bit of everything. And there are some core classes that I took which focused around operations research. So when it comes to optimizing roots or optimizing how much you want to order from from a supplier and things like that, and there was there was a good dose of statistics and there was I entered the workforce. I kind of shielded myself from this repackaged, you know, thing that Joe described earlier, which I fully agree with him, is that, you know, industry engineers have nothing to do with the whole Data science thing. And I spent the first, I don't know, seven years just shooting myself, at least almost ten years, really not realizing that. Look, I've been so deep into it during my school and I've never really cared, you know, from a statistical process control perspective that just saw you doing you controlling your online production line and to oppressor's research.

Speaker2: [00:12:25] You know, if you look at newer networks trying to optimize, you know, their their their output through gradient descent back propagation and things like that. So it's all doing some sort of optimization of the algorithm. And those are the things that if I you know, I feel like if I focus on it a little bit more a be more of a confidence to to to call myself a Data scientist today, I market myself as someone who's more on the business side, but also understands Data science. And I feel like every time I want to deep dove into the Data science piece that I would be wasting time because I also love the business side, because that's that's what drives really companies that are you know, the bottom line is, is money. And I want to be able to to to build a bridge between both. So still today it's a struggle for me, but I enjoy being in the middle. But ultimately, I want to stay on the business side.

Speaker1: [00:13:20] Thanks so much for sharing that. Greg Lisco to Albert Albert, my friend. Why don't you answer our question and then ask the question that you had then after I will go to Mikiko question and if anybody else has a question, let me know actually to the

Speaker2: [00:13:33] Queue I did you guys hear me OK? Yeah. All right. Also. Yeah. So, I mean, I would say mine, since I'm new to data science and analytics anyway, as I was more firm leadership. And I think that's the thing. When I was a new Marine and just getting put into junior leadership positions, it was that that everybody needs to be led or can be led the same way that, you know, if you're a hammer, every problem looks like a nail. All Marines are expected to be hammers. And so you get the feeling that you can kind of shout everyone into into submission and that's how everybody responds. You know, nothing could be further from the truth. So it took me a long time to kind of realize that you are a good leader, needs to figure out what his or her teammates, the subordinates, whatever you want to call them, how they need to be and how to best leverage them and their skills and strengths. And just as I went on then, there was more and more kind of tool box that you can. Yeah, you can. You can yell at them and you can trust that I'm going to like that and stomp and all that good morning stuff. Or you can be diplomatic and kind of reasoned with them or you can leave. But if there's one hundred different ways to lead somebody and get them on board with your project and a good leader, you may have more than one that fits the bill for the questioner.

Speaker1: [00:14:59] Yeah, absolutely. Man, thanks for doing that.

Speaker2: [00:15:01] Yeah.

Speaker1: [00:15:02] And you thought you got a question as well. So so go for it.

Speaker2: [00:15:05] I do. So just for. So we today on Analytica submitted our first live stream, very exciting. And as you know, we were just wrapped up to go to rehab. I don't remember what it was like twenty, twenty or twenty two guests or something like that. And some of them are repeats from the previous year. And so, you know, it's just kind of looking for the way forward and what, you know, what are we doing next. We have some some things going on, but I look around at a lot of different podcasts and YouTube channels and it seems like the interview format is is pretty saturated. And I'm wondering, you know, what do the people here think that are that are involved in some way in that business, either either as podcasters, as a YouTube channel, or consumers of that kind of content? What do you think are the underserved people, the people that don't get called up for interviews? And what are the underserved topics? Just like I said, I was just doing a webinar on a Data security and it didn't seem like it was getting traction. And so that kind of got me to thinking, like, what? What are the topics? What are the people that we're not getting into in our in our interview cycle, in our discussion of that sort of thing? And I go,

Speaker1: [00:16:28] Yeah, sure. Harpreet Sahota just off topic Data cast because other on their podcast. But also I would say so in terms of like format and type of episode, like it'd be cool to see storytelling type of episodes. I was thinking about doing this for my podcast. Right, because when I have Data scientists on, I tend to ask a lot of similar questions because I want to get a whole breadth of a range of responses on a particular topic. And I was thinking about doing something where I tell a story and weave in some responses from different people and then, you know, mix them some like nice music and sound effects in the background. So that that's one thing I think would be interesting to see storytelling type of episodes. But in terms of topics, um, I mean, I would love to hear more about like methodologies like how are people solving problems? I think that and maybe just because I've been thinking about that, maybe because I got this, how to solve a book on my mind, I'd be really interested in hearing about what people do when they come across problems that are really hairy, really messy, and how they think their way through some of those problems. Um, but I'd love to hear some more from the audience. Spencer Holley, my friend, welcome to The Art of the Science. Let's hear from you, man. What's what's some topic that you would love to hear more about that that doesn't get the coverage or cloud that you think it deserves?

Speaker2: [00:18:01] I've seen one of these before a couple of weeks ago. And from that I followed then on LinkedIn and I saw he had a post where he's talking about just the way his he's talking about knowing the business and understanding that and really, really focusing on the business side. And I feel like most people really talk about and saying kind of saying how that's like a lot like anyone knows the tools and can learn the machine learning models. But, you know, you've got

Speaker4: [00:18:30] To apply that in a way that will

Speaker2: [00:18:32] Actually generate

Speaker4: [00:18:33] The right kind of results. So maybe like if anyone knows, kind of a good

Speaker2: [00:18:38] Way to start that started learning that side of things because like I was thinking about it, it seems like like almost the most logical way in a way, not saying it is, but what to me might make sense is spoken almost. You almost have to come in on the business end. I'm not sure that that's true. But that's kind of what it looks like to me. And I don't know if anyone thinks has any advice for people coming in from a more technical side or more technical background.

Speaker1: [00:19:07] I find that really interesting, that maybe like if somebody kind of like a series or maybe just like an episode, maybe I should do this podcast and have an episode called like maybe The Forest for the Trees. And it's like an hour long episode where I talk to people about how they use the random forest in their different industries. And they kind of put that together and talk about how we, you know, here's this album that we all know and love, but here's how we implemented it in our business use case. Let's hear. I'm going to call from people in the audience, have a camera off. So, Martin Garza, let's let's hear from you. Martin, if you're talking your muted or if you put on the spot, if you don't want to

Speaker2: [00:19:46] Wallow, I'm sorry, Harp. And I can

Speaker1: [00:19:49] Hear my voice. Yeah, my little cat, I my

Speaker2: [00:19:52] Harp they have

Speaker1: [00:19:53] They I look

Speaker2: [00:19:54] In the back cover and say, hey, little

Speaker3: [00:19:56] Guy. Oh my gosh, she's so cute. She just say so cute.

Speaker2: [00:20:02] Yeah. Thank you. Well yeah. Yeah. OK, about how to approach the problem, right?

Speaker1: [00:20:14] Well, the question I was curious about is what's what's some topic or area that you feel like doesn't get a lot of exposure in Data science Data content that you would see that you would like to see more content around?

Speaker2: [00:20:32] Ok, well, that's really new in the area there at Data Science Data analysis. I have heard that you talk very focused in advance topics and I somewhere trying to catch up. Right. I mean, having a little bit of sproul's I mean, I was talking with Jennifer and I've been seeing three or four weeks ago for trying to search for an answer to. Right. I mean, I'm a Mexican guy in the border with Texas and I'm having had trouble getting a job. Right. I that and maybe how to to enter to start with this kind of Data. And now this is I mean, I have experience in mainstream manufacturing side as an operations manager and then I use a lot of it there. But being on this data analysis is really a relative thing to just start voting rights.

Speaker1: [00:21:28] A bit of a common theme there so far I'm picking up on. But Benjamin, how about you that you're a bit of a content creator yourself, and if I'm not mistaken, the Seattle Data guy. What do you think? I mean, besides Data engineering, what do you think? Underserved, underserved topic in our field content?

Speaker2: [00:21:49] I think the engineering is obviously one. I really feel like I just started with the engineering and I'm already getting a lot of people responding in such a way where they're like, wow, this is great, the content, but there's just not a big the audience. I think we're still working on that part. I mean, I think I like that whole forced entry concept, because when it comes down to it, I think as much as I feel like there's a ton of Mellops tools that are coming out and, you know, people that's been over the last year or two definitely gaining traction. I still talk to a lot of scientists and it's just this like gap of operationalizing email. Melbourne oddly still exists. I was literally talking to someone yesterday and they're like, oh yeah, we're talking like Data robot and whatever, all these other things. And we're going to, like, figure out how to launch Ramlal. I'm like, well, how are you gonna do it? I'm just curious, do you actually know how Data a lot worse? You know, it's like, no, but Data robots, they'll they'll deal with it for us. It's really cool. But I figure out how to still like. Yeah, just playing Data robot and then it'll work. That's not so. I think there's an odd gap still there where there are obviously communities like me that that's doing a lot in that space. But for some reason that you like you said, like there's still this weird gap. It's like, OK, I built this model now and I work at companies.

Speaker2: [00:23:01] When you look at big tech, a lot of them have systems, they have their email systems and they have it for the last five years. And this is for them. It's easy, but I'm always curious to see, like, what companies do that they don't have ten thousand engineers who can manage these kind of Data email pipelines. So I'm curious, like what small companies are doing, like how are they trying to manage this, how they implementing it, how is it actually working and how they deal with all the talent that comes with getting email people and actually speaking that? I think that's another whole area which is like onboarding. It's this weird section as well. Like I remember when I first started covering do design work, I was working with like a design team at a hospital and it just kind of felt like they let me flop around for like a few months and didn't really provide me much like basis in terms of like, oh, this is how we proceed on projects. This is how we take a product from like an idea or a theory into some sort of final product. And even having that, like, ability of like best practices just better set up for us, I think is an area that would be great for a lot of people that once you start, it's really like you. But what do I do? Like you have all these tools that you have no idea how they actually all work. So I think that would be interesting for me

Speaker4: [00:24:13] To add to that, too. I mean, Mikiko, to answer the question earlier about what are some good tools are a good resource, Mellops and Emily and engineering. And frankly, it seems very underserved right now. It's almost worse Data engineering. And in some ways, although I would say there's maybe more content in machine learning engineering right now, it would say there's there's more noise as well. So you signal to noise ratios out of whack of Data engineering content goes beyond myself. And contrast might be the only people actually doing anything in this area. There's others. But yeah, it's I would say that the MLP community is awesome. Shot up to be cheers for putting that together. Like I would say. Just come through that and just see what you can find. But unfortunately, there is a ton of noise right now. LASO, the person whose article you posted, he's legit. So obviously, you know Josh Tobins course. He's legit, but I mean, you talk to anybody in the field right now and it's there's no there's not really a consensus in terms of how any of the best practices shape up. Therefore, it's kind of impossible to have a rubric in terms of how to properly do small engineering just because it's a field that's in flux right now. And so it's a very underserved content area.

Speaker3: [00:25:29] Yeah, and it's funny that Benjamin mentions, like, unboring, because I'm going through onboarding for the team right now. Right. And like, I have my own boring career. Right. So my team was nice enough to put together like the onboarding boring document about the 30, 60, 90 day plan here. The people that you meet here, the tools and processes here, all the stuff. But the rest of the engineers in my cohort didn't get anything like it. They haven't even met their team, even though, like, I've been in close contact with with like the the staff entry on my team. Right. He's been he's been guiding and like doing an unofficial onboarding cycle for the last three weeks in this week. Right. So but that onboarding stuff like that's like so hard. And I see the other engineers, like in my group who are also working honestly, like on the same team. But they some of them are some of them are like, you know, back and some of them are front and dev. And they're really kind of struggling because we're all working on like a normal product. And they're like, OK, so already you're struggling with the complexity of onboarding as an engineer. But then you have to also know, like all the different systems and pipelines and they're also going through this process of where they're like updating it, too. So, like, for sure, like onboarding is like so important.

Speaker4: [00:26:38] This is interesting commentary. It reminds me of like when I go on boarding of data scientist back, I would say in the early mid twenties it's very similar because there weren't there wasn't really any set of guidelines for how to on board. And so people just kind of make it up as they go along. And unfortunately, that's just the reality. But that same thing was a data scientist. Our office got any better, frankly, but engineers are supposed to be the ones that produce the discipline and the rigor behind everything to make the data scientist succeed. So this will be. Yeah, keep us posted how this goes.

Speaker1: [00:27:11] So if I could distill down a few topics for Albert, the kind of threads I picked up on, there would be maybe a topic about how small or startup teams are functioning at smaller companies where maybe their data size practices mature. It's also some talk there about a Mellops. And I believe from Mikiko, the vibe I got was how to on board new teammates this year from Greg and then after Gregg, let's hear from Russell. What are some Data science or just let's just call it Data just Data topics that you would like to see covered in podcast or different content. And then after that, we'll get to Mikiko question and Christians question after we hear from Greg and wrestle.

Speaker2: [00:27:49] Yeah, something I've been getting curious about lately is we always talk about we have to clean Data, clean Data. Can we hear more about Data-Collection. Right. So is it ever possible to collect data clean at first try. Right. So what are the tools, what are the best practices. The other day I saw then issued a podcast but not podcast video on YouTube about something called Data Curator. I must admit I haven't watched the video yet, but I am feeling is speaking to that that person will be responsible for onboarding clean Data that you don't have to spend much effort transforming as you feed it to tomorrow's and things like that. So I don't think I hear that enough. Like we talk about cleaning, but what if we understand we can't do much about legacy collection methods? But what about fresh new collection methods that will guarantee maybe if we think big about this, that the collection will be of high quality, that we don't have to spend much time transforming for the perfect model output?

Speaker1: [00:28:52] Yeah, like that is actually so part of the job. I create my entire team, we create these these technical workshops like, you know, every month or so. And one of the technical workshops got planned for this summer is actually on that topic is data collection like, okay, great. Everybody says you have to do a new science project to show your skills. How do you get data, collect data. So, yeah, that's that's very much in line with something I was thinking about. Let's go to Russell and then after Russell we will go to Mikiko question, then Christians question. And if you guys have questions, let me add you to the queue.

Speaker2: [00:29:26] Even though I was just getting a comment probably a few minutes back now, just talking about domain expertize being really critical for a fully holistic data analysis. So learning Data, science, data engineering, Emelle. And, you know, being a rockstar in that field is great. But if you don't understand the context of the data that you're using, it's going to be more difficult to get the the output, the insights from that data to resonate with the audience. And you may struggle to understand the set elements of that data. So if you go to my is that you immediately cut off because they seem completely unnatural in the. Context of the Data on the page, you've got to on the field, you you would know that that could that could manifest naturally. So you don't want to exclude all of that's really important. And then tying in with the kind of big business intelligence side of things, you know, actually the cosmetic and the esthetic of arranging something on the pages. Well, that varies distinctly from industry to industry. So having the domain expertize to help transition right away to from that Data side to the to the report's output is very good. And what Greg is mentioning there about the Data curator Data translated, that's pretty much where I sit. I'm not a I'm not a distinct Data signs the design I use. I'm probably more Data engineering than anything else. But I've been working with Data for some 20 years or so before I even heard the term of science. So yeah, I put myself in that page and very often I will bring Data and do some pre processing moments before it goes to someone for deep analysis. Very often that's that's still myself then, but also the other teams, but also will validate the output as well to make sure that nothing has happened in the data processing that's created anomalies or anything else. So that Data curator role I think is is much wider role than just the processing of the data before it goes through for analysis.

Speaker1: [00:31:33] So yeah, I COVID by then as well. That's a really good one. All of those videos are pretty awesome. We should here today shout out to some friends in the audience to see you guys. Marina Tor, good to see you guys here. Happy to have you guys here. Robert, what's going on? So let's go to Mikiko question.

Speaker3: [00:31:49] Actually, I had a two additional ideas on the like, what are underserve topics. So so the first one actually is communication and not via data visualization, literally talking to people, especially on failed projects. I mean, talk about, like, underserve. Right. A lot of times I feel like when you look at when you look at kind of videos or content on how to communicate with your business partners, a lot of times, like the example that's used is a very clean, like successful example. But, you know, the reality is that career advancement and honestly, career growth. But like your personal career development, but career advancement through company a lot of times is on. How do you handle the hard conversations? Like how do you tell them? Like, OK, the forecast was off by like 10, 15 percent, which results in like a one million dollar loss. Right now, a lot of people don't necessarily like in their junior year's like go for that. Like, they won't have to be in that situation because hopefully their managers are not giving them one million dollar projects, but giving them, you know, maybe one hundred thousand dollar products or whatever. But I think that one is really hard and for a lot. And, you know, I think the common sort of stereotype is that a lot of people, especially are engineers or Data scientists or researchers, academics, they don't know how to talk. They don't know how to communicate. And there's almost no material. Right. So there's a I think that's an interesting topic.

Speaker3: [00:33:10] I think the second one and it's actually has already said this, but I think I want to add sort of further support for it is how do you like small, medium sized businesses like effectively use like the science machine learning more, even just statistics or math like in their processes? I posted a link. This is required reading for my staff and to me because we used to support MailChimp. But there's a question of how much GCP do we use and where is it appropriate versus using like an open source or a homegrown thing. And the reality is, I like a lot of this thought process on how companies should implement Mellops and monitoring. It's driven by bigger companies who have like building some data centers. A lot of times serious cases are much more sophisticated and they're a lot more abstract. But reality is that, like, if you're a small or medium sized business owner, you probably don't need everything. You could probably just get some added value, like just from simple tools. So I think that's like a really good area as well, is not just like how do people do starter projects or how do people use, like enterprise level tooling? But how do people just kind of determine like what's the right need and like how to implement it, implement it for them while still kind of going by, like best practices? For the most part? I think those are topics

Speaker1: [00:34:25] Will go like that. Yeah. Dealers that the people skills for analytical thinkers. You guys check that out. But sorry. Going with you, I mean to cut you off there.

Speaker3: [00:34:32] No, no, no, no, no. Yeah. I mean, like, I, I saw that book, I think one the most successful sort of I guess one of the reasons I like using when I was first getting into design was this resource called Like Lomo, which is look over my shoulder from this guy who Victor Chang, who is like a McKinsey consultant. And that's what he does, is he puts people like into sort of hotbox scenarios. And then literally it goes like here the different levels of communication. This is what poor communication looks like, successful communication and like, awesome, we'll get your promotion communication looks like and how to break it down, which I thought was like a really. A successful way to go about it, and it was helpful for me, but in terms of my question, it was I mean, there's all resources like in Alabama laundry. And I think I'm just trying to figure out, like, better filters, like how do I how do I filter to the content that is actually important versus is like being is like company marketing or like white papers or stuff like. Joe, do you get what I'm saying?

Speaker4: [00:35:29] No, I totally get what you're saying. And there's a couple of threads of this one. Your Google comment was interesting. I was actually claiming the other day with this developer, Evangelism JCP, and he brought up an interesting point because he goes into companies, works with developers at these companies to teach them how to use GCP. And the first thing he said is, look, we do a lot of things at Google, are we? We certainly don't do it. We don't recommend you do it our way. But then he keeps marketing materials and studies and it's like you can be just like Google. So I don't you know, there's a big disconnect, I think, between what marketing teams are saying and what's going on on the ground. You know, I see this a lot. Just consulting companies will adopt practices because it's like the Google or Facebook way of doing stuff or whoever, and it doesn't apply. And so I don't know how you would I know exactly what you're what you're saying. I sort of the same thing. I read a ton. And unfortunately, half the things I read are more or really lame marketing pieces that probably that practical and probably shouldn't have been written in the first place.

Speaker3: [00:36:33] So I think yeah, that's like I think I think what a struggle is. And like both the Emilienne Mellops and the engineering spaces that so much of the content education is being sort of driven by like sales, it's selling if they're selling like premium tools or it's pushing open source tools, which is totally fine. Right. Like, you know, explore for open source. But it's yeah. Like, for example, when Google dropped Vertex, right. Like of people were like, oh, it's going to replace all the ability that I asked my seven year. And he's like, yeah, you know. And then he's like, yeah, I know it's not going to replace us because we're a practice. We're not we're not a tool. And then she was posting all the LinkedIn posts where he's like, yeah, this is just the platform that's repackaged. And then there are some pictures that they haven't even released yet. So it's like I think I'm try to figure out the practice and how to continue growing in that practice in a way that is not sort of dependent on the way

Speaker4: [00:37:25] I did this back when the when the big day to day is we're starting out. And I think Data science was kind of kicking off in and I still do it. I just read a ton. And I think by just absorbing an inordinate amount of material is the only way you're going to figure out like what is bullshit and what's not, because you have to develop that filter. And I think after a bit you start understanding, OK, like this article probably I don't even read it. This article is good. Probably read it. And they start getting, I think, more importantly, a censor who is writing, who are who are the people worth paying attention to, you know, but again, there's so much noise right now in the middle of space. So I talk with Demitrius about this and he runs a community and it's like that is not it's it's so noisy. It's just really hard to make sense of all this stuff.

Speaker2: [00:38:07] I could give you guys, like, can I share a little framework with you guys that I've been learning lately for to push you to the next level? I think I think a lot of mosiah as they hire you to kind of not only perform the task at hand, but also if you want to move higher than where you are, you have to make people dream. You have to make people think about the next three, five years. So somebody like you, Mikiko, you you come across a bunch of these technologies, which are these options. You have to learn to put them inside of a framework where you're offering these options that would transform your department, transform your company, and then these options come with risk. If you are able to translate those risk for a leader or leader to understand what's at stake when they select option A versus B, then you become that person who takes for the transformation future mission of the company that puts you way ahead. You don't have to be right. People know you're not there to be right about the future, but you're here to assess and mitigate the risk of selecting GCP method versus another one. So if you go with that framework, you'll be much more comfortable. Of course, you've got to do some due diligence when you explore those different options so you can have a better idea of what will be the best path to success by selecting the best option based on X, Y, Z parameters that you would set. I think those are the things that would transform you and put you on the map for you being on the leadership team very fast. So I've I'm learning this right now and it's I wish day one when I when I joined, I knew it. So and this is what I'm applying daily right now, going for it. And I've I've learned to eliminate noise by focusing on that only thanks.

Speaker1: [00:39:52] If I could pay some bills real quick. You know who does have some awesome content around Mellops Comet Emelle? Not only that, they have an awesome platform, so definitely check out their stuff. Comet Emelle is this cool and definitely a leader in the middle of space. Benjamin. Let's hear from you on this topic. I guess we can kind of blend together the question that Mikiko had with the question that Marina is asking in the chat just recommended readings, Mellops and Deve. Was that also kind of along the same lines as your topic? We keep we keep asking them how to cut through the noise and find good resources on that particular topic.

Speaker2: [00:40:27] I think I think that's that's kind of more like I was actually thinking more about commenting so know and Joe mentioned the comment of noise and dealing with that. I think I think we're dealing with this interesting thing where I like to say it's similar to the whole photography craze that we like when iPhones and things like that came out. People kind of ask the question like, well, why would we pay for photographers? Right. Like, we can now take high quality pictures and then suddenly all realize we very terrible at taking pictures even when we have the best camera we possibly could write. Like having a great photographer makes a lot of sense. So I think it's similar with the content. I think we're realizing that, like, really good content is worth paying for. There's not a lot of people that can do it. Well, I think I think generally when it comes to like Mellops type things, like the stuff I usually like reading is anything that like any of the bigger companies like put out like. I know. I mean, like the engineering teams, like Netflix, I'm usually like following them or Uber or just cause I'm curious, like, you know, they're the ones we're having to deal with all these bigger problems. I'm like, well, how are they dealing with a lot of these problems? Are they implementing things that Joe referenced Google's article? So that's why I generally like seeing what they're actually doing rather than I think focusing on like one person. It's usually these are teams that are having problems, not like one person. So, yeah, generally any of those companies, I think that's what I like looking into, looking at the pipeline, looking at their architecture and then finding out what works or doesn't.

Speaker1: [00:41:53] I don't think you meant anybody else. Got any comments on this topic. Let me know with the next second or two. But if not, then we are going to go to Christine's question here. And if anybody else has questions, go ahead and let me do anything to the channel, actually, to the Q Christian doesn't think anybody else wants to talk about Mellops at this moment, but go ahead and go forth and have a Christian will go to Greg.

Speaker2: [00:42:15] Yeah, just made a more basic question that got me thinking recently was doing interviewing Oversight's, which drops three months ago, and I was interviewing for roles. I didn't have any experience with the product manager roles, things like that, but from a title perspective. But I had experienced entrepreneur sense of spinning a product and a company so you could tell that story. And that's the story I told. And it's actually pretty compelling when you put a story to the pain points of a business that you can understand and you could say, hey, it's a great point. Let me paint you a vision of what it's gonna look like for me to be here for the next year or two. Here's the immediate things that I'm going to do that you're probably not doing right now that will really and that seems to be something that I think is a recipe for a lot of people to be able to, like, get in the door when you maybe don't have the credentials or whatever it might be or you don't have title on your resume. But I'm wondering, to what extent does that also carry over to like a Data science role, which is also very multifaceted and also needs to think about the outcomes, but maybe at least from an outsider's perspective, has a lot more to do with technical chops as well. So does it vary or is that a really good strategy for interviews as well in the Data world? As my general question?

Speaker1: [00:43:24] Great question. Let's turn this one over to this. Let's go to Greg for for this question. But we won't go to your actual question after this. Greg, let's see if we got a good response here for for a question, Christian.

Speaker2: [00:43:36] Important to me, but I'm going to have to ask you to to to ask the question again so I can make sure I grasp it. So generally, I'm going to be more concise. Generally, I've had good experience, other with interviews in other fields of painting a picture of how I would make an impact on the role that I might not have done before. It gets me in the door even if I don't have the technical capabilities. Let's say on paper, are you seen that work in the Data science world? Do you think that works the way it is? It does it vary in kind of what are your comments about trommel? Yeah, I don't see any variations. And I say this because just about every single business case that I bring to the data science team. One thing for sure, before we even start, whether it's software that we're going to build that is powered by Emelle, we have to have what we call entitlement in that entitlement is what really drives agreement alignment on working on that project. So that entitlement is something that is directly correlated or directly connected to how much money the business is going to make. Is it going to make us more money? Is it going to save us more? Those are the things that you want to make sure you connect with. At the end of the day, a project is a project, right? So there's no difference.

Speaker2: [00:44:53] The only difference with Data projects is that you have to have the best practices on how to manage that data, because that's the one thing that you're relying on for yourself. Where are your products to. I guess act as expected to deliver some results, so you have to find a way to definitely build the bridge between your aptitudes to manipulate that Data with an increase in the business sale or decrease in cost or something like that. So you have to find that that way. And it's no different than any other projects. So you can market yourself this way. You'll be you'll be good to go. Of course, there are some other technical improvement projects that you could do. I know if you like hopping off of an old architecture to a new one, that would reduce to the cost of ownership of that architecture. Those are the things that you can you can think about. And at the end of the day, it's about really thinking about the future transformation of the business. If that new architecture is going to help you serve more business needs and also be cheaper for you to manage with less overt overhead or headcount or less Data bill that I call it, then this is something that's a clear win to anybody who understands the nature of the business that they're driving to.

Speaker1: [00:46:32] Mikiko, next. And so the question just that I just saw the version here to really reiterate it in other rules. And can it be for product or business? A customized, compelling story up front has yielded good results. Here's my intro. Here's where I see me plugging into your team, how I can help, what I know about you and competitors. Does this approach work with Data sites, hiring managers as well? Or is there a tighter focus on technical aptitude? We ought to be quico for this one, because you're you're talking about this just at the beginning of the hour here. So I think you'd be well suited in this situation.

Speaker3: [00:47:07] Yeah. OK, so for Dayside Segment eight, it will it'll depend on what the primary output is of the team. So if the team is more research focused, so first off, there is never a reason why you shouldn't do a customized, compelling story and and why you shouldn't sort of position yourself. There is never a reason why you shouldn't do that. Right. OK, the difference is what certain hire managers will care about. And what the difference is, is whether it's a research versus engineering role versus something a little more strategy oriented. Even when I was interviewing for the employee roles, there was a difference between companies like Quora where they wanted a lot more emphasis on research and specifically certain, you know, like certain things like what papers did you publish or what? What algorithms did you implement recently from papers? What do you think about X, Y, Z implementation versus the ones who are a little bit more development focused or entering focused, which was what projects did you did? Well, what projects did you work on? You know, what were the architectural components and what were the decisions and trade offs that you made versus? And then there's also ones are a little bit more like strategy, you know. So, for example, Facebook, all other data scientist roles, they call them data scientists, but they're really more like product analyst roles. And so they will care very much. So about the about like how did you connect, like the experimentation or the work that you did with the KPIs and the outcomes for your team.

Speaker3: [00:48:33] And really, I think actually that's the that's the biggest differentiator. It's like what is the what is there like outcome or what is there a KPI in a way that they're tied to for business strategy? Folks, that's what it's what was the impact on, like certain core metrics or certain core like business financial metrics, depending on what team you're serving for the research folks, it really is about like, you know, what papers that you write up, a blog post that you contribute to. What are the things that the company could, you know, sort of put on their blog, on the website saying like they were driven. And then for the engineering folks, it's literally like, what did you build and did it break and what best practices did you use? So that's, I think, where the differentiation is between those three teams. Sometimes it's a little bit hard to figure out what the team is until you get into the interview, because a lot of the job descriptions, they will literally just copy and paste between companies. So they won't necessarily always say up front, you know, but once you get into the interview, you can you can just ask them. You can be like, you know, so what is like the primary sort of like impact you want me to have?

Speaker1: [00:49:31] If I could just kind of further on Mikiko points while I interview a lot, just because I'm not necessarily looking for a new job, I just I just love being in interviews. I think they're interesting. And plus, if I'm dishing out advice to people that do sometimes dream job, I should at least be going to the same shit that they do. But I do this thing where, like, I literally won't research a company before going into the interview and it could be technical and answer all the questions. Right. But then I can't put the responses within the context of the business. I mean, like and then there are situations where really research the company and I'll get a real good idea of what their product is. I'll even tell you the product I'm. Round with it and and test it and those interviews, I tend to do a lot better because once I know what the product is like, once I get an idea of what their business is all about, I could put I could deploy my technical knowledge into context, if that makes sense. Right. And and talk to them about maybe the types of problems I think that they potentially could be facing and some possible ways that they could address those type of problems that kind of make sense. So thorough research, if you really are interested in the company, do thorough research, download their product, their product, and then deploy your technical knowledge that you do know, even though you may not have it on paper to talk about how you can help them solve their problems. That helpful little awesome application.

Speaker2: [00:50:54] Yeah, that sounds about like what I'd expect. Haven't interviewed in the Data science world yet at least. So just interesting to hear that feedback for sure I. All right,

Speaker1: [00:51:03] We'll go next question. And my dickhead neighbor has his fucking music blasting and it's like shaking this. So I apologize. What is he playing? I have no clue, but it's just bass and it's like, what the fuck are you doing, man? Like, he's shaking things back here. So that's coming up. I'm picking up through the mic. I apologize.

Speaker4: [00:51:20] That's the funny thing I've heard all day. So sorry. I'm not laughing with you.

Speaker1: [00:51:24] If not. Yeah, no, those are cool. So let's go to Greg's question. And if anybody else has a question, go ahead. Let me know right there in the chat. I'll add to the queue. But Greg, go for it.

Speaker2: [00:51:36] So my question is not about I don't think it's about Data science. It's more of something that happened that I don't understand why. I don't know if you guys heard a story about Nasdaq that's running out of computer space to store Berkshire Hathaway's value stock value. So what about the entity done? So how do we stop value is was approaching 400000, blah, blah, blah, which is the total maximum number on the computer that NASDAQ is storing numbers, which is 400 thousand two to the 30 second power. Right. So the last four digits of to do the second power are dedicated to decimals and the rest is that is is is it is it's a digit digital digits. Right. So Berkshire Hathaway stock price was going to cross that to a point where it wouldn't be able to be correct on the Nasdaq board. And I'm like, how do you select such an archaic computer storing, I guess, size to be stuck in that sense? I think they hurried up to upgrade their computers. And do you guys understand why this happened in like what was done to solve it? I don't know if you heard that story.

Speaker1: [00:52:54] That sounds like Nasdaq had a stack overflow to go.

Speaker2: [00:52:59] My my guess is it was similar to like didn't you to have had a similar problem where, like the the number that they were showing, like they finally hit like bigger than they expected. So they probably set that number, like you said, to something that whatever it was in the database or whatever it was, the midsize, a quality or whatever. And then it's like we can't do better than this. And then you got in order to like one of the things where people are like, oh, isn't it easy just to change the Data type? It's like not really like not if it's in like one hundred different places, we're going to now go through whole thing. So it's like probably with the pain. So yeah. So I'll go

Speaker3: [00:53:38] For a real example of this. It's actually MailChimp, the company I'm now working for. So they are a privately held company, which means that they there are a lot of things that are different about it. One of them is that they don't have to do quarterly planning and they don't have to do major tech technology updates. It's just whenever they need to because they're privately held, they're like, yeah, we're never going to go public ever. So we'll just update the engineering when we feel like it. And I mean, nothing I'm saying is like a company secret, but they still have a massive like most of the code is like a beast. So so, you know, it's a yeah. You can have some companies, organizations where they some companies and organizations, especially like startups or public companies, they can sometimes just be a little bit too quick on like fail fast and break things and let's change our entire infrastructure and then get stuck between three or four different vendors. And then you can have the opposite where some companies are like, well, you know, we're probably held we're not going to make any major changes until we need to and then suddenly start seeing the war room kind of just go off. And, you know, I mean, they still deliver, like, really great service. But, you know, I think I think we've only started going on GCP within the last six years or something like maybe eight. So it does it does happen in practice. It's odd because I think a lot of companies are startups, like we kind of hear them, like just kind of switching stuff all the time. So you hear more about, like, failures or. Being hacked or something like that, but you have the opposite end of the spectrum, too.

Speaker2: [00:55:09] So I'm just going to sign off just as a comment on that. Facebook is based on PSP, too. So it's just it is very clear. I mean, it's a modified modified version of PSP, but it's also geeky. But with that, I'm going to sign up. Thanks, guys, for only doing

Speaker1: [00:55:29] It, man. Thanks. Thanks for coming in. Happy to have you here. Let's hear from from Joe on this topic.

Speaker4: [00:55:35] I don't have much more to add. I mean, I'm something happened again for other reasons. But yeah, it's interesting. I mean, I could I could never have imagined that the share price would have reached this high to begin with. But that said, you know, someday it'll be a million dollars or more. I'm sure it's just kind of how the world works. Compounding is a weird thing. So hopefully it gets fixed. So, I mean, who would have thought, though, you know, back in the day that you would have a stock prices high? They'd never split. And I don't think they're ever going to. It's the whole thing is going to I guess the whole point is that you have to prepare for these contingencies. But even then, you know, as a as a general rule, I think Russell pointed out a life lesson. If any arbitrary limits are set in the system, review them regularly. And this is definitely the case that so, you know, imagine the stock market and or buffet once. Are you saying like if the stock market continues at that, the current growth rate about seven percent a year, something like that, you zoom out to at the twenty one hundred the stock market, the Dow needs to be at like three million. So, I mean, think about that for of, what, 30 something thousand right now? Yeah. So, I mean, that's crazy. Yeah. So I mean, but I don't know what protections people have and just a matter of free three million level Data index. Right. That's unfathomably huge right now. But then back in the 20s, I think what the Dow was maybe was two digits or maybe low 3s. So something to think about.

Speaker3: [00:57:01] I like this is where Carlos will come in to talk about decentralized currency and other technology underpinnings for how you would need to distribute and tabulated.

Speaker4: [00:57:11] Also, I guess just this don't don't allow for, like, overflow errors in your every first step. So that's how it is. You know,

Speaker1: [00:57:20] If you're listening, Carlos, I don't know. Carlos listens back on these things, but it's been a while since the Bitly.com/adsoh coming out otherwise. Well, Greg, in our meeting with him on Thursday, so I told him personally that his presence is being missed. If anybody has questions, now is the time to ask. Shout out to shouts of friends. We didn't hear from Toure, my friend. Always good to have you here. Always good to see you. Uh, uh, missed missed hearing some of your commentary.

Speaker5: [00:57:45] So sorry I'm not talking too much. That's been bogged down in transactional data and I think the last couple of weeks don't work. I don't know that I've gotten my 14 hour days now. So I got another couple of weeks ago today. I was supposed to relocate to Norway and. Oh, well, it's not the right tax bill. I have to go back again or fly out again or something. So I was actually flying by Amsterdam and in Holland, they're requiring the PCR test. And in Norway, where I'm going, they just need another type of test. And of course, I didn't have the PCR. So there you go. No flight. And so here I am still stuck in France. That should be in quarantine in Norway for the next ten days.

Speaker1: [00:58:33] Hopefully, hopefully you're enjoying some good wine. And you've also presented reason number 314, why you should not be an accountant. What's up to everybody else? I was hanging out last minute questions. If anybody has questions, we got a day or. Erik, good to see you here, Erik. And this person with a cool name them on a butcher. I try not to butcher it to megamix know and anybody else. If anybody has questions, now's last time to ask them otherwise of my stall until it is time to go. Go for Mikiko.

Speaker3: [00:59:08] Anyone else. I don't say thanks. Jo mentioned that he reads on Saturday mornings. He reads seven seventy articles. Does anyone else have suggestions for how to be more productive? I'm really, I'm really struggling with that, especially since I'm trying to get used to waking up three hours earlier because of my teams in Atlanta. So I struggling with the creativity part, the learning part.

Speaker1: [00:59:31] I carve out like an hour, two hours every morning. And that's like the only chance I get to get reading in part of that will be just like I'll be writing and another hour and a half or so reading. But you're waking up early is the only way, right? If if you don't have if you feel like you don't have enough time, then waking up early is your only option to get more time or just block off chunks of your calendar at work so people cannot schedule meetings with you. I do that a lot too. And I'll just use that time to read as well to go for it.

Speaker2: [01:00:01] I was going to say, before you do any damage, you go read atomic habits. Yeah. Yeah. Listen to what he says about how much space you have to reorganize yourself. It'll be helpful.

Speaker1: [01:00:13] Tiny habits. Another good one as well.

Speaker2: [01:00:15] Dining habits.

Speaker1: [01:00:15] Yes, habits. B.J. Fogg think he's quite active on clubhouse to be atomic. Have the power of habit. Another good one. All these books have it out there. Anybody else have any, quote unquote hacks or anything they do to to get their learning

Speaker3: [01:00:30] In or even routine? Because we have like a routine or ritual.

Speaker4: [01:00:35] I think you just answered it honestly. Like, it just has to become a routine.

Speaker2: [01:00:38] Yeah, right.

Speaker4: [01:00:40] Somebody mentioned getting up early. It works. Yeah. The problem is you got to go to bed early too. Otherwise your brain like fries. Yeah.

Speaker2: [01:00:48] And you just have

Speaker4: [01:00:49] To working well

Speaker3: [01:00:50] At seven a.m. stand ups for three days a week now so. Well yeah. Three hours out. So they meet 10 a.m. Atlanta time. So I need to be about seven a.m. here.

Speaker4: [01:01:00] You can be like JoCo willing and then take a picture of your watch every morning at like four thirty or some ungodly hour like that. When he's up I get up at 5:00 usually and read, but there's no there's not really a half great. He's got to make a time and just make it a habit. So there is a secret.

Speaker3: [01:01:14] Oh so good at that before finking. OK, yeah. Yeah.

Speaker4: [01:01:18] It's like exercising right. I mean you're crosthwaite or you know how it is. It's like you're going to make the time for your routine. There's no shortcuts. Anyone who tells you the shortcuts is bullshit. And so it's time and energy.

Speaker1: [01:01:33] Yeah. Time adds for other things like watching TV or YouTube or whatever is it. But yeah, I mean,

Speaker3: [01:01:40] I guess the other half, the question is so OK, so, so I want to like next level up within the year, year and a half to get to senior Emily. So what are, what would be the activities I should be prioritizing. I feel like reading case studies is good, but to some degree because I'm in I'm like really junior high on the totem pole for once, which is really nice. It was so nice. But since I'm really junior idol, I'm not necessarily like in the position to make like technology classes. Right. So by the same time I want to continue like upscaling, but development's not the same at advancement. So basically I want to figure out what are the three where are the three or four activities I should be prioritizing?

Speaker4: [01:02:21] You want to. Sure.

Speaker3: [01:02:22] People know I want to go I want to go up the technical contributor.

Speaker4: [01:02:26] That saves a ton of questions. OK, well, some people advanced. They become they become a manager. And this usually isn't the best thing in the world for them either, because like now, most people shouldn't manage. Actually, they have no business doing it. So that's good. You're if you got that out of the way, I think that clears it up. I think the technical progression is just is just it's just, you know, to make the weight training equivalent of sets and reps is really all comes down to, you know, maybe set some goals for yourself in terms of learning. So I have a spreadsheet where I have like my learning path basically for like three months and just every every day, basically, it's the progression and maybe that's where you how you do it. But really, just when it comes down to being senior, though, it's kind of like what a senior for your team, like, what are they expecting to do? Like, that's most of it. And like you can contribute and add your weight and add value. I think that's how most of you become a senior.

Speaker2: [01:03:16] I think your best place right now to start the conversation. Now, Mikiko, I wouldn't wait because when it comes to getting promoted, it depends a lot about your your manager. Right? Depends a lot on that. But you're in control of asking for that. So for me, he's in our matrix is super helpful to kind of the clutter when needed. Prioritize the future transformation of a department to addressing the now needed urgencies. So aligning with your manager on what does a senior MLT portfolio looks like in terms of how do you how do you act? So Inken are the things I'm working on? Do they have senior NLP scope? You want to align with him or her as fast as possible and then create a plan for performing at that level at your current level right now? So if you have some sort of level guideline available right now for how senior employees, what do they work on, what kind of tasks, what kind of scope did you have McLeese of these tasks and attach the things you working on next to them to make sure that you're working on the right things. But you have to make sure you have that alignment with your manager as fast as possible to make sure that you get no surprises. You close the gap as fast as possible, and it's a no brainer when it's time for promotion.

Speaker1: [01:04:40] Just just a question kind of follow up. Just drill down a little bit more. So is the objective to move up the ladder in terms of title, or is the objective to move up the skill set in terms of ability mean not that the not that they both like are mutually exclusive, but which one is it that you're trying to optimize for so.

Speaker3: [01:04:58] Right for for the short term, it's the skills because I'm realizing that especially right. Coming from the design. Today on background, you know, I don't say I'm a Shi'ite engineer, but there's a lot of bad habits and bad practices that I developed or a lot of good practices I didn't develop because I didn't know right until recently. So I think in the short term, it's definitely I want to like I want to add value does have to be a huge value. And that value could be just not being like a sink by one, like add value. I want to like develop the like first principles skill set, but I want to then translate that into advancement up the ladder. I also I think with think well OK with this might be a generalization, but it seems like engineering to be honest. It's a little it's a lot harder to kind of bullshit you bullshit your way up. It seems like you do need to hit certain kind of like milestones or like sort of have certain projects under your belt. Luckily, I have a meeting with my director next week, so honestly, I'll just bring that up because why not? Right. Yeah. And like it, they have ladders, they're redeveloping it this year. So it's not super sandstone, but they're working on it. And then my hiring manager, she actually only started this week. And at MailChimp, they don't let managers I honestly don't know. Managers are encouraged to take the first two months to do a listening period. So right now she's still going is the team and all that. But yeah, because I think something I'm realizing is that there's a lot of like Data hustle, a Data grind out there. And what I want to do is try to still down serve my activities to what is actually relevant and important. But I think yeah, like I'll have that conversation with my director because I think he'll have a yeah, he'll have some expectations.

Speaker1: [01:06:43] So I mean, well, you know, I came from an academic background as a proper statistician, like a clinical trial statistician, and then moving into like a technology company, I had to up my software engineering skills and not that I have any software engineering skills, but I can write good, reproducible code that is production ready. But the way I got from zero to that was I just like put myself onto projects because I had the I had the luxury of, you know, choosing which project I wanted to work on and would put myself on projects that I knew were difficult for me and then would pair program as much as possible with like the nicest engineer who was like most willing, you know, they're all pretty nice. Right. So for the most part, and just pare program with them and work through problems that way and then just ask them like, oh, why did you do that? Like, you know, I would have thought about doing it this way, but you didn't get that way. So help me help me make the leap from, you know, my reasoning to your reasoning and try to do it that way, if that makes sense, that us you have a hand up, go for it.

Speaker5: [01:07:44] It's all right. I was just going to say that technically, like everybody in this group, more or less, you're all at the eighty percent already. It's just the last twenty percent you need to fill out because you're asking the right questions. You're curious. You're looking into you're listening to advice. These are all the things that technically is what's going to get you there for you. I don't think it's a question of not getting there. It's just a question. But you will get there. But I think what's most important is to be a little bit patient and humble because you may come across as too aggressive and that scares people. So when you start getting that kind of this is part of my own experience, is that I have a tendency of scaring people because I get very active and all that. I want to do things and fix things. And I'm not necessarily always listening to the the atmosphere I'm in at the moment. And that can scare off a lot of people. But clearly, 80 percent are already there. It's just a question of organizing it. And with the advice you're getting here, there's no doubt in my mind you'll get there. And when you listen to the Joe and Greg and everybody here, they're all been there. They're doing it. They've done it. That's also why they're smiling when they hear the questions as well. So far, so good luck with that. Just take your time and you'll get there. Don't worry.

Speaker1: [01:09:09] Excellent words of encouragement, talk. And if you if you have I mean, it's just the book recommendation. But if you have audible and you have the premium membership, whatever, there's a book on there called The Practicing Mind, and I've been listening to that this week. And one thing that that book really exposes is and you hear this advice on a lot of different types of philosophy, but disconnecting yourself from the results of the outcome, it's cool to have a goal, but don't let the goal be the be all and all. Let it be kind of more of a directional kind of place and then use practice in the process as a rudder to steer your way to that goal. So focus more on the practice and practicing and let go the goal. Um, a lot of good advice here on books. I saw a book here from Greg called How to Read a Book. That's an interesting one.

Speaker3: [01:09:58] I'm ordering all the them on Amazon right now even as we speak.

Speaker1: [01:10:01] Another book recommendation, Jim Quicks book. The book is amazing. I've I've enrolled in. Pretty much all of Jim Quix online course is the really good Jim Quirk, if you're listening, please come on a podcast. Love to have you. He's not listening. You'll never come on the podcast. Guys had no other questions here. I don't see any other questions, but thank you guys so much for taking time out of schedule to be here today. Appreciate seeing you guys all here. It was missed you guys last week and I'm glad you guys are here today, though. Be sure to tune into the episode that released today with Dave Gray, author of several best selling books, including Limited Thinking. Next week I got an episode. It's next week is the first Friday of the month. So it's like one of my quote unquote conversations episode. And I'm doing that one with one of my good friends. Argin Argin is also the host of the Rising Latorre Laterally podcast. Think I was on that podcast? Yes, I was. So definitely take a look at that other book recommendation. Thinking fast and slow. That's a good one. Daniel Kahneman actually released a new book just recently, I think it was called I Can't Remember the name of it, but I got it on Audible. Um, I'm looking forward to digging

Speaker4: [01:11:10] Into all the noise. Noise.

Speaker1: [01:11:12] Have you read it yet?

Speaker4: [01:11:13] I haven't read it yet. It's on the on the list, so. Yeah. See how it is. I didn't get that good reviews for some reason though. Yeah. But I mean it's actually good.

Speaker1: [01:11:22] Yeah. So I love it and I don't pay attention reviews you know, I just, just read it if you like the author.

Speaker2: [01:11:29] Exactly. Yeah. Yeah. I just finished this book. I love going back to the things that we've been doing for so long and we don't know why we're here. There's this book called Applied Economics and I've listened to it a lot. Oh, that's why we like to purchase these things so much. That's why these houses are annoying to purchase things like that, or that's why this was happening and it's so good. So it's a good one to get to economics.

Speaker1: [01:11:55] Yeah, I've got a I've got a plan to to touch on the fundamentals again this summer cos something I do over the summer is, is go back to fundamentals and basics. And I've got two different microeconomic books that I plan on touching on. One of them is macro microeconomics, not macro microeconomics for dummies. And I got the Cartoon Guide to Microeconomics and then I'm doing the rational optimist. I maritally read it once before, but I feel like doing the economics thing again. Get back to that will be get started. Mikiko you're saying some

Speaker3: [01:12:26] Greg orthopraxy mind. Who is it. Thomastown.

Speaker1: [01:12:29] Ok, that's the one.

Speaker2: [01:12:30] Yeah. Yeah. Thomas Starner.

Speaker1: [01:12:31] Yeah. And it's free right now on Orrible if you have premium. I've been listening to it and I feel like it's just like the book that I need to be listening to right now just because I feel a bit. It happens every now and then you feel a bit scattered and a bit unfocused in your mind. Um, so I'm looking forward to, uh, to to make my way through. That doesn't look like there's any other questions right there in the chat. Um, Joe's got another amazing book here, compounding passionate lifelong learning. All right. So, yeah, there's going to be a few books delivered to me this week, so that's awesome.

Speaker2: [01:13:05] Can I make a quick confession for you, Harpreet? Yeah, please. You know, here's what this is my time for you to. Thank you. Here's one reason. Your Show Me Being English is my third language. And a lot of times I don't have the confidence to express myself. It's good here. It feels good here. It's convenient. But I'm also questioning how it comes out. And sometimes I don't get I'm not understood. So your show helps me gain that confidence that I need. So wherever I go when I talk, I know what I say can be wrong, but also I'm not afraid to get corrected, but from anyone. And this also is practice for me on a weekly basis for any other thing that I get invited in. So thank you for having me and I appreciate that

Speaker1: [01:13:52] Man is always my pleasure to have you, my brother. Thank you. Thank you for being here. Speaking of things that Greg gets invited to, do not forget to register for the Data Community Content Creators Award. Gregersen be speaking there. Ben Tailers be speaking there. Sarah is going to be speaking there. So is somebody who's got some fire content that hasn't been on my podcast and these officers yet. But she does need to dial Lou. And there's a Gilbert Gilbert, me speaking there. So definitely go and check that out. Joe, sorry you had to see that you are now right on that. Well, um, a lot of good book recommendations. There's a lot of great links here in the in the in the chat. I'll be sure to link all that stuff right there into the show note so that everybody can can go through and read all that stuff. Take care everybody. Have a good rest of the evening, afternoon and weekend. Long weekend for some of you guys. Remember, you've got one life on this planet. Why not try to do some big everybody? Cheers.