Harpreet: [00:00:06] What's up, everybody, welcome. Welcome to the comet and open office hours powered by the @TheArtistsOfDataScience. I'm super excited to have all of you guys here. It is Sunday, June 11th. I can't believe that it's like already halfway through the year, man. It's that's been been going quick, man. I'm super excited to have all you guys here, whether you are watching on YouTube, on Twitch or live on LinkedIn. If you guys have any questions at all, go ahead and put them in the chat. Also, there is a link for you to join us right here in the living room. I would love to have all the guys here joining us. So let's let's kick things off. Man. I'm I'm wondering I was just thinking about this earlier while I was, you know, doing some dishes this morning, thinking about, you know, what do I find so interesting about Data science and about machine learning? And, you know, I figured this would be a great question to kind of kick the hour off with. So I want to know, you guys, what do you find so interesting about machine learning? What kind of drew you to this field? Let's go ahead and start with that with Cristoff and then we'll hear from from Maryanne Cristoff. What is it about machine learning that you find super interesting?

Christoff: [00:01:13] I knew you were going to pick me, but, uh, what I like is the and there is a lot of math in it because when I was in high school, I was pretty good at math and I never had the idea what I could do with it. And after I just learned how to program, it was like two years ago, I had no idea what machine learning meant. I knew there was such a thing, but I didn't know what it actually meant. And when I discovered it involves a lot of math and I was like programmer, but never using math is that's what actually happened to the truth machine. And so that's how I started it, because I [00:02:00] thought I could finally use this. I say ability to write nice awesomeness.

Harpreet: [00:02:05] That's cool. Let's hear from married man. What about you? What was so interesting about machine learning for you?

Meran: [00:02:13] I knew the topic matter. Well, there's not only that. Only six people, five exposes just extra. I'll go in there more generally. I think a lot of us boy here and basically engineers and scientists, they like solving puzzles, were curious in nature and want to find the answer. I mean, you ask a lot of questions and want to find answers and before our capital to be solving puzzles. But at some point, my puzzle that solving quality where they don't have any outcome, tangible outcome, and discovering that I'm going to search a little bit, not machine learning so much. And so let me just stop for me to achieve something, but say that the scientist you can ask a lot of questions and find answers to some things that are not obvious to people. And that's what drove me to the field. So solving puzzles in the first place, but puzzles that actually have some impact when you find the solution

Harpreet: [00:03:22] Actually love him. And there are some great reasons to get in the field. I love that nobody mentioned, you know, the fact that we just get paid good money. I love that you guys actually came through it with the real reasons that you're interested in it. For me, I just like doing hard things like this. To me, it's like it's it's difficult to do. But I enjoy doing it because it's fun and it's exciting and it's like like I just feel like that the return on learning just the acceleration curve just gets steeper and steeper with every everything that you that I do at least. So that's one of the main reasons why I love I love this field of data science. I love machine learning is super interesting. It's super fun. And, [00:04:00] um, being able to, you know, possibly predict future things, like to me that's like super exciting as well. But yeah, man, super excited to have all you guys here. If you guys have questions, everybody tuning in on LinkedIn, on YouTube, on Twitch, please do let me know what your questions are or better yet, join us right here in the room. I'd be happy to have you guys here shout out to everybody else in the room. I see where my my friends is here, Naqash. Naqash is the mastermind behind the editing and mixing of the @TheArtistsOfDataScience podcast, so thank you for hanging out, man. Appreciate everything that you do. So let's open it up for four questions. What's up to the Bharat? Actually, Bharat? We didn't get a chance to hear from you, my friend. So Bharat what is it about machine learning that you find so interesting that kind of drew you to the field?

Bharat: [00:04:45] Ok, first off, I'm still sort of transitioning to the machine learning field. I'm still working as a software engineer, so I'm getting there. And, uh, yeah, the nature is like I love my in back in my high school days since I was a kid. So pretty much what Austin said. About, you know, being in a place that I could actually use map and then ended up liking programing, too, so this seemed like a perfect combination of those things. And you even get to do a lot more exploratory stuff and you can get into something like predicting the future and having somebody take action based on the future you predict and also have a solid reasoning for how, how or why you predict the future. I mean, how cool is that in most other professions? Do you really get to do that?

Harpreet: [00:05:38] Yeah, and watch me, man. Awesome. Okay, well, let's open it up. Guys, let's take questions on any topic whatsoever. I mean, actually. Awesome. Like what what what draws you to to the field of machine learning? Because, I mean, you can you can be working for any type of industry, any type of tech company. But what is it about machine learning in particular that you find really interesting? [00:06:00]

Meran: [00:06:00] Yeah, there's what I'm realizing, I think is there's a few things like one is that especially in there's just a huge community focus. A lot of people are really, really dedicated to helping each other out and in ways and sort of like this, like rising tide lifts all boats sort of attitude. I mean, it's obviously very competitive. And like we're we're all out to, like, advance ourselves. But there's also this sort of like we do that by making it more explainable, making all this stuff more explainable, making it more transparent, as we can be creative about the way we think about use cases. And it's a field that is growing so fast. It's like there's all these kinds of like intersections with my sort of artistic past or my more creative past, like generation after generation, like all these kinds of things. I can find within it something that appeals to these different parts of me. Like I said, I'm not a data scientist. I'm not any codes or anything like that. But I think those kinds of things, the community part of it, there's this like there's room for creativity, generativity, and it's just evolving so fast. I think that's implicit in the study of the field itself.

Harpreet: [00:07:05] Yeah. Speaking of that, that creative like generative art part of it, I was reading my friend's book earlier this weekend, my good friend John Crohn. You guys might recognize John Crowne. He's the host of the Super Davison's podcast, Become Pretty Good Friends recently. But I was reading his his book. He's got some excellent chapters here. I was reading the chapter specifically on natural language processing because that's what I've been interested in recently. But Chapter three in this book talks about machine art. So machine art. And it's really, really interesting to talk about Gan's like, you know, and things like that. So Coard examples and everything. I thumbed through that chapter, but I was just like my mind was blown by by how cool the stuff is. So if you guys get an opportunity to check out John's Bookman, John is awesome. He's got an amazing series on YouTube as well. That kind of goes that in depth behind the math and everything. So I do see some questions [00:08:00] starting to roll in into the chat. I know that, Cristoff, you had your hand up prior to marriage, so we've got to stop them. We've got to marry and then we'll see any other questions that come in through LinkedIn or YouTube. I got my eyes peeled, guys. We got you taken care of. Go for it, Cristoff.

Christoff: [00:08:16] So my question isn't about Data science, so if so I can wait if that if other questions are more important now.

Harpreet: [00:08:24] Yeah, but definitely if you want to differ for sure, let's see what else going on. So Meran had a question here about Data position at he wants to know if this is for real. I don't know if I should pull that up and put Oracle on blast during comets office hours, but what was the question?

Meran: [00:08:41] You said that this morning a couple of days ago, that they had like sixty thousand positions just for Data scientists and they're not that well on their website. And then this morning, I did the same steps now Data. But it wasn't an all founded something. Is it possible? I mean, this is ridiculous to me.

Harpreet: [00:09:06] So I'm not sure what the question was. Is it possible that for there to be that much opportunity? Yeah, definitely. I think you're at your metered Marion

Meran: [00:09:15] To have that many openings and to increase the positions in a couple of days, actually.

Harpreet: [00:09:21] Yeah, yeah. That's interesting. We're talking about this on Friday during the @TheArtistsOfDataScience happy hour with had had a bunch of friends on Ken jee. Was there been, uh, Ben Taylor Jewry's. And then we're talking about is there dying or going anywhere? And no, it's not. It's just only going to increase more and more. Why? Because look at the world we live in. It's like the fourth industrial revolution, right where it's now more than ever. Data is being generated at speeds that were impossible, you know, before. We need people to go through that data and make sense of it, make meaning of it. You know, there's new products that can be developed, new things that could happen [00:10:00] with all this wonderful data that's being collected. So I don't think I don't think the science is going anywhere. I mean, and can a company have three thousand open positions? Yeah, definitely Oracle is friggin massive, but I definitely think it is absolutely possible, um, you know, for an organization that large to have open positions. Um, but yeah, I don't think their science is going anywhere. I only see, um, Data related roles just getting, uh, proliferating more and more. It might not be just the data scientist job title. I think you might start seeing more requisitions for Data engineers, machine learning operations engineer, um, data architects, machine learning architects, things like that. Just a set of skills where you need people to work with Data and build pipelines and systems that take data from the real world into some and use application and everything in the middle.

Harpreet: [00:10:53] Um, so definitely not going anywhere. Let's go ahead and let's take this question here from Sateesh. That's my friend. Go for it. And it was it was look like there's another Harpreet Sahota in the in this room ballons, not me. So Surtees go for it to teach you Armytage. So um, if you want to meet yourself, go for it. But it turned out that second Harpreet Sahota was actually Renata Sateesh. You are still on you. Go ahead. I'll read the question out from the teacher. Uh he's uh doing PGP and AML in Great Lakes still am CS internally not considering people who done with courses. Please suggest me how to get into a project when people expect real time experience. Um, uh, so I'm not sure how to answer the first part of the question. I'm not sure I really understand the first part of the question. So teach if you want to, uh uh. If you want to talk to us about that, please do. But I mean, in terms of suggest how to get into a project when people expect real time experience, like, I think when you see a job posting [00:12:00] and the job postings asking for experience, not necessarily work experience, you can get experience on your own. Right. Like you can build projects on your own. Right. And you just develop experience through that. So, I mean, what project should you do? I'm not sure. What are you interested in? Right. Maybe you are interested in music and maybe you have Spotify, maybe you use Spotify tremendously.

Harpreet: [00:12:22] So then maybe you can build a small project where you're pulling data from your your own listening Data from the Spotify API, doing some, you know, transformations with the putting it into some database, whether it's a cloud database, local database and then doing some type of analysis with it. Right. I mean, you're only limited by creativity, but I see to teach your unmetered, so I'll let you go for it. Still cannot hear anything from from Sateesh. Um, now is the teacher actually not audible? Um, so no go for it now. Maybe we can hear you try again. Yeah. Still nothing. All right, so figure out your audio situation will come right back to you. I see there's a lot of questions coming into the chat, but I guess how to get into a project when people expect real time experience. Real time experience does not mean work experience that could be experienced on your own. And it's just a matter of building a project that you find interesting that you would enjoy doing that end to end. Right. With respect to eminences, not considering people who done with courses. I mean, I've got comments on that. I don't know if I want to vocalize them here, but we'll talk about that later. Let's continue on with with Mary and second question, Mary, and go for it. And Satish, if you figure out your ideal situation to let us know, OK?

Meran: [00:13:42] My second question is, a couple of days ago, I guess it was the guy who says that this is start up and that's actually very common, some sort of unpaid internship of this fellow flameouts. I don't know how to look at it. The thought of [00:14:00] it as an opportunity to tag a company name that what is a company. But it is it felt like, let's say I'm transitioning from another field and thank goodness that are still learning. And is it helpful in terms of helping you to get a job in another company or that is a better way. And the other thing is sort of it looks it seems to me, as I would like to know, but it's different. It's almost like exploitation. People like me in the position that they need. The person they face to tough also mentioned that we get really stressed and I feel in the way that somebody is taking advantage of. American accent But is it helpful to get the unpaid internship at the startup so that you can stop a career? Yes. Yeah.

Harpreet: [00:14:59] So if you're a student, if you're in university yet, do unpaid internships if you need to. Obviously getting paid is better. So if I had the opportunity ahead of me in front of me to either get paid or not get paid, I'm always and pick the opportunity to get paid. And if getting paid means I keep my day job and work on something on my own on the side, then I'll do that. I wouldn't quit my job and go to an unpaid internship, I guess is what I'm saying. So at that second point about that exploitation part, I mean, I've got no comment on that. You know, that's just no comment on that whatsoever. But here's the thing. Guys like you can build a product you can gain. This is the only field where you can gain experience by not having a job. Right. To be an accountant, you have to actually work in an accounting firm. You have to work underneath an accountant to be a financial adviser. Same thing. You've got to work in a financial advising office. You got to do all that stuff. But we live in a world where if you want to break into this field, you can do it on your own by building a project [00:16:00] on your Data is everywhere. There's open Data portals, right? There's APIs where you can get Data for all of your wearable devices. It's up to you to leverage that, to build a project that can showcase your ability to do the job and then apply for as many jobs as possible.

Harpreet: [00:16:17] Share your work with as many people as possible. Um, I mean, not to just promote myself. I'm developing a course currently called How to Create a Project that will get you hired that will be out in a few months. But you can do it. You just have to have the skill and the discipline and the interest and creativity to make it happen. Right. This is the only field, I think, where you can get real world experience without having a job like, I don't know, many other fields outside of software engineering, outside of data science, outside of this it kind of realm where you can get experience like that. Um, so should you quit your job, take an unpaid internship? That choice is yours. Would I do it? Absolutely not. Um, I would rather just keep my full time job and work in the early mornings or late evenings whenever I have a couple of free hours on a side project just to develop my skill. Right. And then share that project with as many people as possible, talk about it as much as possible, write articles about it. Um, put it on your resume. Talk about it in interviews, things like that. I'm going to pause there and see if you have any questions or comments. And I'm monitoring all the channels right now. There's a lot of questions coming in, so I'll get to all of them. Go for it.

Meran: [00:17:28] Uh, just a quick follow up. I have a little bit of experience when applying for a job. Most of the companies put in the requirements of the experience in that industry environment, which means that it was somewhat of a surprise to scientists. And also. So in that sense, the internship gives you a well, it doesn't give the best for your work because they [00:18:00] built a company that does that.

Harpreet: [00:18:03] But, uh, yeah. So you could do this. How about round up four of your friends in a distributed environment? Right. And you guys work on a project together. That way you have to worry about version control. That way you have to worry about delegating tasks that we have to worry about managing people and dealing with deadlines and things like that. Right. Create the environment for yourself. You can do that in this field. Right. And any company that that says you need two years of actual work experience like and you're talking about this other experience, you've got to be like, oh, OK. Well, that's pretty much the same thing that we do here, except you're just doing it on the side for fun. Right? So if you're worried about real world work experience, get three or four of your friends together. You guys come together to work on a little project where you guys have to worry about version control. You have to worry about Data version where you have to worry about experiment management. Maybe it's a tool that come at Emelle or whatever. Right. And and make it happen. Um, somebody is asking a question here about business impact. Um. How do you pick projects such that you can showcase business impact when you don't really get access to real world Data is everywhere, my friend. Go to an open Data portal that is real world messe Data right in terms of business impact legman.

Harpreet: [00:19:14] Like if. I mean. If you obviously can be a little bit more difficult to show business impact if you're doing a personal project, but you can still frame the potential business impact of it, right? You can still talk about, oh, if this was deployed into the real world, here's the impact that it would have had. Here's what it would have done right. You could still think about and conceptualize, OK, if this was in the real world, here's what I think would happen. You could still think about and hypothesize what the business the impact is without actually having the business impact. Right. Because that's what actually matters. Right. Is the fact that you can actually think about, conceptualize, understand what the business impact would be of the work that you're doing. Right. Because you're not just like doing randomized search [00:20:00] just for the hell of it. Right. You're doing it so you can find a model that's going to move the needle on some other metric. Right. You have to tie your model metric to a business metric and you still think about conceptualize what that is going to be like. So all of these things you can you can watch what I'm looking for. You could simulate any project, any personal project. I'll stop ranting right now to see if there's any other questions being asked. Question go for it. I think Stree had a question so often. Go for it.

Meran: [00:20:26] Yeah, I just I wanted to say, following up on that point, that I think that sort of work of speculation or really thinking critically about business impacts, it's like something that you could build into your process of doing projects, regardless of what the answer is. It's sort of like the way you document your projects when you're writing. Either writing reports or something like that should always be like you think of this, like there's only a section of the thinking that's done and the crafting of the project that's done, even if it's like you pull out to potential use cases and you just sort of explore them a little bit, I think like that just making that part of the practice as opposed to attaching it on at the end is something not to do, just like make it part of your thought process in project building. I've seen that folks who do that tend to be more successful in tying all things together and making the project cohesive and thoughtful and just from the projects that I've been working on before.

Harpreet: [00:21:16] Yeah, absolutely. Absolutely. Thank you very much, Austin. I'm so glad you like to combine what some of us think about what I was saying and then think about the business aspect and document it and just showcase that you are thinking about it. You know, you can you can not only can you code and think about Data, but you can think about the real world impact implications. Um, sorry. Go for it.

Austin: [00:21:38] Yeah. So I think we when we talk about in this kind of related to the project subject as well, but when we talk about doing a project, there's often a lot of discussion about, especially if you're trying to talk about the project and or assume they're going to talk about what the business impact [00:22:00] was, what you hoped to achieve with the project. In that regard, I had a specific question because my interest is specifically in health care and I'm from a previous job. I have experience in healthcare and I wanted to leverage that experience and that knowledge and look for a project in the same domain. So I did find a Data set, I think, a year ago, and then I stopped working on it. Then I finished it recently and I found another data set, kind of unrelated, but in the same field. So now I have two projects in that same field that are completed and that mainly came from my interest in the field and kind of expand on my domain knowledge and just kind of use that and see how I can apply machine learning to that. How do you go about addressing the business impact when it's not a business driven project to begin with? The purpose of the project is more oriented towards oriented towards whether you can apply machine learning in diagnostics, for example. So how do you talk about something like that? And I presume where everything revolves around I drove X business, I drove like X business objectives and earned the company X dollars like navigate that field.

Harpreet: [00:23:20] I mean, who said everything has to revolve around that on a resume like where's that preconceived notion coming from? You could still use a star format. You can still do situation, task, action, result. Right. Result doesn't necessarily have to be a business result. If you're not working in a business setting, it could be a finding or a hypothesis. OK, because I did this. Here's the result that I think might occur. Right. You could still frame it that way on a resume. Right. And if it's a personal take home project. Right. Like like the end business impact, I don't think really matters. What matters is the way you think through everything you did. Right. From conceptualization of the problem statement to gathering data to [00:24:00] cleaning Data, to all the analysis in between to how your analysis inform your choices for the model that you're going to develop, how you picked models. And did you consider that the problems that might occur if you were to deploy this model? How are you going to correct for that? Right. It's you can still think about all that stuff. And speak to all that stuff, even though it didn't really drive business impact, right? Like is what I'm saying making sense, right? Like you just focus on the real world business impact. What about all the thought process that had to go through it? And if you can explain that and talk about that clearly in a interview, people can be like, oh, OK, well, shit, this kid knows what he's talking about.

Harpreet: [00:24:36] Cool, right? Because you can put business result on your resume and then come to a fucking interview and the like and not know what the fuck you're talking about and the decent guy. Holy shit, man, this guy's a joke. He's just making shit up and putting it on his resume. Right. Who do you want to be? Right. I rather picked the guy who you know, when I press him and talk to him about these type of questions in an interview, he's he's going to show me that. Yeah, I've thought about this. I've thought about that. And this is what I think might have happened, even though it wasn't really a business impact. Right. Because at the end of the day, you need to be able to think and communicate and show that you were thinking through this process from start to finish. Does that make sense? Yep. Thank you. Yeah. So, I mean, your projects, it sounds like it sounds like if you were in an interview, you'd crash it because you thought really deeply about the problems you you're working with and potential impact that could have even though it didn't really go out to the real world, you still thought about what could happen if this thing was actually out there in the real world, which is important. Right.

Austin: [00:25:34] Um, my problem has been trying to get past the PRISM stage, because if you're looking at a man, someone's going to glance at it for five seconds before throwing it in the trash.

Harpreet: [00:25:45] So let me write this out there. Once you apply for a job and submit your resume to you, just leave it at that. Are you and

Austin: [00:25:52] I try to find people on LinkedIn and try and business and that results are mixed.

Harpreet: [00:25:58] Yeah. So you've got to make sure you're targeting the [00:26:00] right people. Right. So go go to the company's LinkedIn profile, look for the technical recruiter, look for people who are higher up, not just like individual contributor, data scientist, but somebody who looks like they've got some clout. They've got some say in the decision making process, reach out to them, point them to a project and connect it like, oh, you guys in this company are doing this thing. Well, check this out. I've got a project that does this thing, which is very similar to the work you're doing or, you know, is related to it somehow. Check it out. And by the way, I applied for this job. My resume is in the system. If you get an opportunity to check it out, be happy to talk. Talk more with the right. Um, and then it's just a numbers game, like, you know, I mean, like it's like you need to have some expectations for every job that you apply for. Right. Like realistically like the moment you submit a resume to then reach out to somebody and try to get the process kicked off any given job you have, like less than a one percent chance of landing that job. Right. Realistically. Right. So what do you do? You make sure that you apply to as many jobs and you make sure that as you progress along the process for a job, that you're updating your probability of landing that job based on your experience in the interview. Right. And then, you know, for me personally, any interview I go to, even if I think I crushed it, I still give myself no more than a 15 percent chance that this thing is going to pan out and I'll get a job offer. Now, it's like, OK, cool. Well, if it didn't work out great, I assigned it this probability of not working out. So whatever. I'm just on to the next one. Right. It's just a matter of tempering your expectations and just making a numbers game, because that's what it is.

Austin: [00:27:29] It reminds me of a book. I don't know something about my thinking and that's

Harpreet: [00:27:35] Thinking the best. That said, that book shook me to my core. The book changed my life. You should listen to the interview I did with anything. It is on my podcast. I did an interview with the president's book. And he did. Yes, it is. OK, so check that out. But yeah, that book I take a look. I changed my life, shook me to my core. I changed the way I view the world because here I was a statistician, somebody who loved probability theory, just never having it explained [00:28:00] to me in that context. Oh, my God, that book changed my life. I highly recommend it and listen to the interview I did with it as well. Sounds good. Thank you. So, yeah, I mean, look, I mean, every time I hear people talking about the the struggles they have in the job search, I'm just like, you guys are just making excuses for yourself. Just just do it and keep doing it and keep doing it and keep doing it. Like like you have to improve and iterate through the job search process. And you do that by making sure you do mock interviews, make sure you're applying for jobs, make sure that your project looks good. Right. There's no entitlement just for submitting a resume. Like nobody's gonna call you back just because you submitted a résumé. You have to put in work to make it happen. Anything you want to have happen for you, you've got to put in the effort to make it happen. So a lot of the stuff I see with people like complaining to me about the job search process, I'm like, do you just need to get out of your own way? You have to write, um, sorry, hot take there, go for it. Teach you wanted to speak on something about something so

Meran: [00:28:53] Big on the Internet was not consulted on the last two years. I used to work and some good are doing company that would guarantee this would be a one to cover letters like the days to explain even these days, sometimes in Abbottabad downtown. And by the way, Nigeria like the. So I went through all these kind of things I know in the process, but there is no change to it this year. So what happens like when they get what they know, the entire process right from the beginning from portfolio level Data, LinkedIn, OK.

Harpreet: [00:29:34] Do you have proof like no one is one thing, but proof that you can do it is another thing. So do you have proof that you can do it?

Meran: [00:29:40] Definitely will do that.

Harpreet: [00:29:43] Until then, Frohlich, I can't open your skull and be like, oh, he knows all this stuff. Now, Amaney To show me, you need to show me through a project, right? Like if you're just like, oh, I took these courses, I did these certificates, why can't I get a job? Because nobody can open your head and verify that you know something. You need proof of work. [00:30:00]

Meran: [00:30:00] You need to. And that is one thing I need to focus on. Definitely. OK, that's not the best.

Harpreet: [00:30:06] Like the only thing you need to focus on if you want to get a job, that is the only thing you need to focus on is OK, I know all these things. Let me now put it to work in the real world. Let me make this happen. Right, and showcase it. So I started going

Meran: [00:30:20] Data faster than the first. And second thing is that within company, if we want to get it, let's say I'm working on Wyndham's, I want to go there to say, OK, you and I did some quarters, but they're expecting at least six months experience thought getting into some project would actually. That doesn't seem right. So we can get that exposure then if you want to apply out there, go for interviews and face that and continue to do this. And this definitely will get you can track it. But people that definitely lack with the knowledge that we don't want people, we need people to get the relevant experience. So that is what I mean. So many managers, these people in my neck would be because that really saying we don't want people like this, we need people development experience. So that is kind of a challenge we can see these days.

Harpreet: [00:31:10] Yeah. So where are you based out of where are you looking for jobs? Are you in India looking for jobs in India. Yeah, yeah. Dude, like I've know nothing about the Indian job market. Like I can't speak to it. Why? Because I mean, obviously I'm Indian, but I've never lived in India, never worked in India, never had to find a job in India. Like the advice I give is highly only relevant to the Western world because it's an entirely different value system. It's an entirely different culture. Not only that, like what's the population of India like one point, something billion. That means proportionally you've got like three times the number of applicants for any given role than somebody in the US would have. Right. So you got a lot more competition, man. Like shit's different over there. Right. And I can't really say with. So what they want in India, bro, like, I have no fucking clue, no clue whatsoever. I'm speaking only from a North American standpoint. Western [00:32:00] world standpoint. Right. OK, so I don't know, like I don't know what they want. India, like I think the job application for some of these things in India, I'm like, all right, well, you guys want you guys want Hercules. Uh, so

Meran: [00:32:10] I'm also having end up here in Brazil. So so my aim is for can only actually maybe be from Indian people, like maybe in the next three to six months. But my aim is first sergeant and as well. So before that, I guess what I'm suggesting is I would focus on building for the deficit Indian. And then another thing is, so like this year, India is it's quite difficult. Like people like they are not welcoming people like so many companies that asking and so recently I got an email from said for London, but that is putting experienced people online. So it is a mindset like people we can work with. I can say I can provide for them on that deployment. I can assure them that this is our what they need. But people when people are not ready, that is very difficult to get.

Harpreet: [00:32:56] Yeah, like I said, man, I can't really speak to what things are like in India just because, like, you literally have thirty times a competition as a U.S. candidate would have and you have three hundred times the competition that a Canadian would have just because the sheer volume of people who have live in the right and nowadays in India, it's like, OK, before it has to be lawyer, doctor, engineer. Now it's like doctor, engineer or Data scientist. So everybody's going to be there. Scientist like I don't know, like I don't know what the situation is like there. So you're going to have challenges to face there. And you know, whether my advice applies to you or not, I don't know. Clearly, it sounds like you have issues. So, I mean, trackpad Christiania, you might be able to help you out. So we'll just move

Meran: [00:33:37] Because experienced people like us, one, we have domain knowledge. We know how things will go right from start deployment, going for other technologies. But still this relevant experience, the only concern is what your experience in.

Harpreet: [00:33:52] Yeah, but like I said, um, relevant experience is also a personal project deployed, uh, that either is running locally [00:34:00] or in a cloud in some server. That stuff counts. Um, India, like, I don't know I don't know a relevant experience in India. Three go up. You got a question?

Austin: [00:34:09] Oh, yeah. I just wanted to I think you mentioned in one of your podcast that you're American, but you live in Canada. Yeah. Do you have you noticed a difference in the job market for Data signs in Canada versus America in terms of like what kind what expectations they have? Are there any differences to begin with?

Harpreet: [00:34:30] I don't think there's a difference in the job market, like in terms of expectations, what they're looking for. It's exactly the same. Um. Yeah. So I don't think there's really much of a difference. There's probably less people in Canada that are doing these type of roles. And that's, again, just like the entire population of Canada is like 30 something million, which is less than California. I think California itself has a greater population than all of Canada combined. Um, so there are a bunch of rules here in Canada and you'll see them open for months on end just because they can't get anybody to to to fill them. Um, yeah, I'd say the terms what they're looking for exactly the same.

Meran: [00:35:06] Ok, just curious. Yeah. Bullough. Yes. If you're in the states can apply for a job in Canada.

Harpreet: [00:35:14] Uh yeah. So the way things work in Canada is in order for you to get a job in Canada, in order for a Canadian company to offer a job to a non Canadian permanent resident or non Canadian citizen, that company has to get what's called a labor market opinion. Right. And because because I think it's the same thing in the U.S.. Right. Like you have a right or you have an obligation to to employ your own citizens and your own permanent residence before you look for out outside. People like you are obligated to do that as a country. Um, and so in order for a Canadian company to hire somebody from outside, they have to get this thing called a labor market opinion, saying, you know what, we can't find enough Canadians to fill this role. So that's why we have to go and look for somebody outside of Canada. And then from [00:36:00] there, it's like the, you know, wants to get that Lieberman's opinion done. It's like a process for for, you know, getting the visa set up and all that stuff. Um, yeah, hopefully I answered your question.

Meran: [00:36:10] Yes. I think it's much more difficult than all of the other obstacles. It big.

Harpreet: [00:36:15] Yeah. So, uh, when I came to Canada, it was I was able to come in what's called, um, well, there's this NAFTA agreement and there's a certain job functions that, uh, you can easily expedite your process of getting into to Canada. So for me, it was you know, I was a mathematician, statistician and actuary. That was the class of, uh, jobs that I fell into. Um, so that just made the process a little bit easier because I moved to Canada working as a statistician. But, you know, pharmaceutical company. Um, so. Yeah, but I mean, that might be changing now that we live in such a remote world, so. Yeah. All right. So shout out to everybody else drilling in ANWR admin, uh, hollering at the, uh, the the chat there and LinkedIn. What's going on. Good to see you again as a weekend.

Asha: [00:37:02] Not bad. Not bad at all. Oh yes.

Harpreet: [00:37:04] Good girl. It's been a long, long week man. Let me tell you, it's been a long week. A lot of a lot of ups and downs, I'll tell you that much. Um, yeah. I mean I get I get so worked up and and and excited when it comes to me telling people that you have to take shit into your own hands and make it happen. Uh, so I apologize if I come off, like, really just like aggressive about this, because I can't stand when people make excuses for themselves about things that they can make happen for themselves. So if anybody was out, like, really aggressive, it's because I'm aggressive about certain things in particular. Right. Um, but yeah. That being said, I was going to any questions, any comments you want to share with us?

Asha: [00:37:46] I think I've been better, but I just got in. Oh nice. Like I always miss the good discussions.

Harpreet: [00:37:52] It's always recorded and shared so. Yeah. Yeah, and I was just talking about, you know, how to how to get experience in that, you know, in [00:38:00] this field, you don't actually need a job to get real world experience because Data is everywhere. Data is the real world. And you can always create real projects. Right? Well, you should develop three projects, only three projects. Right. The one project should be a kind of a Data engineering etel type of project. And this project should be pulling data from, um, maybe an API. Right. And you get that data and it comes in as like adjacent blob. And then you add structure to that Jason blob and then you do some interesting, you know, interesting transformations and, you know, feature engineering, whatever. And then you dump that into a database, maybe a cloud database or a local database or whatever, and just automate that process. Right. That's one type of project. You need to showcase your data engineering skill set. Right. Obviously, Data engineering job is far more difficult than that. But at least as a data scientist, you should be able to pull data structure, data dump into a database and automate that process so that it happens, you know, automatically the next type of project you should do is just like an end to end project where maybe you're deploying a small Glascott, right. And end to end, meaning you start with raw data, go through the process, do a modeling bit and then serve it, whether it's locally or on a cloud somewhere.

Harpreet: [00:39:18] Um, and then third have projects should do is just one that really dives deep into the area that you're most interested in, sort of like your superpower. Right. And your superpower can be anything like if you are, for example, me, a statistician, or when I was transitioning into data science, I was mathematician, statistician. That was my superpower. So I did a project that was entirely focused on, um, statistics and machine learning, classical machine learning. But if your superpower is software engineering, well, then you can really double down on that in a project where superpower is, I don't know, product management. Maybe you could talk about how you can build a project to really, uh, framing the problem statement and business value [00:40:00] of your project. Right. So whatever your superpower is, whatever it is that you're most interesting, interested in, it's NLP to an NLP project, if it's computer vision, vision project and so forth. Right. So those three type of projects and like, that's really enough to showcase that you can do the job. Um, that is my firm belief and I'm sure many other people would agree with me. And if they don't, then that's OK. Awesome, so a shout out to everybody else in the room, so, hum, what's up? There's a question here on LinkedIn. Let me just go ahead and answer that. Question is how much big data and cloud computing play a role in data scientist job? It plays a big role for those jobs in which that is a requirement.

Harpreet: [00:40:40] Um, so, yes, I mean, you should have some knowledge of it, right? You should have that in your toolkit. More importantly, you should be able to it to learn that thing quickly as possible. I think that Kenji was talking about this on the happy hour of the day. More important than the actual discrete technical skill is the skill of learning, the skill of being able to pick something up. And if you need tips on learning how to learn, tune into the episode at Released on Friday with the one and only Dr. Barbara Oakley, who had a course called Learning How to Learn the Most Popular Course on Coursera. Um, like millions of people have taken that course. She wrote a book called The Mind for Numbers as well, had an opportunity to bring her on to the podcast. Then another episode. It was with Scott Young, author of Ultra Learning. So those two books combined will give you a solid framework on how to learn how to create small projects for yourself. Um, enough self promotion, everybody and everybody has questions. Go ahead and let me know. Um, scanning all of the streams, don't see any other questions. Maybe I missed them. But if anybody has a question right here, let me know. Cristoff, go for it.

Christoff: [00:41:46] Ok, so just like I said, my question isn't about Data science, but it's somehow connected. Uh, how pretty your creative person. I mean, you came up with ideas for podcasts for this, uh, mentoring session or [00:42:00] content creator awards. So I believe that you think, uh, creativity is a skill like something that you can, uh, improve. It's like a muscle you can work on. So my question is for you, but it's for everybody. How do you boost creativity? How do you come up with new ideas? Do you have any habits or routines?

Harpreet: [00:42:23] Uh, that's that's an interesting question, because the podcasts I'm releasing next Friday with the one and only James Altucher, the idea machine himself, uh, it's it's going to be kind of covering that. Right. So James Outhere does this thing he calls, uh, he has this thing he calls the idea muscle. And one of his practices is just writing 10 ideas a day every day, um, and just writing those ideas out. And, you know, they don't have to be good ideas that have to be bad ideas just as long as they're writing ideas out and they can be ideas on anything between ideas for, you know, ten ideas for my next LinkedIn post, ten ideas for a headline, ten ideas for what I want to eat for dinner or whatever. Just ten ideas. You just have to keep on flexing that idea. Muscle and really creativity is just combining things right, just combining different things together. Reading this book right here, The Creativity Leap by Natalie Nixon. She'll be coming on the podcast, um, soon. She sent me the book and everything, and I've got a copy of the book to give away as well. We get a booked up. Yeah. The Creativity Leap is a good book. It's just it's it's all about connecting ideas and connecting dots that on the surface don't look like they make sense or belong together. But that intersection becomes powerful. So I don't know if I answer your question or not. Um, it's really just thinking about, OK,

Christoff: [00:43:39] I was like just doing it like sitting like having some time every day or every week a blog just or sitting with a piece of paper or anything and writing down any ideas like you choose the topic you want to create ideas about, you just

Harpreet: [00:43:57] Do it. You need to create that that space. Right, [00:44:00] that space and time for yourself to do it. And for me, it's, you know, just consistently every morning coming down here in this room had this space. And I just show up here whether I want to or not. And I just start doing things. I start writing. Right. I got to write every morning. And out of that writing ideas come out. I'm like, OK, well, that's a quality. Let me try that. We try that. We're just being just aware and open minded enough to to really, um, see things, you know, in a different perspective. So if you want some good, really good tips on that, there's two episodes that I released that really, really touched deep on this. One of them was the interview I did with Christian Bush, who wrote The Serendipity mindset. Then the other one was, I forgot the name of that what I title that episode. But if you look it up, it'll it'll be there on my podcast. And then when I did with a near Bashan, we talked about creativity as a mindset. So if you definitely listen to those two interviews I did and you'll get a good a lot of good insight into how to be more creative and think creatively. But it is essentially all about connecting dots that maybe don't look like that, make sense, giving yourself the time and space to think creatively and just being completely open minded. Right. And just a lot of it is just not being afraid. Right. Like, I didn't care if people thought it was stupid, if I did like the People's Choice Awards for Data. Scientists like to do it anyways and reach out to Kate. And Kate might think it's stupid and I might have to do it on my own. But Kate thought it was awesome and she wanted to do it. You know, we're. Out, you know,

Christoff: [00:45:24] Ok, I think just one question, what kind of writing are you doing in the morning? Is it like journaling and when your ideas come up?

Harpreet: [00:45:34] Yeah, I do a few different things. So one of my big things is this right here on the artist's way to the Camron. So this is it's just it's just a bunch of blank pages, but it's three handwritten reform pages every single morning that I write. And it's just like a brain dump. That's the big one. And then I'll have just them. I had this little mini thing and if I'm on a walk, whatever this is in my pocket, I'll just write ideas down. Um, and [00:46:00] um. Yeah, this just right in the morning. It could be anything doesn't have to be structured, doesn't have to be formal. It's just whatever, just whatever's in your head just write it out. Right. And just let it flow. Austin, let's let's hear from you on this.

Meran: [00:46:13] Yeah. As soon as you said that I was thinking of this, if anybody listening to this podcast but Ezra Klein show he doesn't want to just like really cool interview this great interviewer, political guy who does a lot of different ones. And I don't know, Jeff Tweedy, the lead singer of Wilco, the band Wilco, and Jeff Tweedy was talking about how ill he does it with the words a lot, where he just really focuses on things like word couplings or phrases that really stick in his mind. And he takes those phrases and then he tries to pick a part. Why they're very interesting to him, why these phrases like and he just does this sort of mapping around that, from what I can tell, is like basically this phrase, what does it mean? It just goes on and writes about it and teases out the connection to those unexpected connection, because creativity is all about our previous things like association. Right. It's all about making these unlikely connections, whether it's between words or a problem in a solution, whatever it is, I think like building that part of your brain that's making associations, even if it's you're going on a walk and imagining what an animal is feeling or developing empathy like all these things to contribute to creativity because they're creating association between what you're experiencing and what you imagine. And I think that's like a super important part of the process, whether it's through words or problems you're solving in your life, whatever it is, I think that's really, really important.

Harpreet: [00:47:31] Yeah, absolutely. And there's some great tips there.

Christoff: [00:47:34] And just as I go that about about thinking what animals are thinking or feeling that I've never heard about it.

Harpreet: [00:47:42] And yeah, I think a lot of it is also the Goslin put it here in the chat. I really like this is just disconnecting process from the outcome. I think that is super, super important. Um, just yeah. You just do it. Who cares what the outcome is going to be as long as you're focused on the actual doing it, the actual [00:48:00] doing part of it. Right. Um, so there's some questions coming in here in the chat. It's funny, I think Kate joined in, uh, on LinkedIn right at the moment where I mentioned her name, like she just summoned and appeared. Kate, what's going on? A couple of questions here in. There's questions here in the room. There's a question on LinkedIn. Let's go to the questions here in the room from so so, um, so I can't join the video or Chadiha network issues, but he has a question about how important do I think a U.S. or Canada based Ms. Degree to become a machine learning engineer for me, software engineers or any other way to make it happen? Uh, I guess the question is, do I need to get a master's degree from the U.S. or Canada to become a machine learning engineer? Um, I don't think so. I think you can pick it up on the side and learn everything that you need to learn. Um, if you're going for a research type of role, if you want to work at it at a research organization there, you're going to definitely need to have more academic experience.

Harpreet: [00:49:01] Right. Um, just because researching is a skill, the ability to research is a skill, and it requires somebody to be very, you know, both driven and it requires somebody to be really disciplined and rigorous about how they go through something. And you don't get that in in undergraduate programs that much because you just kind of show up school, take exams and pass, whereas graduate programs like Master's and PhD, they're much more self led. Um, you have to do a lot of research on your own. It's more than just showing up and taking, you know, an exam. So if you want to be in a research type of position, I think a graduate degree is going to be extremely important. Um, is the it doesn't have to be from U.S. or Canada like I don't know. It sounds to me like you're, again, talking about India. Like I know nothing about the Indian job market. I know nothing about the job market outside of the Western world. It's not going to comment on India because I don't I know nothing about it. It is an entirely different beast. Um, again, [00:50:00] for the reasons that we discussed, different culture, different value system. And you literally have way more competition. So things are different. I should go for it.

Asha: [00:50:09] Now, my question is very different from this. So great to put. But something else. I have a question. Um, when you're learning something, you come across a hard topic. Sometimes you just you you get you're stuck there. You're stuck there for. And Data, how do you get past the how things how do you get back to normal learning when you get to that? But I guess that's what I wanted to ask, because I've been hitting a wall, and when you get frustrated, you just never see

Harpreet: [00:50:35] That's what you want to do. Yeah. If you get frustrated, yeah. Get up, walk away, take some time off and think about something else or do something else. That's kind of what I would do though, because you got to understand, like during that frustrating part, those parts that you're describing, that's actually where the learning actually happens. Right. Learning doesn't happen when it's easy. The learning happens in those difficult parts. So first thing is just don't put a time constraint on yourself. Right. I'm going to learn this thing and, you know, in three days. Otherwise, it's useless because it's just a process. Right. So just so this is kind of like my my learning plan for anything that I do. Right. So, for example, like I'm learning natural language processing right now. It's something that I've been really interested in because I'm sitting on a wealth of text Data from the podcast. Right. And my way of learning it is this. I'll spend a week just reading through books, reading articles, just kind of getting a putting the ideas in my head like, OK, this is what natural language processing looks like, right? This is what it's all about at a high level. Just understand it superficially and then I'll do a project. Right. And the project is just OK. How does this thing fit together? I don't care about the in-depth bits of it. I don't care about the details of PDF or word and beddings or word to vac or anything like that. Right now I just see how things fit together. Right. How do I go from an actual corpus of text to a, you know, actual model that can classify it? How do these fit pieces fit together? And then I'll do that part and [00:52:00] then I'll go back from the very beginning and say, OK, great, maybe I followed some tutorials and I've done some stuff and I've kind of patch something together.

Harpreet: [00:52:06] Just understand the workflow. Now let me go back and dig in on, OK, like what it was to IDF actually do. What are the different testified. How you know, how is how is my result going to change if I tried this type of victimization versus another type. Right. And assess the impact on that. And if I don't understand, like the nitty gritty details of the math, I'm just like, OK, as long as I understand the explanation of it. Right. Like, I don't need to understand the inner workings of it. I just need to understand the explanation for why this thing works. So for me, anything that I learn far more after the explanation, being able to grasp and understand the explanation for why it works rather than like the nitty gritty details of, you know, why is it that I have to transpose this and then multiply by the inverse and then why do I have to take the determined and then find the value of smashing Matrix? I don't understand. I really concern myself with that stuff unless I have to. As long as I understand the explanation, then I can dig deeper and deeper. Does that make sense? Yeah, yeah. Hopefully it makes sense. Also, what about you? What's the process like when learning again if anybody else wants to share? I'd love to hear from you guys.

Meran: [00:53:13] It's a good question. Yeah, I think, I think I take that that point when you're making Harp and then I sort of will try to find someone to talk with about it. I've always found that that's super helpful for me because one of the things that gets a blocker for me and this is maybe is more general, but it's like it's all in my own head or even all in my own writing. And I don't have anyone to talk to about it or talk with about a problem facing in that broad sort of sense, that connective sense. If I can find someone to sort of talk through things that I just discover so much more about, what I'm actually really thinking underneath the surface by like saying it out loud, hearing how someone responds, what questions they have, because a lot of times you don't know the questions to ask until someone else comes in, like ask you the question. It's like, oh, shit, that's the one that I need to actually spend more time thinking about or we'll work it out right [00:54:00] now. So I think having this collaborative mindset and I think that's also really valuable when you're building your sort of portfolio and your skin, your and your preparation for job interviews and things like that, is that sort of collaborative thing as well, where you're working on problems with other people. So I think that's that's what I would say. In addition to Harp revisiting, which I think is really good point,

Harpreet: [00:54:20] Cristoff, go for it.

Christoff: [00:54:21] And I also agree and I also I'd like to add to try to make some analogy to what you're learning with something you already know, because that's how you can and I mean you can get the idea of how it's working. And also, don't worry if you if it takes too long, because what's wrong with you don't know it because some things we learn are really, really difficult. And if you don't get it after two or three days, it happens. And I think we've all been through something like this. And so just don't pace yourself that after three days, you should have already understood it. And if you don't, it means that something wrong with you. It's not. It's just so difficult. And if you get a chance to try to talk about it, because it helps really. Because when you try to explain it, I mean, you could talk to someone who doesn't have any idea about it. And just try to explain how you understand it and you can come up with some explanation of things you didn't understand until that moment.

Harpreet: [00:55:32] Yeah, yeah. That's where all the learning happens by that that hard phase, that stuck phase. That's where and like the was saying, if you have to go back over it, you have to restart. Go for it. Right. Like it's completely OK. Like another example from my personal life. Right. Like professionally I was how am I doing NLP. But also like I just been really, really interested for some reason in like I got to think I'm crazy, but like quantum mechanics, many worlds theory, things like that. So I've been listening to [00:56:00] a book from David Deutsch, The Fabric of Reality. And I've had to listen to the first chapter like three times and like on like point eight speed because it's like crazy. My mind is being blown every minute I'm reading it. And so I supplement that reading with, OK, let me go watch, you know, interviews with him. Let me see what he's saying about it. Let me find people who have podcasts that are talking about his stuff. Like for example, like when I was reading Nassim Taleb stuff, um, I found some due to the podcast just broke down everything that Nassim Taleb was talking about, you know, easier, digestible kind of pieces. And, um. Yeah, and it's it's crazy. Like, I wrestle with those ideas. It's tough. It's challenging, but I don't really care about understanding the details of it. I just care more about the explanation. I can I understand the explanation. Right. Um, because that's what really I'm going for when I'm learning something is I just want understanding. You know, I'd rather understand, um, explanations and why things are happening then, like the actual nitty gritty of it.

Asha: [00:57:00] Coproducer I was going to add, you need a quantum mechanics due to a volunteer.

Harpreet: [00:57:05] Really, are you.

Asha: [00:57:06] That's that it was a few of the units. They did. Yeah. Nice.

Harpreet: [00:57:10] All right. Well, we're going to have to have some chats because this stuff is wild. It is insane. Uh, I was watching this documentary last night with it was it must have been like thirty years old, I think from 1992 as a BBC documentary with David Deutsch while he was writing his book, The Fabric of Reality. And he's talking about interference patterns and wave interference. And like, I was just like, oh, that is insane. But I will definitely check. Uh, are you familiar with David Deutche stuff? Have you read his book, The Fabric of Reality or Beginning of Infinity?

Asha: [00:57:43] No, but I sure I read what I had in terms of class notes because it was a few units. I did two of those units. Yeah. So I had to pass them.

Harpreet: [00:57:51] And stuff is so crazy and like, like so for me, like I was going to be difficult for me to understand quantum theory or quantum mechanics [00:58:00] from the physics point of view. But I like I can understand it from a probabilistic sense. Right. So understanding something from my frame of reference, that's kind of tangential to it, that is coming at it orthogonality perpendicularly. Right. Um, because because how because of how deeply I understand probability theory. It helps me understand quantum mechanics, the explanations of it a little bit more. Right. So that's another thing you can do in your learning when you're stuck on something is try to use what you already know and understand it from that perspective. That makes sense. All right, any other questions, there's a question here on LinkedIn. From Maneesh and, uh, have a fear of showing my skill. I'm learning a lot of stuff from the Internet, but not the right direction. Wanted to become a demand planner, an expert in times for forecasting any suggestion. Stop being scared to show your skill. That's one thing. And just do a lot of projects, man. Like that's how you get more confident. Like, that's gonna be my answer to everybody for everything. How do I get a job? And besides do a project, how do I get a job as a machine to a project.

Harpreet: [00:59:04] Why? Because the doing is where you learn, but learning happens across iterations. It happens across the many, many iterations that you do. The thing, um, you know, that's that's what it's like because a world very easily gives us the opportunity to do the same thing over and over and over and over again. If you were to if I was the, you know, got a corner grocery store here, if I was the owner of the corner grocery store and I had to use my time, would I just stock my shelves the way they are and just do that over and over? Or would I experiment what I maybe switch up the order of things that I stocked the shelves? Would I change up the signs that I have outside? I switch up the marketing. What I experiment with different pricing. Right. To see what works. Um, so the experimentation and the iterations, that's where the learning happens across that. Any other questions? I don't see anything else coming in through which nobody ever watches on twitch like one viewer and it's me doing my own thing. Uh, the people [01:00:00] watching on YouTube are silent and looks like LinkedIn has no questions. Last opportunity for questions from anybody here. Go for it. If you guys have a question, now's the time, my friends.

Asha: [01:00:09] Do you have to learn how to play the guitar? You said you were going to learn.

Harpreet: [01:00:12] It's still sitting there. I know. I have. I've not. I have not. No, no. Guilty as charged. Guilty as charged. Um, but I do like I well I kind of strum along on it. Like for example, when I'm frustrated or when I'm stuck, I just go to that thing and I'll play like I'll play like G to see to D like or some power chords or try to mimic some Green Day song. Uh so I'll do that. Um but yeah I mean it's mostly that I've got that thing there and I've got this right here that I. But these are things that I do and I'm stuck. So here's I got this like this Rubik's Cube that's like multidimensional Rubik's Cube and I'll try to play that they put it together or things like that. Or this is kind of like my go to and I'm stuck is to play around with this. Yeah. Because when you're stuck, you need that ability to let your mind wander if you only get into the neuroscience of it. Right. There are some I don't know much about neuroscience. If anybody is listening on LinkedIn and it's like this guy has no clue he's talking about this because I'm just regurgitating what I've learned from other people. Um, but like, there's, you know, this and Barbara Oakley talks about it in in our interview as well, like the diffuse mode of thinking. Right. So where you are not entirely focused on something, but it's your back of your mind, your subconscious is kind of wrestling it over while you're doing a different task. And then all of a sudden you'll notice the idea pop up or a breakthrough pop up and it starts connecting and making sense. So the more frustrated you are, the more you take that as an indication that you need to get up, walk away, do some different. All right. No other questions or comments or anything from anyone. Appreciate everybody joining in on LinkedIn. Everybody here in the room with us, Cristoff, go for it.

Christoff: [01:01:54] A quick question, because when you've got does it help when you you've got this [01:02:00] will be you, because I believe this diffused mode is supposed to let all the focus focus go. And when you do so, when you when you're something solving some kind of puzzles, you focus on them. It's like playing chess in a good way to go through it. Just mode, because your focus is focusing on playing chess. So I'm just curious if that's really helped.

Harpreet: [01:02:25] Yeah. I mean, it just helps to just stop thinking about the thing that you're thinking about. That's that's kind of like the goal of it. Right. So like this like I wouldn't say like I'm like fully focused on solving this. I'm just like kind of like I'm just thinking and, you know, working on it, um, not really focusing my attention entirely on this thing. Um, so that's kind of how I use it. Like, I'm not like intently focused on like I've had this for months and I still haven't been able to put it back together just because I'm like playing around with it, you know, being tactile and having fun. Awesome. So it looks like there are no other questions or comments. Thank you, everybody, for joining and appreciate you, uh, you know, being a lively discussion, like I get hyped up so so like I get crazy when it comes to getting shit, when it comes to, like, getting after what it is that you want to get done. And, you know, people make excuses for themselves. I will not tolerate it. I say just do it, make it happen. That being said, guys, take care. Remember, you've got one life on this planet. When I tried to something because everyone.