Harpreet: [00:00:05] What's up, everybody, welcome, welcome to the artist Data side, happy hour, it is Friday, October 22nd, 2021. Man, it's getting cold up here in Winnipeg. It's like forty one degrees Fahrenheit outside. It's proper winter. Ken, what's going on, man? I got to get a aloha. Sweater that is next on the list of items when I go shopping. Hopefully, you guys got a chance to tune in to the episode of release today with Emily Borchetta as we talked about her book Clear, Closer, Better. Alison went live a few times this week, man. I've been going live, so on. Earlier this week, I was live with with Natalie Nixon. We talked about her book The Creativity Leap and yesterday went live with Christina Stephanopoulos. That's all there for you to view on YouTube if you want to, but have no fear will be released on the podcast as well at some point in the near future. Next week, I got an episode that's being released that I'm super excited about Matt. I got Andy Hunt on the podcast. You might know him as the author of a very, very famous book, The Pragmatic Programmer. That's the book I know that a lot of us know and love, so had the honor of talking to him. We talked about his book. Automatic programing, but also pragmatic thinking and learning, which was quite awesome, and if you did not yet get enough of my live streams, I will be live tomorrow, October 23rd at 10:00 a.m. Central Time with the Data professor who Harpreet: [00:01:40] Himself will be Harpreet: [00:01:41] Live streamed with with Chanin, and I'm excited for Harpreet: [00:01:44] That. So be sure to Harpreet: [00:01:46] Tune in for that. And also, if you haven't. Make sure you go back and listen to the comet ML office hours I did on Wednesday. I know it's not released as part of the podcast anymore, but they're still live streamed on YouTube [00:02:00] and the YouTube video is there for you to check out. We had been coming on the show was pretty much just me and Vin the entire time, so I got to interview him again pretty much for the podcast, Harpreet: [00:02:11] So I'll Harpreet: [00:02:12] Do my best to get that episode. Kind of. Appalled and and maybe the queue to the podcast, but I am super excited to be here today, hopefully you guys are as well. Yes, let's get into this man. What's your favorite Data science shortcut hack or trick that you found just magical that once he learned it, you're like, Oh my God, this is amazing. I wish everybody knew this. Let's start with Serge. Then we go to a can and and then after that, by the way, everybody tuning in on Harpreet: [00:02:42] Linkedin and Harpreet: [00:02:43] On YouTube, I am monitoring. So let me know if you've got a question. Go ahead and type your question right there into the comments. And if you guys have questions in the Zoom room, by all means, let me know Serge go for it. You are on mute. Serge: [00:03:04] I love numpy.where Um, I'm a huge fan of. I use it a lot. It's amazing how much I can do with NumPy, where, yeah, Harpreet: [00:03:15] It's quite versatile. I was using that earlier this week, actually. What was life like before using a numpy.where No, no, oh Serge: [00:03:24] Geez, I guess I would use some kind of apply with some kind of, you know, lambda or something? Yeah, it was complicated. Yeah, it's quite tedious. Well, lambda is also great. You know, in some cases, it's so easy to use. Also, I tend to put lambda inside of brackets beside pandas, Data frames and and also when I do group AIs, it's very easy to find what I'm looking for with the Lambda. Harpreet: [00:03:53] Yeah, yeah. That's a good, good, versatile combination, for sure. I mean, shit like that. Why do you need a sequel? I don't know. I don't know. I can't [00:04:00] go for it. Ken: [00:04:02] So I actually took this in a different way, like what is a hack that you like to learn Data science faster in the career? Harpreet: [00:04:09] Oh, that's great, man. Like anything, you consider a hack? Yeah. Ken: [00:04:12] One of my favorite things to do is reviewing other people's code on Kaggle. So just going through notebooks and Harpreet: [00:04:19] Just kind of taking a peek Ken: [00:04:20] Into someone else's thought process, especially in the earlier stages or when learning something completely new. That for me, it completely supercharge the learning process because. It was a lot of the things we're outside of, of my realm of knowledge, right? And so to get exposure to those things, I can either look at the documentation or I can look at it when it's applied. And a lot of the techniques or a lot of the subtleties that other people used in practice were a little bit different. It gave me more context around a unique, Harpreet: [00:04:52] You know, a unique Ken: [00:04:53] Function or whatever it might be. On the other side of that, something that that I use probably more than I should is just like pivot tables and pandas. That to me, like when you start out, you're like, Oh, just group by everything. And Pivot Table is so much easier for me to use than that. I use that in almost every exploratory analysis that I do. Harpreet: [00:05:13] So I think those are two Ken: [00:05:15] Sides of different coins, but still answer the same question, hopefully. Harpreet: [00:05:19] Yeah, absolutely. I was that I used the Nampai or sorry pandas pivot table or this week I was doing a binary classification, just wanted to quickly glean the Harpreet: [00:05:26] Distribution of the classes Harpreet: [00:05:28] Across a couple of particular features, and that came quite in handy. I'm wondering, like you said, you spend a lot of time reading other people's code. Do you think most people, when they are writing code, appreciate the fact that code is read more often than it is written? Ken: [00:05:47] I mean, I don't think people appreciate that fact, and I think Harpreet: [00:05:51] Like from a learning perspective, Ken: [00:05:54] It should be. It depends on what stage of learning. Right. I'm 100 percent in the camp that you [00:06:00] should do everything hands on. You should work on as many projects as possible, whatever it might be. Harpreet: [00:06:04] But sometimes that's just like taking Ken: [00:06:07] Other code that you've seen and experimenting with it, right? If I'm going through and I was starting and doing the Titanic dataset like, I don't see it as a problem. If if I'm like looking at someone else's code, I'm like copying the cells and I'm experimenting with like tuning the parameters or wherever it is, right? Like like how you actually train a model or Harpreet: [00:06:27] How you look at Ken: [00:06:28] The distributions of the histogram. It's not going to be like that different from person to person. If you're doing the same analysis like you should be learning on the edge of what is like. Part of your understanding and a lot of the time, what we do in Data science is repetitive. Harpreet: [00:06:45] Right? We should be Ken: [00:06:45] Trying to work on the things through their repetition and copying code that is going to be different for us or going to be unique or that's going to change from analysis to analysis. And a big part Harpreet: [00:06:56] Of that is just like Ken: [00:06:58] Reading and observing and exploring other different code that you haven't seen before. Harpreet: [00:07:04] But I can't thank you very much a question that we kicking off with my friends Harpreet: [00:07:08] Is what is your favorite Harpreet: [00:07:10] Data science shortcut? Life hack, whatever that you've learned could be, however, you take that that phrase, however you mean it. Let's go to Venus, go to Eric. And then if Marina Wrestle or Matt, I would like to jump in, please do. And then, by the way, remember that we are taking questions. So LinkedIn YouTube. If you guys got questions, let me know. Ken: [00:07:31] Yeah, I got two that are kind of sideways, one is Tensorflow extended stack. I know everybody loves PyTorch now, but Tensorflow extended like introduced me to the easiest way possible to get a model into production. I mean, and it's you know, when you talk about data science, it's not so much learning data science, but learning to make it useful and get it off your laptop and actually put it into production. Tensorflow extended. It is kind of one of those stacks where it's just really easy. And if you've never done it before [00:08:00] Harpreet: [00:08:00] And like Kubernetes Ken: [00:08:01] Kind of scares you extend, it is really easy to just. It just works in the documentations there. And it's it's kind of awesome, even though, like I said, it's fallen behind a little bit. But still, I like that as a hack. And the other one's kind of weird, but using an IED that makes warnings, not just errors, like any time you see a weird squiggly line or a highlight or something like that, chase it down because Harpreet: [00:08:25] You're going to Ken: [00:08:26] Learn best practices and coding that you didn't even know existed. And when it comes to data science, I don't know why. Worst practices come back to bite you so much harder in Data science than they do, even in building like really serious back end systems, but for whatever reason. Worst practices have caused me no end of pain, where if I had just chased the Squiggly Harpreet: [00:08:51] Line or Ken: [00:08:52] The yellow highlight or whatever it Harpreet: [00:08:54] Was, I'd have been done two Ken: [00:08:55] Days ago. So those are my kind of sideways hacks. Harpreet: [00:09:00] Absolutely love that. Speaking of AIDS, do like what's your what's your take on extensions like? I've added some extensions and then sometimes I just feel like they get in the way. Harpreet: [00:09:09] And I just like I Harpreet: [00:09:11] Almost like coding just with with text, running it and then just getting there. What are your thoughts on that? Ken: [00:09:16] You're savage. Absolutely savage. I think it's great. I love to use because I'm old. I mean, you can see of short in my beard a little bit, so you don't see the gray anymore. But like I, Harpreet: [00:09:28] I remember Borland. That's that's how old I am. Ken: [00:09:32] And they used to be horrible. And now I users just so amazing and easy to use. And so I like I use intelligent, you know, plug shut out. I've used my charm before. I love every notebook there's ever existed. Somebody said Vim. And that kind of scares me a little bit Harpreet: [00:09:54] Like I've Ken: [00:09:55] Used Harpreet: [00:09:56] Them. But I'll be honest, vim intimidates me. Ken: [00:09:58] But yeah, I love add [00:10:00] ons extensions. But at some point with like pythons, the scary one. Because when you start getting your environment too complicated, you have to update your container every couple of days and it's just again. Yeah, pythons, the one where too Harpreet: [00:10:15] Many extensions are just deadly. Ken: [00:10:17] Yeah, I think that's what had a friend that he Data. He was a data scientist, really good data scientist. He would exclusively write his code in vim. And I was like, There's an easier way to do this. You know that, right? And he's like, This is just how I do it. This is how I'm always going to do it. And I'm like, If it works and your work is Harpreet: [00:10:36] Good, do whatever you want. But. Yeah, I want to Harpreet: [00:10:42] Learn how to do them, I know there's an audience member out there. Dave Mango, I believe I think Matt as well, both might be a vim users. I think it's awesome. Know the keyboard shortcuts. Eric, what's your favorite shortcut or whatever? And then Marina wrestle with the guys. Have a shortcutting share this, please do. Serge: [00:11:06] So I think the thing that came to mind for me is that made a huge difference for me, just getting started was GitHub desktop. I think because we say like, Oh do projects, oh, put your stuff on GitHub and like, for me, it was like, how do I copy and paste an entire Jupyter notebook into GitHub? Like, I don't get how to do that. You know, I didn't understand. And then like somebody who had been through my master's program just before me, I was like, Dude, GitHub desktop, change your life. Seven minute YouTube Harpreet: [00:11:36] Video And now Serge: [00:11:38] All of a sudden, I could feel like a big kid because I could push stuff and pull stuff and fetch stuff and whatever. You know, and now I don't use it so much anymore. I do still sometimes, but usually I just Harpreet: [00:11:52] Now I just push through Serge: [00:11:53] Vs code or whatever. But regardless, I don't. I rarely go to the terminal just to. Push [00:12:00] and pull and things like that, I do other stuff, but definitely GitHub desktop would be the thing. I think that really helped me get my stuff off my computer and out where other people could see it, so I could have better conversations and learn from others and share more. Harpreet: [00:12:16] Absolutely. And I say that's actually really, really good tip, thank you Harpreet: [00:12:19] For sharing that. Harpreet: [00:12:20] Kenji NWSL, too has been far from Harpreet: [00:12:23] Recently, Harpreet: [00:12:24] And if I was you with that z by HP laptop, you got that. I'd put a bunch on that thing, man, but you still got windows on there. But I guess, yeah, it's a little bit of best of both worlds. Russell go for it, Russell then Marina. Russell: [00:12:40] Evening, so there's a lot of good folk on the call here, so I'm going to go low fine with this. I think, you know, you guys can all come up with far better detail ones than me, but one of the best hacks I've got is comment in your code. Don't forget to comment. You write code this week. Have a weekend. Forget about it. Come back to it next week. At least if you like me, you'll struggle to remember what you were doing, what the purpose was. Harpreet: [00:13:08] So just add Russell: [00:13:09] Comments to your code. It's one of the best hacks I can think of when I remember to do it. I still forget sometimes, but I try to comment wherever I can. Harpreet: [00:13:19] Yeah, that's that's one thing I do a lot of is I overcome it, my hood, I feel like, but it's because I'm very, very forgetful. Like, there are certain aspects of the code which is kind of readable, but it's what it's doing. But for some of those tricky things too, yeah, I just over overcompensate with the with the comments. Marina, go for it and just just for everybody who's joining in. We're just talking about life, hacks, Harpreet: [00:13:44] Tips, tricks, Harpreet: [00:13:44] Whatever for data science, your favorite one. And if you have Harpreet: [00:13:47] Questions wherever you're tuning Harpreet: [00:13:48] In, do let me know. Linkedin YouTube talking to you guys. And if you're on LinkedIn, please share this with your network. Go for it. Serge: [00:13:55] And it for me, I think cannot really mention that. [00:14:00] Is this cover that you have in Kyle and other places. You have the whole code that you can go through and learn from somebody in other fields. You don't have that. And I feel even guilty using everybody, you know, like, that's like, it is more something I just feel like this is amazing. You can get somebody code and then go line by line and learn that. That, to me, was like opening Harpreet: [00:14:24] My my mind. Serge: [00:14:26] But probably the one that I enjoy the most is when I Harpreet: [00:14:29] Discovered, maybe like two Serge: [00:14:31] Years ago when it went to, like maybe two years ago or something, they merge. I was so happy, you Harpreet: [00:14:39] Know, the same thing with people being Serge: [00:14:41] Like the tables, but the merch, I guess I was tempted to make a T-shirt. I love merch, but it will have been a little bit weird. I just found when you discovered some of these functions, all of the sudden the amount of things that you started doing. Yeah, like I was kind of like pivoting point for me. Yet I wonder, like, Harpreet: [00:15:06] Like for forget, at least, right, like I don't I don't get really super sophisticated with my good stuff. I just push, pull whatever clone I don't do like branches or stuff like that. How how crazy have you been getting with that? I wonder. I wonder, what's the Harpreet: [00:15:22] Difference between how a Harpreet: [00:15:24] Machine learning engineer uses get versus how a data scientist uses get if anybody has any insights. Go ahead and just let me know because I would love to love to pick that apart, and then I'll just go to you first. Mexico actually has been on both sides of the fence as well. So I'd love to hear from from Mexico. If you are available Mexico. Do let us know how has your usage of Harpreet: [00:15:44] Git changed Harpreet: [00:15:46] Since you've crossed from a data scientist to machine learning engineer? Mikiko: [00:15:52] Well, for one thing, I use it. I know everyone laughs, right? So [00:16:00] a couple of things are really nice about about GitHub. First of all, if you can do pre commit hooks. So for example, if you want to do things like if you check in any Python code or SQL code and you want to like lint it and nicely format it, you can do that through a pre commit. Hooks with GitHub Harpreet: [00:16:24] Actions Mikiko: [00:16:25] Is really, really super nice, as like a lightweight CI CD solution. You get some amount, some some number for free and then you have to start paying for it. But that's something that's like severely kind of overlooked, especially if you're trying to do like a data science project. I think that's super important. The other nice thing, too, is you can set up template or PR templates. So for example, if you really want to heavily make use of like pull releases or you want people to collaborate on your project, you can set up a nice little template where you can, for example, specify like, you know, describe the change that you're making, you know, x y z. All this really cool stuff. And it's integration with or, you know, integrating GitHub with sicced tools like Travis CI Build Kite Circle CI is really pretty. It's almost seamless because a lot of times it's just setting up the right YAML file and then going like, Yes, connect it to, you know, connect my account in Travis CI or some place to my account in GitHub or this repo. So there's a lot of cool things that you know you can kind of do there. I think one of the difficulties is that like. Learning guitar and GitHub, it's really hard because gets kind of what we call like a leaky abstraction. So most people, the way they interact with it is they type in a bunch of random commands and copy paste and pray to God that they're doing something right and they don't really base their entire like repo. My suggestion, [00:18:00] honestly, is to look at the the missing semester from MIT. So it's a website that has a bunch of free videos of a class that they teach every year, where they basically cover topics that are kind of like the meta skills of engineering. So, for example, using Harpreet: [00:18:21] Command line and shell Mikiko: [00:18:24] Doing bash scripting, Harpreet: [00:18:25] Using Git and GitHub, like, Mikiko: [00:18:30] For example, like a make file like why would you use a make file? Like, why do you see that in so many projects? So they cover kind of like what they call like the meta skills of engineering and especially, I think, around data science or machine learning products. That is where it kind of gets tricky and, you know, but the beautiful part. So, yeah, so if you're trying to learn like GitHub, I would really highly recommend the lecture on Git GitHub from the missing semester from MIT because they really talk you through the underlying foundational principles. Because once you understand the principles, then you can understand the commands, even as you know, as shite, as they are. The other book I would recommend is, I think, advanced. It's like the advanced GitHub book. I got to find it Pro Pro Pro get. That book is also really fantastic. Harpreet: [00:19:25] It walks you through it. Mikiko: [00:19:27] But honestly, I would say, like, that's like, I feel like that's the biggest sort of distinction between sort of people who use GitHub really get in GitHub very powerfully, which honestly, it's mostly engineers versus like people who sort of kind of interact with it by poking at it. They kind of know that it's important and they understand version controlling is important, but they don't understand, like all the features and why you'd use them. But yeah. I know sorry, that's really that's like that's really long. Harpreet: [00:19:56] Ninety three percent of the stuff you're talking about, you have never even heard of. [00:20:00] So that is insane. And this missing semester from MIT has got a pretty interesting. Harpreet: [00:20:06] It looks Harpreet: [00:20:06] Awesome. I mean, you got, you know, of course, review overview with the shell shell tools and scripting editors like the Vim Data wrangling command line environment, Harpreet: [00:20:15] Version Harpreet: [00:20:15] Control, debugging and profiling, better programing security and cryptography potpourri. Say I just type in Harpreet: [00:20:22] Mit Harpreet: [00:20:23] Missing semester and they'll show up. Thank you very much for that. Make you go and damn you using get. That's a Mikiko: [00:20:28] Yeah, it's honestly I'd say like the life hack for data science was one I left data Harpreet: [00:20:32] Science and went over to the dark Mikiko: [00:20:34] Side of engineering. But I'd say the other life hack was honestly like cutting out a lot of the noise in Data science, machine learning and kind of making Harpreet: [00:20:43] Sure I understood the Mikiko: [00:20:45] Fundamentals because. Yeah, like I feel like there's there's like certain roles where you do need to like you should be reading papers, you should be keeping up the research, you should be on the edges and fringes of what's going on of innovation in data science, machine learning. But I feel like there's a lot of Harpreet: [00:21:04] Roles or Mikiko: [00:21:06] There are a lot of people who are in a certain stage in their career where they probably could use going back to the fundamentals. And honestly, my life hack has just been filtering out a lot of the newsletters which talk about the next nearest thing from Europe's right and instead finding those really, really obscure learning resources that go back over the fundamentals such that I can now understand all the innovations that have come up and I can I can further appreciate it because if you don't understand well, if you don't conceptually understand linear algebra, you're not going to understand Harpreet: [00:21:40] Like neural nets. You do understand neural nets. Mikiko: [00:21:43] You're not going to understand the cool shit that comes up in Europe's right. Like it's you have to have these foundational conceptual kind of places in your ladder to do the work. Harpreet: [00:21:53] Speaking of ladders, you can't get to the top ladder without the bottom 18 of them, right? I'm saying foundation's [00:22:00] my friends then. Well, the question I was asking was about get, yeah, yeah, I guess the difference between get required, get knowledge between, I guess, male engineers and data scientists. Ken: [00:22:16] I came from like hard core software development and get was the sacred thing where there were a few people who were allowed to do it and you needed to Harpreet: [00:22:28] Know like a basic Ken: [00:22:29] Amount or everyone clowns you until the day that you died. And so like, I had that, you know, I don't know, mid-level understanding of guilt because I was the, I don't know, third person on the call chain. So every once in a while I had to Harpreet: [00:22:46] Google how to do something on Ken: [00:22:47] Git. So I kind of remembered some of it. And I think that was most of us and then getting into data science and there was no source control like everything in a notebook, it was weird. And after a couple of years, I got into some worst practices where I was on teams and I would let people do that. And so like, gets this thing that I came back to in 2015 Harpreet: [00:23:10] Realizing, Oh Ken: [00:23:11] Yeah, Data science needs best practices too. And that's kind of like the arc is Harpreet: [00:23:18] If you if you start going get you Ken: [00:23:20] Start going best practices. And I don't know what it is about, get, but it's like that gateway drug into doing things better and managing your code and working more collaboratively when it comes to actually building solutions. And then solutions start being more reliable, but gets this amazing thing. But when it comes to get knowledge, I've always been. I think that third person down the call stacks. I've never really had to be Harpreet: [00:23:46] The get Ken: [00:23:47] Meister. And there's always been that one person who is. It seems like every dev shop and every Ml engineering shop has like the get deity who understands branching strategies and how to get it to [00:24:00] do acrobatics so that I don't destroy things every time I check in something questionable. So it's and I'm like the least questionable developer in most cases. So they've gotten really good at Harpreet: [00:24:12] Protecting main Ken: [00:24:14] Branches and even some of the experimental branches that you can't really mess with. And so I would say, like if you're on a team where no one understands, get the easiest way to become indispensable and to ask for like a six figure Harpreet: [00:24:29] Raise is to Ken: [00:24:30] Become the get Harpreet: [00:24:32] Meister, Ken: [00:24:33] The deity of Git. And you'll have a job for life and you get promoted pretty quick because nobody wants to learn it. Harpreet: [00:24:41] And that's actually a good case for for. Then when are you going to, you know, create a target? Best practices for Data science, of course, man. That's something I would definitely enroll in. Ken: [00:24:53] Sadly, I don't think I'm smart enough to do. Like every time I think I understand get somebody calls me up with a question. I'm like, I have no way. No, I know how to do that. Oh wow. I just broke everything, you know, it's like that arc Harpreet: [00:25:06] Where I realize Ken: [00:25:08] I don't know anything. And then I think, Oh no, no, no, yeah, I do know this, and then I don't know this. Harpreet: [00:25:12] So I think that course would be horrible. Harpreet: [00:25:16] Dean said it was the Gateway drug Gateway Git Gateway into technology. Michael Goldfarb and Mark commented that he loves get so I don't know why Mark loves get, Harpreet: [00:25:25] And then Gino wants to Harpreet: [00:25:27] Talk about using it because she was just using it earlier today, but go from Mexico and go to a Serge: [00:25:34] Market. I think it's one of these things where like, so you Mikiko: [00:25:39] Get some shops where they are Harpreet: [00:25:40] Just like, Mikiko: [00:25:42] They're like nuts over everything DevOps and agile and scrum and like in bigger tech companies, right? Like they have very, very mature sort of processes and structures. But it's one of these things where like just a little bit of process and structure and best Serge: [00:25:57] Practices, Mikiko: [00:25:58] Even if you're like a small like [00:26:00] SMB or midsize or whatever really is so beneficial, it is so beneficial is if you think about it like I and I'm dating myself in the opposite direction in a way. You know, I hear of some of these like hardcore engineers where they talk about a time like before get maybe sublime. I know sublime or what? You know, there is there is a time where people used to email each other code. I mean. And, you know, God bless it, the way I look at it is if you are by not using the best tools and practices we have today, you are not honoring their sacrifice. Can you imagine what it was like debugging code keeping track of who did what? I mean, you do get blamed. You can figure out exactly who screwed that thing up. Imagine there was no blame, right? So the way I look at it is Harpreet: [00:26:53] If you learn Mikiko: [00:26:54] From what the people before us suffer through, you are honoring that sacrifice. You are honoring it. And I think we should all do that. Harpreet: [00:27:05] We should all learn from the Mikiko: [00:27:07] People who went before us. We should integrate a little structure and process in our lives so that we're not emailing code to each other at like midnight. Little snippets of jar files. Like, let's not do that. Harpreet: [00:27:19] The first time anyone has ever given a commencement speech on a happy hour. Michael, thank you so much. That's very inspiring. Mark, go for it. And after Mark will go to China. Serge: [00:27:31] Yeah, so my my current job at home was the first time I've used it for Harpreet: [00:27:35] My job, and I think to Serge: [00:27:37] Myself, what was I doing before this point? Because now I'm like scared to code without it because I must lose something or completely messed something up. Harpreet: [00:27:46] And there's like Serge: [00:27:47] No way for me to easily go go back. And like also, it's interesting because I do work both on the engineering side and also the data science side kind of have Harpreet: [00:27:57] My foot in both both realms Serge: [00:27:59] And on the data science [00:28:00] side. It's very basic. It's just like we just don't want to mess up our code base and we have code review. It's just a gate to have code review before we merge it in someone else's picks up the code. Well, on the engineering side, that's when you get really shine to me because they're just like, All right, you have a whole code base. I'm building a Data product any implemented and connected to the code base. I don't feel scared anymore that I'm going to mess something up because I'll have to try really hard for me to crash the code base because of kind of the gates that get asked for it. But also the thing I really like about is that it forces me to think about how I do my commits. So I want to do this chunk of logic first and then commit it. I'll do another chunk of logic first and then commit it. And so it really makes me think instead of writing these long like monoliths, scripts is actually thinking like, All right, I'm going to write this function. That's going to be a piece of logic or this pull request, you know, implement this change and I may be distracted do x y z? But I should make that into another pull request. Serge: [00:29:00] So like, for example, like adding a new product feature versus like refactoring, keeping those things separate? So doing essentially just forces me to be a better programmer. Now I'm completely new ish. I still feel to Google. I'll be using it for over a year now. There's so much like I was putting in the comments, like rebasing. I always spend an hour just learning how to how to do it correctly because I always forget every single time or like when you're doing a large series of of pull requests. So I might have one gate and I say runs like our whole project that spans over months. Instead of having this giant code review where I have to review thousands of lines of code, you want to break it up into different types of pull requests. Well, that was my first time doing it. And so I layered it on top of branches and then I do a rebase and end up having to do it like rebased like a thousand lines because I messed it up somewhere. So there's definitely ways you can fall into the trap of like, [00:30:00] Oh yeah, I totally did this wrong. But if you stick to the Harpreet: [00:30:02] Basics from a data Serge: [00:30:04] Science perspective, it'll take you extremely far than not having gates. And then when you get to the scary stuff, just know there's like a whole bunch of online resources to help steer you away. At the very least, they just won't merge it and you'll be OK. Harpreet: [00:30:19] Absolutely love this man like this is a really make me feel insecure about my get my get knowledge, you know, go for it and then do you know after this you have a leftover question from last week. Do you want to get into that after your comment? Serge: [00:30:35] It sure would be happy to do that. Yeah. So a couple of comments. First on get gosh. When I started the Data Science Bootcamp, we were using Harpreet: [00:30:48] It, but not in a very Serge: [00:30:50] Useful way. And so Harpreet: [00:30:54] Then I'm like, Serge: [00:30:55] Ok, at some point I'm like, I need to get more serious about using this resource. And so I have kind of a, well, it's not. It's sort of a horror story, I guess. Harpreet: [00:31:06] So it's like, you think, Well, what could Serge: [00:31:09] What could really go wrong? Which sounds a little crazy. But I had a big project and I ended up screwing it all up because Harpreet: [00:31:20] I guess I didn't Serge: [00:31:21] Really know what I was doing on it. And so it was like a cautionary tale. And I'm like, OK, I need to do something to upskill. So I posted a link in the chat for a Udemy course on Get, which is done by Colt Harpreet: [00:31:37] Steele, who many of you probably Serge: [00:31:39] Know. If you've done Udemy courses, I think he's quite good. I think the course is quite good, so that may be helpful for people. Yeah, there's a shout out to called steel in the chat from Eric Sims so that so when I did that, I'm like, OK, I'm getting a better handle on this thing. I then [00:32:00] had a my capstone project, which I was using get locally before pushing it out to GitHub. And one thing I noticed it was a big project and I noticed that the notebooks kept getting bigger and bigger and bigger and bigger, even though I wasn't adding stuff. And eventually I realized having done some Google searches, that the problem was that Harpreet: [00:32:27] I guess doesn't deal very Serge: [00:32:30] Well with with these kinds of revisions because I think it literally saved right? It saves everything, saves all your graphs, and every time you Harpreet: [00:32:42] Run Serge: [00:32:42] A, you know, just to Harpreet: [00:32:44] Check out your data frame, you run that Serge: [00:32:47] It, you think it saves all of that. So point is that the notebook was getting huge and it would take forever to to actually like even just load it on the computer. And I can't even remember what code I had to write, but you have I actually had to do something within. There's some code you can put in to Harpreet: [00:33:08] Get using Serge: [00:33:09] Command line, but then also basically pretty much. I would, you know. Restart the colonel before saving it, so basically that would take out all of that stuff and the notebook would come back down to being a very reasonable size. So I kind of put that out there because this is Data science related, and I think that that would be useful information for people who are looking to use good. And also someone commented in the comments. I feel seen. And so do I, because I'm thinking, am I the only person who's struggling with this? So it's really fascinating to hear other people's stories Harpreet: [00:33:50] And Serge: [00:33:50] Experiences with get and. And yeah, I'm sorry. I was working. I was creating a get repo and kind of updating [00:34:00] it right? Literally as the happy hour started. And so Harpreet: [00:34:05] That's why I wanted Serge: [00:34:06] To get here on time. But I joined Harpreet: [00:34:08] Late. Serge: [00:34:09] So yes, Harpreet: [00:34:10] Great moment, right when we were talking about Serge: [00:34:12] This. I pulled up the LinkedIn first and I'm like, Oh, wait, they're talking about, get how timely? Yeah. So I'm curious to know if anyone has any thoughts on that or. Yeah. Harpreet: [00:34:26] Uh, that me, I'm sure everybody here has got something to say about that good stuff because it's I mean, really the chat it's talking about the terminal. I just want to share my quick though. [00:34:39] This. You know, right there. Harpreet: [00:34:43] But you you did have a question from last week that was really good that I would love to get to. Whenever you're ready for that, let me know. Russell says another good lo fi act Harpreet: [00:34:51] That he has, which is Harpreet: [00:34:52] A good one that nobody has mentioned yet, is keep a library of code snippets that you can later reuse or helper functions and things like that. That is really critical. Is the building. Greg, what's up? Eric Gitonga is in the house. Eric, what's going on, my friend? A.. Good to see you, Mark. Sorry, Matt Blazer, as well as the hard questions, he has that question, man. You know. As I have no clue where to take it now, Mikiko: [00:35:25] Actually, let me just drop three, so three things that probably most people didn't know they could do with get number one, if you had a bug, sneak into your code at some point, you can actually find that, by the way, you could potentially find it. By searching through like your commit history or the logs, the second one is you can compare like your different git commits also to see like what changes were made, and you can compare like commits from like many different histories, right? You can kind of do that already in the GitHub UI, but you can do that like in the command line. And also, you can make like your [00:36:00] commit trees, too, which is really nice. Harpreet: [00:36:06] When you first started, like doing all this crazy stuff that I would get me Harpreet: [00:36:09] Kick or were you like, scared that you Harpreet: [00:36:10] Might break something or did you first practice on just like your own like personal repository with just, you know? Mikiko: [00:36:18] Yeah, I think, yeah, I think I've always been scared of making mistakes, right, because like, that's the worst thing, and I mean, like, so I. Ok, so like, I've seen someone get fired for making a. For merging some bad stuff into a main code base and all that, so I'm going to be honest, that really made me afraid to do stuff like I was like, Oh my God, if I move in any direction, I could get fired. You know, and it, I think. I mean, first off, like in retrospect, when I think about it, it's one of these things where like when people do stuff with like a code base or whatever, you should never see it as like. Instead of kind of blaming a person we should always like, introspect about, could the process have been better? Can we set up structures in which people can thrive without feeling like they're making mistakes, right? And I think the way a teen treats their code base will in some ways sort of tell you the culture of that team and company, right? Because if you think about it like now, there are ways to, for example, in GitHub, you can enable like branch Harpreet: [00:37:36] Protection of the default Mikiko: [00:37:37] Branch of the main branch or the default branch. Like, you can enable that so that no one can commit to it without getting a proper review, right? So there are checks in place. It's just will the team or the organization Harpreet: [00:37:49] In a way step up to say, like, Hey, Mikiko: [00:37:51] You know, this was an accountability thing, right? For example, like the was a Toy Story movie that got deleted twice in its entirety [00:38:00] at Pixar every once. Yeah, so everyone has like their story of when they screwed up the code base, right? Or the database. That's a very famous Harpreet: [00:38:10] One because Mikiko: [00:38:13] They literally had a rip hatchet from different servers that people had, but that entire movie was deleted twice. Now that being said, apparently Harpreet: [00:38:22] The the Mikiko: [00:38:23] Dba was not fired. No one knows who it is. There's never been a whisper of a name because they looked and they went, Look, we were hypothetically supposed to have been backing everything up and we didn't. And this is not like one person's fault. This is the responsibility accountability of an entire team. Right? So I was a little bit worried. But the nice thing is that once you kind of learn the underpinnings, so for example, almost nothing in get is destructive, you have to really be intentionally destroying code. To screw up the way the jet model works is it's additive, so unless you use like a special command that you don't understand, it will just keep adding versions, right? So you can always go back and you can roll stuff back. Harpreet: [00:39:09] You can go back. Mikiko: [00:39:09] You can. And I've done this before, right? We were running updates on a bunch of projects. And that's like one part of the whole Mhlongo ops role. All right. We were rolling updates on a bunch of like models and production, Harpreet: [00:39:22] And we in some cases Mikiko: [00:39:25] The initial update didn't go well. So we had a role. We had to roll it back to a prior commit. And then we just redid it, and it was fine, right? But that's what having like a proper structure and process will enable you do. And also understanding that underlying concept, most things Harpreet: [00:39:40] Tend to be sort of as long as you kind Mikiko: [00:39:42] Of version control, most things tend to be just additive. So once you understand that, it's like you can just kind of do whatever you want with it to some, to some degree, right? But yeah, so I would say like. In general, it does seem [00:40:00] like sometimes there's a lot of gatekeeping on the engineering and data science machine learning side. I do think that in general, people should not be so worried about making mistakes. If you do make a mistake, understand that there probably should have been a process or a structure to prevent you from making that colossal mistake. Harpreet: [00:40:16] Um, yeah. Thank you very much. Akiko, let's go to Gina for the next question. Then if anybody else has a question, please Harpreet: [00:40:23] Do let me know. Harpreet: [00:40:26] Linkedin if you guys are watching, there's like nine. If you watch on LinkedIn, I should probably do a better job of actually like informing people when I'm going live. Serge: [00:40:35] Can I can I ask a quick question? Yeah. Yeah. While we're on the GitHub question, what's the difference between GitLab and GitHub? Like an only open source, GitLab is open source. Gitlab just went public. Um, and why would a company choose one over another? Or an individual choose one over another? Harpreet: [00:40:59] A good question. Let's see if my understanding is correct. So get itself is just the language instead of processes, but what I know GitLab is just like and GitHub or just software kind of on top of that, but I'd love to hear from somebody who actually knows then or Kiko or Serge: [00:41:18] Yeah, I think GitLab is open source or something like that when GitHub is more governed. But I'm not too sure. Just want you to understand, but I think enterprise uses both. You can go, yeah, Mikiko: [00:41:34] Yeah, so so gets the command line utility, that's that, yeah, gets the command line utility that was partially developed by Linus being who is the father of Linux, you know, that was one of his great contributions. Github is the man. It's like the hosted managed version of it. And same with GitLab and a lot of these other sort of things like so the flavor [00:42:00] is, is the enterprise sort of offerings that they offer. Typically, why would one use one or the other? I'm not sure, to be honest. A lot of times it's just it's a question of essentially. Like, what integration does it have with the ecosystem of tools around it? A lot of times, I think if you're someone who is like looking to do your own like personal projects, GitHub is still the best way to go. Just for a number of reasons, like it's just the home of open source. If you're like an enterprise company or a startup, there might be other reasons why you would pick GitLab over GitHub. So, for example, in one star bios that we use GitLab instead of GitHub, and part of it was, I think it offered some better like security and also enterprise integrations. But yeah, and the reason why it's also, I think, good for people to get more practice with understanding like Git as a command line utility is because in many ways it Harpreet: [00:42:56] It helps you Mikiko: [00:42:57] Understand other command line utilities like the goods and the bads. And there are a lot of good ones and there are a lot of bad ones. Harpreet: [00:43:11] Regs that answer your question. All right, thank you. Yeah, let's go to our genius question. If anybody else has a question, please do let me know if the comment right here, the Channel A. Harpreet: [00:43:20] You or if Harpreet: [00:43:21] Anyone on LinkedIn YouTube Twitch asked lot questions. Let me know. Go for it. Serge: [00:43:27] Thanks. Harpreet: [00:43:28] So this is Serge: [00:43:29] A backing career corner kind of question. And so look forward to your thoughts and advice. So. So Bernie won some people around here who weren't. When I introduced myself previously, I'm Harpreet: [00:43:46] Let's say I'm mid-career. Serge: [00:43:48] People talk about dating themselves, Harpreet: [00:43:50] And I think maybe I'm Serge: [00:43:52] A tad bit older than some of the folks Harpreet: [00:43:55] Who were Serge: [00:43:57] Who are mentioning that. And so [00:44:00] career shift. I've done a fair amount of analytical work in the past. And then I decided I wanted to shift to Data science because the tools have become so robust and it's way, way beyond using Excel Harpreet: [00:44:17] And even some of the Serge: [00:44:18] Plug ins for Monte Carlo and things like that. And so now I finished my bootcamp, I've done some additional projects and so on, and I'm looking to get a job. One of the things that I've heard is that some people will say, Oh, you're an experienced professional. I've done consulting, I've managed teams, et cetera. You know, you could you could be a great fit for a like a data analytics or data science team. Now, obviously, it's going to depend on the place. Harpreet: [00:44:55] Google, probably not, Serge: [00:44:56] Obviously, because you're going to need someone to have so much experience in many other companies, though, it could be a good fit. You know, the tools you don't necessarily Harpreet: [00:45:08] Have to, you know, you're not Serge: [00:45:09] Going to be necessarily coding all day. But a key thing is being able to communicate Harpreet: [00:45:14] With Serge: [00:45:15] Managers and folks outside of that team, being able to kind of translate the technical aspects or the the data science tools, explaining those in a way Harpreet: [00:45:28] That non-technical Serge: [00:45:29] People can understand. And having worked in consulting and having worked with some pretty senior level people. There's I can tell you there, there are times when it's really helpful to have somebody who has that kind of experience. I would love to hear from this group what you all think, since so many of you are, are and have been data scientists and companies large and small. Harpreet: [00:45:54] And I Serge: [00:45:55] Really love to hear your thoughts on this idea Harpreet: [00:45:59] That, [00:46:00] oh, you could Serge: [00:46:01] Be great in this kind of a role, you know? Or is it a kind of thing like, Hey, Harpreet: [00:46:06] Man, you're starting from the bottom. This is just the Serge: [00:46:09] Way it is. Go get yourself an Harpreet: [00:46:10] Entry level position and you know Serge: [00:46:15] You're going to need to develop those skills more in a in whatever company you end up working for. I hope that that question is at least I hope that was clear. Happy to clarify if anyone would like me to. Harpreet: [00:46:34] Let's go to Greg says. Hang up. Anybody else would like to jump in. Please do. I think after Greg, let's go to Vin. And then if anybody else was jump in, maybe can or keycode love you guys. Serge: [00:46:44] I just have a quick thing for you to help you change your perspective. You mentioned Google. And you say Google, maybe not. You will be surprised how many teams inside of tech teams do not know anything about data analytics or stuck in excel doing the basics. So do not discount yourself, especially these big dogs that makes you scared. Find a team within these big teams. These teams are. These are conglomerates. They have so many departments. They are not necessarily as advanced as the whole organization. And they need people like you with deep experience in accounting who bring in a new analytics knowledge or experience to the team so you can help them leverage the tools that the organization has developed from the inside. So you bring your experience, you learn their tool, you help them adopt their own tools, they are struggling. You need to find these small teams, those isolated, siloed team, Harpreet: [00:47:45] To bring your experience to them. Do not be Serge: [00:47:47] Scared of the so-called tech team because you will be surprised how many of them Harpreet: [00:47:51] Are archaic, like working in Serge: [00:47:53] An archaic way. So change your perspective in that. Do some research find the teams [00:48:00] that connect with Harpreet: [00:48:01] The experience Serge: [00:48:02] That you've had and then bringing them another set of AIs because they need your help? So don't don't be afraid about the tech reviews the tech element. Just look for the departments that can find value in what you can bring. Thank you, Greg. That is Harpreet: [00:48:17] So that's so kind Serge: [00:48:18] Of you, and I'm such a great insight, and I just want to say also, I heard Greg, I heard you on the Super Data Science podcast, some what was it some months ago? And I was just like, Oh my god, I saw this guy in the happy hour. Like, Wow, such a such a fantastic story you have. Harpreet: [00:48:37] And yeah, so I really, Serge: [00:48:40] Really appreciate your thoughts, and I hadn't thought about it in that way. I'm not so intimidated by the Googles and all that my my degrees are from fancy schools, so I'm not worried too much about that. But at the same time, right, you know, Harpreet: [00:48:57] Because Serge: [00:48:58] They are always in the press about some amazing thing that they've done and they can recruit from pretty much everywhere. Harpreet: [00:49:05] It's like, you know, do I even try? Serge: [00:49:07] I don't know, but you've given me some another way of looking at it. So I thank you for that. Harpreet: [00:49:14] Absolutely. Craig, thank you very much. Greg was also on my podcast, which you can listen to and on Ken's podcast, I believe. Let's go to the van after this. And by the way, like, I mean, for the record, I'm thirty eight years old. I got my first job in Data science. I guess some actual data scientists when I was thirty five. Serge: [00:49:31] So yeah, you're not that old man. Harpreet: [00:49:39] I dye my beard. Trust me, there's a reason why I dye my beard and frequently get haircuts, because otherwise it's just gray everywhere. Serge: [00:49:47] Well, you know, better living through chemistry, right? We do. It can. Harpreet: [00:49:52] Here's of that. Then go go for it. Ken: [00:49:56] I'm going to kind of blow up a little inside secret about non tech [00:50:00] companies and senior leadership in non tech companies. And I'm going to use Eric because, you know, he's got the self-confidence that I can say this about him. Harpreet: [00:50:08] If somebody in the C-suite Ken: [00:50:11] Has Eric walk into a meeting and start talking about a project, there's just like this thing in the back of their mind where they're like, Nope, too young. And so there is ageism, and this is non tech company or non tech companies and tech companies, they're used to like 15 year olds walking in and, you know, coming up with great ideas. But in legacy business models, legacy companies, when they see somebody with experience with obvious capability that's come from an extensive body of work years, you know, decades, preferably there's a different level of trust. There's a different relationship that gets built. And you're going to have that example or are you going to have that, you know, that ability to walk into multiple rooms and be credible, Harpreet: [00:51:03] Which is Ken: [00:51:04] Huge and tech teams need that badly. They don't really know, for the most part, exactly how valuable someone with over 20 years of experience is Harpreet: [00:51:17] Until they have Ken: [00:51:18] That person in their team. Harpreet: [00:51:20] And it's, Ken: [00:51:21] You know, it's an evolution of trying to teach people that, you know, your tech career doesn't end at 10 years. Like, that's yes, you become a senior or whatever. And then it's like you fall off a cliff. That doesn't happen you. You actually get better and better and better. And typically, people in different careers transition into any type of engineering career that you can think of. And so what you're going to find your biggest value Harpreet: [00:51:46] Is that body Ken: [00:51:48] Of work that you've done and you're going to just have a story for every problem they run into. You're going to have a, well, we did this here. I saw that happen a few times. Here's what [00:52:00] we did to solve it. And every single time you do that, like you're worth, climbs tremendously. And so you're going to be an amazingly capable data scientist. You're going to bring, you know, a strong analytics background and strong analytics skill set. You're going to bring domain expertize, but you're also going to bring steadiness. And I think that's the underrated capability of Harpreet: [00:52:24] People as they get Ken: [00:52:25] Into their 30s, 40s, 50s and even 60s. There's a level of steadiness that just keeps coming in where you can take a team out of crisis mode in about two months just with your presence. Just by asking the questions that most people that are young are too scared, you know, to really break the well, we've got to get this done next week, and somebody with your capability comes in and goes, No, you don't. No, no, no. Trust me, I've done this before. No. So this is how it works. And that's the invaluable piece. And it's hard when you're interviewing to really explain to companies and to teams where you could be the oldest person on the team or there could be somebody, maybe two layers of leadership up who's the same age as Harpreet: [00:53:10] You are, but they've Ken: [00:53:11] Really never had the experience on their team and the benefits of it. And if you can in the interview, drop a few gems, drop a few of those stories. You know where they ask you about your experience, ask you about what you did before. Drop a gem or two, because that changes people's perception when they hear that in an interview and they go, Oh God, none of us knew that none of us would know how to handle that situation that way. Harpreet: [00:53:38] And you Ken: [00:53:39] Really do. You find that you you get a job for reasons you didn't think you'd get the job for? And they'll hire you for senior level positions, Harpreet: [00:53:48] Even though Ken: [00:53:49] From a capability standpoint, you may not feel like you're qualified for it. You really are because you bring so much more. And so look at, you know, yeah, you have maybe two years of [00:54:00] three years of, you know, four years of and they're looking for eight years of and it's like, Harpreet: [00:54:04] No, no, no, you will have all Ken: [00:54:05] Of this other stuff that comes with you. And it's so valuable. So go for higher step roles than you think you might be qualified for because you are qualified. You bring a lot of experience to the Harpreet: [00:54:18] Role and definitely don't Ken: [00:54:20] Underplay that. Don't feel like that's something you Harpreet: [00:54:23] Want to hide. Ken: [00:54:24] Obviously, on your resume, you can't put everything you want to on it or be 35 pages. But when you get to the point where you're talking to people, introduce it, bring it into the conversation, find ways to talk about the pretty awesome things you've done and the things that you've learned that they probably don't know. Serge: [00:54:41] Thank you so much, Ben. Harpreet: [00:54:42] That is gosh, that Serge: [00:54:44] Makes me feel so much better, I have to tell you. Yeah, I have a lot of thoughts on that. It's just, I guess the next step is how. How do you find those people, I mean, I guess through networking, it's just that I have trouble with that trying to figure out, you know? I mean, I look at job postings because I just kind of gives you a sense of the market. But obviously, we've all heard that, you know, whatever the state is, who knows, 70, 80 percent of Harpreet: [00:55:13] Jobs are not posted at all. Serge: [00:55:14] It's through relationships or the job is created for somebody essentially. So yeah, it's how do I find those folks? Like you said, once you get in and talk to them, that's, you know, that's where it can really happen. But you're looking Ken: [00:55:29] For companies that are struggling. You want to find some companies that are struggling because they're looking for different solutions. That's really what you want to see. Harpreet: [00:55:36] Is any company, Ken: [00:55:37] Any group, anything, you know, in that direction where you look at it and you go, Harpreet: [00:55:42] These people are struggling, they're having Ken: [00:55:44] Some challenges. Maybe they're having some churn because when you sit down with them, they're ready for something different. And so when you send in a resume, the person reading it all of a sudden is like, I think maybe it's time for somebody like this to come in and help us out. [00:56:00] And so any time that you have a different type of skill set or a sideways Harpreet: [00:56:04] Skill set, the easiest way Ken: [00:56:06] To find companies is to look for companies in trouble because they're they are suffering challenges, and if they've hurt enough, they're ready to do something else. They will not continue to push their head through the same wall that didn't that didn't move last time. And so you have greater opportunities, especially with your experience and problem solving history, to really connect with the hiring manager through your resume through the problems that you've solved that they're having right now. Serge: [00:56:35] That's fascinating, thank you so much in Harpreet: [00:56:38] That Serge: [00:56:39] I hadn't thought about that either, you know, in some ways I might be like, Oh, I want to stay away from a struggling company. You don't want to end up on a sinking ship, but gosh, that's a fascinating and a whole new way of looking at it. So thank you so much. Harpreet: [00:56:53] Let's go to a Ken and then Mark, and if anybody else has questions. Let me know what A.. Linkedin YouTube. Let me know Ken: [00:57:01] If we can. Yeah, I will say I Harpreet: [00:57:04] Agree with almost everything Ken: [00:57:06] Vince said there. I will say that Gina, I I kind of am inclined to agree with your sentiment there, but if a company is struggling, it might be because they're doing a lot of the same things over and over again and they can't learn from their mistakes. And so I mean, there are definitely companies that are struggling that want to go the other direction that want to change. But those changes usually come with a change in management. Right. So if you've seen that this company has Harpreet: [00:57:32] Recently hired a new Ken: [00:57:33] Person and they're building new infrastructure in there and they're they're like actively doing things to change how the direction they're going. Then I think that that's a really good sign if the company is just kind of spiraling. You're talking to people on the teams and they're using antiquated software. They're using x y z. Then we're in one of those positions, Harpreet: [00:57:54] Something I would ask you about. Ken: [00:57:56] This specific situation of finding these companies is [00:58:00] how would you approach that like a data scientist? Right. This is this is the domain you want to work in. This, to me, is a problem that can like, can be solved with analytics. And you know, the way I would solve that problem using analytics versus how you would solve that problem versus analytics versus how someone else in this group would is different. But applying like that mindset and philosophy, it's like a really great way to practice Harpreet: [00:58:24] And start applying your Ken: [00:58:26] All the skills that you've learned through the boot camp and these other Harpreet: [00:58:28] Things Ken: [00:58:30] To what Harpreet: [00:58:30] You're doing, you know, an Ken: [00:58:31] Example of like the interview process and doing that. Not necessarily exploratory. Like what you're what you're Harpreet: [00:58:36] Working on is one of my friends. Ken: [00:58:38] Every interview, every application he sent out, he tracked it, marked everything down. He did this over the course of three, three interview cycles, right? Harpreet: [00:58:47] So one was when he Ken: [00:58:48] Was entry Harpreet: [00:58:49] Level, one when he was Ken: [00:58:50] More senior and one when he was like a like a very senior data scientist now, right? And what he learned is that different approaches that those different stages had very different results. Right now, he can apply through job boards, job postings, wherever it is, and he has very good success early in his career as an entry level data scientist. Absolutely no success with that path, right? And you might find that with your specific situation, right, where you have really good experience from Harpreet: [00:59:19] Before, maybe you're a Ken: [00:59:20] Little newer to the pure technical data science stuff that a specific channel of reaching out to people Harpreet: [00:59:26] Is going to lead Ken: [00:59:27] To a higher percentage of like good opportunities that you find you might also want to think about. Ok, well, how do I find companies? How can I identify companies that fit a mold that I might be interested in that might start with, just like collecting companies that you're inspired by on a day to day basis? Right? It doesn't Harpreet: [00:59:44] Matter if they have postings, Ken: [00:59:45] Whatever it is, and then maybe it's a little difficult to scrape LinkedIn, Harpreet: [00:59:49] But like going on Ken: [00:59:50] Glassdoor, scraping a bunch of information on job postings or descriptions of Serge: [00:59:54] Companies and looking for keywords and and Ken: [00:59:57] Creating a topic model or, you Harpreet: [00:59:59] Know, like [01:00:00] that. That, to me, is Ken: [01:00:01] Something that is so overlooked when people are approaching this, this process, it's like we have these six tools, but we're approaching these same problems with are like human. I want to get a job perspective rather than are like the toolkit Harpreet: [01:00:18] That we used to analyze Ken: [01:00:19] Problems exactly like this. So I would say like, hey, like, you know, you've you've learned these great skills, try and apply them in this area. Worst case, you have like a really cool project that you can show these employers about how you found found out about them, right? Serge: [01:00:33] I love that. Thank you so much. That's so helpful. And then a couple of things you mentioned, and I will give props out to the book. Mixing was on, I think like a month ago or something, and I did end up buying his book and, you know, got immediate value out of it. So I was really pleased. And of course, he talks about really the outreach and, you know, obviously the low they are, there it is. Mark has it the ace, the data science base, the data science interview, I think, is what it was called. And yeah, and he talks about obviously the low hit rate that you're going to get most of the time applying for job board stuff unless, you know, that was a really great point you made. Can that yeah, once you're what you got all of that stuff that the the wish list or whatever it is that they put out there. Not everyone, but many. Once you have all of that, you're the purple squirrel is some people like to say, you're just kind of this other unicorn, then sure, you can apply online. But yeah, I really appreciate your thoughts on that. That's really helpful. Harpreet: [01:01:43] Thank you. Ken: [01:01:44] Can can comment a link, a blog post from my friend that I was describing about his process and a little bit here, if I can find it. Harpreet: [01:01:51] Yeah, absolutely. I'll make sure I'll post it on LinkedIn too. And then we'll the, uh, all the links in the show notes as well. Talking [01:02:00] about antiquated software if the company is still using. Microsoft Access Database then run the other way. Shout out to nixing, Nick, thank you for sending the book my way. I'm super excited to have you on the show. We'll be going live at some point in November. And keep an eye out for that. Then let's go to Mark and then Matt Blazer has a question. Greg, do you have a question to you, be to the queue or do you want to Harpreet: [01:02:27] Comment on this? No. Serge: [01:02:29] Something I wanted to say real quick to to Zena. But Mark, go ahead. Harpreet: [01:02:33] Yes, go mark. And then Greg. Serge: [01:02:36] Just quickly building on Ben's point about finding a company who is like struggling. I put a link in there for the stars model not to be confused with the star framework for interviewing, but the stars model is it basically breaks down various types of companies and where there are in current stages and what strategies you apply for it and how you kind of build them, buy in for those type of things, like using that when you kind of do like, understand your due diligence about company, you can look at that those models and see like, how do I frame my messaging to show the value I can bring to this company? So that's really helpful. And then going back to like a job posting, I actually do not like job postings. I don't apply to them because I feel like I'm just screaming into a void and nothing's really happening. From there, I I go by the mindset if like, I'm a company of one and I'm selling my services out to the Harpreet: [01:03:25] Market and specifically Serge: [01:03:27] Companies. And so essentially, I treat myself as if I'm a sales director, SDR, and I'm creating like pitches kind of cold calls in a way. And so what I do is I actually don't focus on the job postings. I focus on what companies are working on really cool things that align with my interests, understanding the companies and their pain points and how I can have value to them and then finding the decision maker on LinkedIn. So the hiring manager, the recruiter, whoever may be a true. I love to use Crunchbase, but that's because I [01:04:00] focused on startups. Harpreet: [01:04:01] If you're not interested in Serge: [01:04:01] Startups, that might be the best approach, but essentially that's that's what I do. And so I identify those key decision makers, especially if they're posting on LinkedIn, because then I get to read like, where are the pain? Points are talking about what are some things that are really interested in maybe even making a post about position of their hiring on? But essentially, I just reach out to and get this like to the decision makers. And I, you know, they have a job posting. I just say, like, Hey, nice mark, I give a quick pitch saying, like, here's my background, here's my skills. This is why I think aligns to your company really well. These are the problems I think you're working on with. I read based on your blogs, and this is where my skillset fits into that. Here's my resume attached. And then I don't even say, like, Do you want me? I say I would love to talk with you to learn more about the potential opportunities. And many times, like I've had had people say like, Oh yeah, like, we have a job posting out. Serge: [01:04:59] We're actually about to put out a couple of weeks like, let's talk. And like, there you go. I just got ahead of every single person that was just about to apply to the job online on the portal. And also the other thing, too, is that other times they're like, Wow, that's really great. We don't have a position, but we'll definitely keep you in mind. Boom, now I have a channel for a decision maker later on if they do have a position. So I was built up my network and because we're not LinkedIn now, I'm posting on LinkedIn constantly. I'm on their mind all the time. And so I've had people come back and be like, Hey, we have a position now. And so I just get out of the trap of the screaming into the void game and just go straight to the decision maker because I'm just going to spoon feed them all the amazing points of why they should hire me so they can repeat it back to their team and make their job as easy as possible because hiring is really hard. Harpreet: [01:05:49] I absolutely love that that's a great mental model. Think of yourself as a company of one, as an SDR for yourself, excellent process and magnificent use of persuasive [01:06:00] language like the way you frame Harpreet: [01:06:01] That at the end. Harpreet: [01:06:02] They're not projecting it on call, but when can we meet? I love that. Thank you very much. Let's go to Greg. And then after Greg, we are going to go to Matt Blaze's question, and if anybody else has a question, please do let me know Greg go for it. Serge: [01:06:15] Yeah, I wanted to add a little bit of on top of what everyone said is to not forget the your the power of storytelling, right? So to what Vin was saying too about your experience, you know, detailing how you would solve a problem based on your experience is one where you can add that that that that cherry on top is by saying something like, look, if I if if, for example, you were targeting Google, for example, you're targeting some some non tech position, you could say I've solved this business case using XYZ tools. However, with Google tools, I could use BigQuery and Data studio to kind of show the results to my stakeholders and things like that. So you showcase that you're able to understand not only your problem solving skills, but also you can relate to the tools that are used internally to solve these same issues. So that kind of build the bridge between your experience and help solve issues. And also you can relate or you have a high level understanding of the tools that they have on the inside to solve these, these these jobs. And you can do the same thing that little cherry on top doing an interview as well. So right now that you're not in, you can use the power of storytelling through your portfolio to have that story lined out. And also, you know, everything else everyone says about finding people who will look at that story, who will look at that portfolio, you just apply it randomly, selecting someone to take a look at your portfolio and then ask them for feedback or even, you know, connect you with someone who may be [01:08:00] hiring or just simply you looking at, you know, non tech positions that may need some analytical piece. There are a lot of program manager positions that require some sort of ability to perform some analysis. That's your way in. A lot of these companies do that are not like really older companies like legacy companies that I call them that are, like, kind Harpreet: [01:08:23] Of stuck in one way they promote Serge: [01:08:26] Like cross movement, right? So you can come in and as a program manager, you showcase analytical skills. And next thing you know, within a year, you transfer to an analytics position because you showcase that you're so good at it, right? So find an entry point and then move around, but be very selective on which company and look at the company culture and to determine how you can move around. Thank you, that's great. Thanks so much, Greg. I'd say, well, I will say thank you to Mark, even though he had to jump off, but these are fantastic points. I so appreciate it. Harpreet: [01:09:02] I agree, I think so much in. That was a lot of excellent advice. Have no fear it will be rereleased on Sunday, as well as a transcript so you can download a transcript. Take notes and and get some action steps in place. Let's go to a map. Last question can see you later. Thank you for swinging by. Go for it, Matt Bartlett. Serge: [01:09:21] Yeah, so I mean, I hear a lot on the show all the time about like how to apply for external jobs, like what we need to set up for the portfolio. I'm just curious about like what it takes for like more of an internal position. Harpreet: [01:09:33] So to give you a background, Serge: [01:09:35] Basically Harpreet: [01:09:36] At my company, Serge: [01:09:37] We're starting to look for more like for a few more data Harpreet: [01:09:41] Scientists. And I was always Serge: [01:09:42] Wondering, like for everyone's experience, what kind of steps do you Harpreet: [01:09:46] Guys take to Serge: [01:09:47] Apply for the positions? I mean, obviously know your manager and whatnot, but is it like a project portfolio or like, what do you usually do to help you increase your chances to get that kind of position Harpreet: [01:09:58] Or just a little bit more context [01:10:00] real quick? Matt, what's your current role at the company? Serge: [01:10:04] I'm currently governance analyst, but I am working with them on the Data, the current data scientist for with machine learning models to govern that and write out the documentation Harpreet: [01:10:16] That the data governance type of role then go for it like right off the bat, man, that sounds like a really good like Ml Ops kind of background to me, but I go for it in. Ken: [01:10:26] I think the the biggest thing to figure out is, do they have a process because a lot of places have, you know, a pretty well documented process for this is Harpreet: [01:10:35] How you get to this Ken: [01:10:37] Role that have a career path. They have, you know, some sort of transition plan. They have a training program. They have, you know, you talk to this person, express your interest, get your manager's approval. And so there's got to be some logistical process to go through. And that's usually where you want to start. Because if you start going outside of the process, you may find out that, yeah, you're qualified, but you didn't do Harpreet: [01:11:02] All the, you know, the hoop jumps that you needed Ken: [01:11:05] To. And so you're going to have to wait until next cycle or something like that. So, you know, step one, figure out what the process is and make sure that you go through something like that you've already got it sounds like an end because you're working with the team, you probably know the people that are going to be hiring you. And so step two for me really is if you've already got that network, just start asking them directly, you know, what would it take for me to transition into this role? Harpreet: [01:11:32] What do you Ken: [01:11:32] Want to see from me? Because I'm interested. I think I'm qualified, but you're obviously going to make the final judgment. So what do you want to see from me? Harpreet: [01:11:41] And then you Ken: [01:11:42] Have to listen really carefully to that response because it's either going, it's going to be, you know, between two sides of Harpreet: [01:11:48] The pendulum. One, I have Ken: [01:11:50] No idea, but they're going to say it using a whole lot of words that make it sound like they do. And on the other side, they're going to give you really specific advice. Where do this, this, this and this, and I'll hire you. And [01:12:00] if you have somebody who doesn't know what they need, it's going to be a whole lot harder. And you may even find yourself teaching them how to create a career path for other people to get into an internal promotion. Because some Data Data science teams have no idea how someone outside of their group would even get the capabilities to be part of their team. And the only way they ever think about growing the team is hiring externally. Harpreet: [01:12:27] And so you may Ken: [01:12:28] Be responsible for creating a little bit of this yourself, and you have to figure out how open they are to that. So, you know, when you go through an internal promotion, you really have to Harpreet: [01:12:39] You have to gauge culture Ken: [01:12:41] Almost as much as you do. What's it going to take, you know, for me to get hired? What capabilities do I need in order to do this job successfully? So don't forget both sides, you know, and ask, Don't don't be shy about it. Don't be coy. Just say, Look, I want the job. What do I got to do? Serge: [01:12:59] Ok, thank you. Yeah, they did mention they did mention a few requirements, which Harpreet: [01:13:04] Was come up with like Serge: [01:13:05] Some use cases, and then I think they also mentioned master's degree too, but I'm in the middle of it, so. Harpreet: [01:13:15] I feel like you might have some good tips here, just based on the previous advice you had given to. Serge: [01:13:21] You know. Yeah, yeah, I think I think Vin nailed it, right, so it's it's about figuring out one, Harpreet: [01:13:28] What is what is the job Serge: [01:13:30] Responsibility, what are the what are the tasks that you have to fulfill to excel at this position one and how is it being done today? What is the current process and then how can you marry it with what you've done right now in your current position, leveraging your own data, your own processes? Can you build the bridge and have a framework for that to to ask those questions when you're ready? If you're not familiar with the team and you're interested, then you read [01:14:00] the job position in the requirements and then you kind of, in your own words, kind of build a bridge in terms of what you've done in yourself and line them up like. So I've solved this, this, this, and I do believe that performing these tasks within my position is correlated to what you're looking for. Harpreet: [01:14:23] Can you tell me what are the gaps? Serge: [01:14:25] What am I missing here? At the end of the day, what you want to surface is what are the gaps right? And seems like you already have that. And then, you know, understand whether you need to know what to do to close these gaps, right? You've mentioned some of them, which is a degree which is kind of, you know, I don't know. It's it depends on the company. But as soon as you find what these gaps are, then you actively work to close them and you will find that most of the time in your current position, you will find an opportunity to perform a task or apply a process that is the same as the position that you were trying to go. A quick example I can give you right now I am a program manager like a risk manager. Right now, I'm trying to move to a product manager position, but I work like a product manager. Everything I do, the way I handle projects and the way I handle my my processes. I work with my stakeholders. I take user requirements, translate that to technical requirements, work with the tech folks and things like that. So not necessarily what a risk manager does, but you know, I try my best to build my experience so I can relate to what a product manager does or when I'm ready to move there. I can quickly show that Harpreet: [01:15:49] I've had Serge: [01:15:50] All of the tools, all of the processes. I've applied them all so I can showcase that I'm ready Harpreet: [01:15:56] To hop on to that Serge: [01:15:57] Next job. And you know, [01:16:00] at the end of the day, it's about making sure that you're you're, you know, checking all the main boxes and showcase that you can perform that next step or that next level or that next position to excel. Harpreet: [01:16:18] I think very much I'm wondering, is it ever OK to just like just email the hiring manager directly and I mean, try to just be like, Hey, look before you go and start interviewing other candidates. Let me just sell you on my skills. You all know how busy it it it gets around here. You know how expensive it is to hire some new people like just let me get a shot before you Harpreet: [01:16:38] Talk to someone else. Can you don't get Serge: [01:16:39] Don't get me wrong. Hiring managers love that because they're lazy. Harpreet: [01:16:43] They will. They love when Serge: [01:16:45] People are motivated to want the job motivated to learn and they know this, this percent that you have to fill. They're not looking for it. One hundred percent match for a position you're looking for. One, do you have the minimum that you can showcase that you've done? That correlates to the position that I'm trying to hire for. But also, do you have the will to learn new things when you get on board it, so people with motivation to reach out is good? And another thing you could do too, and it depends on from the company culture is a your job. What are the things? What are the discussions you can take with your manager to take on projects that can correlate or to solve projects in the same way of of the position you try to move to? Right. So that could be a little bit Harpreet: [01:17:30] Tricky because some Serge: [01:17:31] Managers might say, Well, why are you trying to do that or trying to leave me and say things like that? So you have to watch out for the culture and things like that? So certain cultures, they promote people moving Harpreet: [01:17:41] Around so you can be Serge: [01:17:42] Open with your manager and say, Look, I'm interested in this project. There's a value problem here, but this is how we'll solve it. I will solve it like a data scientist. Do you approve that? Yes or no? If yes, then good. Now you're solving a project as a data scientist that you're going to solve that project [01:18:00] and use that as a use case to hop on the position that you went for. Harpreet: [01:18:05] Greg, thank you so much. Go for it. Ken: [01:18:07] Yeah, there's a little secret that you can do with hiring managers. Every hiring manager has a latitude on what those requirements are. So you look at an HR job description and HR is saying these are the requirements, but most hiring managers can actually push back and say, You know what, if we retitle this, why don't we take away like that master's degree requirement? They can maybe change it Harpreet: [01:18:29] To Ken: [01:18:30] Master's or in progress. And so you would be able to be kind of sidestepping a couple of the requirements. And all you have to really do is ask the question directly, Hey, I understand you require a masters, but I'm almost there. I mean, am I close enough? Could we overlook that requirement? Can we retitle the position? Harpreet: [01:18:49] Can we? Ken: [01:18:50] Because there's a lot that you know that I've been able to do to sneak people in the side doors because they were just awesome and they were in the company. I can't explain to you how valuable that institutional knowledge is, where you have all the relationships, you know, all of the things that I'm going to have to spend six months teaching somebody anyway. And so you have the opportunity, you know, look at the requirements, definitely Harpreet: [01:19:16] Try to check as many Ken: [01:19:17] Boxes as possible. But also don't be afraid to say, Hey, can we get rid of this box? Because hiring managers in most Harpreet: [01:19:25] Companies can go back Ken: [01:19:26] To HR and say, Yeah, I understand this is the job description for this reason. But can I slip Harpreet: [01:19:32] In this little Ken: [01:19:33] Phrasing that we put in there for all time just to help this person get in the door? And typically it's a yes. Harpreet: [01:19:41] Ben, thank you very much, Matt. Go for it if you got any follow up questions. Serge: [01:19:45] No, I don't. That's interesting that you mentioned that, Ben, because Harpreet: [01:19:48] I come from like working over Serge: [01:19:49] In East Asia, where the the hiring Harpreet: [01:19:51] Requirement is like ironclad. Serge: [01:19:53] You cannot get past masters or you cannot get past the checkbox and you cannot question it. So I mean, culturally, it's like the [01:20:00] first time I'm actually hearing Harpreet: [01:20:01] That, like you can try and find a way to Serge: [01:20:04] Negotiate. So that's kind of a relief for me. Ken: [01:20:09] Yeah, yeah, it's different here because, well, in really large, high structure companies, there are some there are some process requirements, but it's typically not for the technology team. I mean, that's one of the nice things about the technology team is there's usually a whole lot more flexibility in job descriptions, job requirements and titling. That's the great thing about technology is you can retitle a position, you can bend a Harpreet: [01:20:38] Position, you can regroup a position Ken: [01:20:41] In some cases to, you know, trade with somebody else. Harpreet: [01:20:45] You can. There's so much Ken: [01:20:46] More flexibility in technology organizations because outside of like heavily regulated industries, we Harpreet: [01:20:53] Can mess around and HR is usually Ken: [01:20:56] On board with it because Harpreet: [01:20:58] There is Ken: [01:20:59] Such a range of capabilities that can be qualified for a job. And so you don't run into nearly as much of the discriminatory hiring practices of the regulated industry type requirements, and you don't have any sort of licensing. And so it's different here, and there's usually a lot more latitude than you think there is. Harpreet: [01:21:20] You go for it. Serge: [01:21:22] Yeah, thanks, Matthew, for for raising that, because one that's an important cultural understanding, I mean, it's just interesting from my perspective for one, but also I wanted to kind of add on to that what Vin was saying. It's it's it's been fascinating to me, having worked in government, public sector and private sector actually most recently, higher ed academic environment. Talk about iron clad. Talk about once a job description has [01:22:00] been put out there. I was in a university environment. It's not that it couldn't never be modified, but oh good lord, was it a challenge? And and also the attitude? You can't underestimate the culture. Honestly, it's so important because man, oh man, especially probably here in the U.S., there's such a broad range. So much diversity organizationally, culturally that, yeah, it's there is. I guess you could say there's always another way to quote the kingmaker in the matrix. You know, there are other Harpreet smiling. There really are other other avenues. And. But Matthew, it's you know, it's in a way it's not surprising Harpreet: [01:22:48] If you're Serge: [01:22:49] In a different country that people might be looking at you differently or at the requirements differently. But Matthew, are you? Harpreet: [01:22:57] I think you're in the U.S.. Serge: [01:22:59] Is that accurate or where are you at right now? Harpreet: [01:23:01] Yeah, I'm Serge: [01:23:02] Currently in the U.S. Harpreet: [01:23:03] It's just like when I did Serge: [01:23:04] My internships as a data analyst, like years ago, Harpreet: [01:23:08] It was over in Japan, Serge: [01:23:09] So they were very structured about what they wanted. You had to have boxes A, B and C checked, otherwise they would never even consider you. Yeah, and that's really important for you to call out because I think that. Um, we even if we know intellectually or relearn that things are different, on the one hand, it's exciting. I've been so thrilled as I've gone on my Data journey just to see that, you know, not every place is operating, certainly not not like the university, but even not like in many other jobs that I've had or organizations that I've had where they really think you need to have all these things. Sometimes you do. But a lot of times you don't. And at least in more technologically [01:24:00] Harpreet: [01:24:01] Oriented positions, Serge: [01:24:03] I think and I hope I know there's exceptions, but there is an acknowledgment has been said that you're going to have to you're going to have to get people up to speed and it's going to take time. Harpreet: [01:24:14] It just is. But, you know, it Serge: [01:24:17] Seems to me in a lot of organizations, they still have this notion these days Harpreet: [01:24:23] That people should Serge: [01:24:24] Be plug and play. And it's like, good luck with that because you're still going to Harpreet: [01:24:28] Have to educate people. Serge: [01:24:30] They're still going to have to get up to speed in the culture and the tools and the organizational dynamics is just the way it is. So I think, Matthew, it's great that you that you kind of Harpreet: [01:24:41] You brought that out because that's it's very important sometimes Serge: [01:24:46] For people to know where you are, where you've come from to kind of help guide your efforts going forward. Harpreet: [01:24:54] It is like a really interesting point, because like I get hit up a lot on LinkedIn for like career advice and stuff like that. And you know, obviously I made, you know, post about that as well. And. A lot of the people I get message from are like people in countries that are not North America. Like, for example, India and I always feel like I like the advice I might state on my personal advice might give. You might not apply that one because it is a different country, different culture, different language, different value system, different education system. So obviously, there are obvious exceptions. I don't know. Like, I feel like people. You come to me for this career advice. Linkedin come from different countries, and I don't think it necessarily apply. I'm just wondering if he has Harpreet: [01:25:42] A weird for feeling that way. Is that Harpreet: [01:25:45] Him? What are your thoughts? Ken: [01:25:49] No, it's it's funny, it's like there's Harpreet: [01:25:51] Something about you and me that Ken: [01:25:52] Makes people from other countries approach us. I don't I mean, I can't figure it out. You know, we don't. We don't. [01:26:00] I mean, we've got a couple. I mean, well, you know, some obvious things in common. But yeah, but it's yeah, it's interesting that I have to I have to preface career advice to a lot of people the same way where and I get a lot from Europe, where I'll say, look, Harpreet: [01:26:15] European market, Ken: [01:26:16] Totally different dynamics. And there Harpreet: [01:26:19] Are like three Ken: [01:26:20] Different Europe's when it comes to the marketplace there. And then there's England, which is totally, you know, you got another one for really. So it's it's really difficult to give generic advice Harpreet: [01:26:33] That will cross Ken: [01:26:35] Countries and cultures. And when you said Japan, it was like, Yeah, exactly. Yep, that not like a lot of it's unique in a lot of ways and Harpreet: [01:26:45] Structured in ways that are, Ken: [01:26:50] If you're not from there, it doesn't make sense. I think that's the best way to explain it. Like I grew up in Hawaii, so I kind of understand I got a lot of the cultural influence from Japan, so I kind of feel like I understand it a little better. Harpreet: [01:27:02] But there's there's so Ken: [01:27:03] Much diversity across Asia, where each country, you know, from country to country, what they're looking for, the number of Data scientists they want. You know what? They want data scientists to be doing the level of maturity in the companies themselves, across countries and industries. Harpreet: [01:27:22] There's so much diversity Ken: [01:27:23] That, yeah, no, I feel the same way. I get asked for career advice and I feel like I have to ask more questions that I'm answering in order to give a good answer. And sometimes for me, the best answer is I just I don't I don't know your market well enough. I'm sorry. Harpreet: [01:27:37] Yeah, yeah. Yes. Yes, them, some push back, some post, and I click and see what they're at like, OK, well, obviously you're from a different country like device. I give its kind of focus towards towards mostly North Americans because that's what we know best, right? It's, you know, things for for that then appreciate that does not look like there's any other questions. So a couple of quick shout outs this week. [01:28:00] I've got a few. I'm going live again multiple times. So tomorrow with the Data professor going live with Chanin, then is there like last minute opportunity for people to sign up for your course? Well, your course starts tomorrow, so if you're on LinkedIn, there's a few people still watching. Go to Garvin's course that this weekend. Ken: [01:28:19] Yeah, it is. But I mean, it's pretty much too late to sign so. All right. Thanks for the shout out. I appreciate it can be a great course. Harpreet: [01:28:25] Yeah, yeah, I've taken it. It is amazing. Keep an eye out for it next year when it comes back. But yeah, interviewing. Going live tomorrow, 10:00 a.m. Central Time, Saturday, October 23rd with the Data Professor Channon, if you miss it. Have no fear it'll be on YouTube and then dripped again later on the podcast many, many months from now. On Wednesday, I'm going live with Marcus De. He wrote the book The Creativity Code Art and Innovation in the Age of AI. He is a professor of mathematics at Oxford University. He's been on a number of various really cool documentaries in mostly math documentaries and things like that. We'll talk about that book and then his new book, Thinking Better The Art of the Shortcut, which is what kicked off the question earlier today about shortcuts. Art of shortcut in math and life. Then going live with Danny Ma on Thursday. Um, I believe like 4:00 p.m. Central Time and then also recording with Darlene and Lou on Wednesday, but do not think that we will be going live for that. And of course, going live on Wednesday for the comet officer. I hope you guys can join in there. You guys have had enough live harpreet this month. I think 15 times total. I've gone live this month. Top voice of LinkedIn. I'm the only one like actually really talking on LinkedIn like this now, you know, like you can actually hear my voice was the vote was the vote at LinkedIn. Holler at me, holler at me next [01:30:00] week. So this week released an episode. Emily Bell. Let's check that out. A lot of live streams happening this week. Tune into that next week. Got a episode releasing with Andy Hunt, who wrote the pragmatic programmer legendary guy. So look forward to sharing that with you all. Take care. Have a good rest of the weekend. We will be in touch. My friends. Remember you got one life on this planet. Why not try to do some big cheers, everyone?