OH27-22-08-2021

Harpreet: [00:00:05] What's up, everybody? How's it going? Welcome to the comet, small office hours powered by the RC Data Science. It is Sunday, August 22nd. Super excited to have all the guys here. Hopefully AIs got a chance to tune into the episode that I released on the podcast just the other day with David Benjamin.

Harpreet: [00:00:21] We're talking about

Harpreet: [00:00:22] His book, Cracking Complexity. It's an amazing book. I really relied on the concepts of the book when I was going through some Data road map and Data strategy stuff, and my previous job found it to be immensely helpful and took a lot of those frameworks and started playing with them a little bit. So I think you guys will enjoy that episode as well. The book is actually free on

Harpreet: [00:00:41] Audible

Harpreet: [00:00:43] Premium or whatever they have, whatever it's called, Audible Prime. So you could definitely check that out there. Sorry about that, guys. So definitely check out that book on Audible. It's a great book. Definitely tune in to the episode I actually did with all mentioner earlier this week from Bright Data. It was it was a cool episode, man. I really enjoyed speaking with him, learning about his

Harpreet: [00:01:08] Company, learning about the DNA

Harpreet: [00:01:10] Of bright Data. So hopefully I'll get a chance to check that out. It'll be released on the podcast coming up this week. But it's also

Harpreet: [00:01:18] It's like on on on YouTube

Harpreet: [00:01:20] Already and LinkedIn as well. Yeah, man. Shout out to everybody in the room. Super excited to have all the friends here.

Harpreet: [00:01:27] What's going on, Meryn?

Harpreet: [00:01:28] Rodney Cristoff. I ask that if you're not speaking, keep yourself muted, please, because I do not need that background noise. So to Mark Rodney and everybody else in the room and some books out, have you guys here, if you guys got questions, go ahead and drop them into the chat, wherever it is that you are watching from. Whether it's YouTube or which or LinkedIn. I'll be taking questions from you guys. How's everybody doing, man? Hasbro's weekend going? Well, I'm excited to hear what you guys are up to. Mark [00:02:00] Latham, some exciting news I saw pop up on my feed the other day. Dude, that's so cool, man. Tell us a little bit about that opportunity that you got going on, Mark. And and then we'll jump into that question you got.

Mark: [00:02:10] Definitely. So I recently started my own company on the Mark Data LLC, essentially doing that to all all the kind of opportunities out for me from just being active on LinkedIn, really coming into play. So I do a lot of content creation. Data, Shiji consulting, speaking various things, and while my first clients, I'm partnering with Super Data Science to teach part of their course for their 10 week analytics course for the Python section, so we create like a python module teaching students. It's going to be really fun to super excited for the opportunity and to to really share my passion of Data with others.

Harpreet: [00:02:54] That's a cool opportunity. I'm excited for you. That's that's huge. I mean, Super Day of Science, that's one of the biggest brand names here in Data Science. So that's Kurtley an opportunity to work with them and impact. I don't know, I was ins of people to that program. And this can be huge.

Mark: [00:03:09] It's playout. It really scales up my ability to to reach students. I was so happy they reached out.

Harpreet: [00:03:14] Yeah, that's so cool, man. Shout out to a to one of my good friends from grad school. I see you watching it on LinkedIn Eric St. Louis Burnaman. It's been quite intense. I heard from you member spending many, many hours in the library studying for actuarial exams with you, hopefully doing well, man. But Marc Mauer, let's get into your question.

Mark: [00:03:35] Absolutely. So I think my career has definitely has phases of learning and application. So at one moment, I might be very Statts having another moment, maybe be very, you know, building product and python heavy, another moment, very like database heavy and business application. And oftentimes I feel myself where I'm learning a lot in that one space [00:04:00] and then we have to switch again. I feel like I forget a lot of things. So I'm switching back to like teach myself statistics and python again and apply that. And I feel super rusty because I've been so focused on business and databases for like six months. And so how do you balance retaining the things you learn is always just going to be like this resti phase and you're like, oh, I know where to go, search it up? Or is there a point where you're still proficient at it all and you just double down on certain areas? I guess to really summarize it is that I learn it, I apply it. I feel like it's not retained where I feel confident, where I can just go

Harpreet: [00:04:35] Jump right back

Mark: [00:04:36] Into it. I have some self learning again.

Harpreet: [00:04:38] Yeah. I mean, that's definitely something I struggle with as well. And one of the things I do is I always make time to go back and revisit fundamentals and revisit basics. But I like to do it in like the most entertaining way possible for me. So I've got a bunch of, I guess, manga and comic books that are all about the fundamentals. So I've got like the cartoon guide to statistics. I've got the manga guide to statistics, manga guide to regression analysis and so forth. Right. So I've got a bunch of these really entertaining books that I'll revisit. You know, they'll take maybe like two days to get there. And they're so fun to read. And they just kind of get those concepts. Keeps them on on top of the mind. And I try to do that, you know, at least once a month or so. And then I'm always watching on the on great courses, like, for example, like I'm watching this course on great courses is just all about geometry. It is just the fundamentals of geometry and then multivariate calculus just to revisit the stuff.

Harpreet: [00:05:31] And I feel like, as I like when I was first

Harpreet: [00:05:34] Exposed to some of that stuff, it just didn't make sense to me. There are just formulas, right? Like I literally was just rote memorization formulas and just doing the work by hand. But then as I progressed along my career, like, I feel like I can go back to it

Harpreet: [00:05:46] And I see some deeper connections,

Harpreet: [00:05:48] Some more intuition for it, with it with those topics, I always try to revisit the basics. I always like that for me. Like I don't I don't I'm definitely not one of those people that just has everything memorized off the top of my head. I always have to go back [00:06:00] and look stuff up, but at least I know a few good places where I can look. And then I also have a few words in my mind so that I know what to go and

Harpreet: [00:06:10] Search, if that makes sense. Cristoff, let's

Harpreet: [00:06:14] Go to you then after Christophers. I'd love to hear from Rodney on this, because Rodney, is that somebody who actually like knows everything off the top of his head? So.

Cristoff: [00:06:23] So currently I'm reading this book, Limitless from being quick, and he writes that if we fail to remember something, there are two main reasons. Either you didn't connect it with something that you you already know

Harpreet: [00:06:40] Or you just assume that you did, that this

Cristoff: [00:06:44] Information is important. So it's either or. And if I may suggest something, just grab this book and try to learn it, because I just started so I can tell you what are the best ways to remembering things. But I know by now that these are the main reasons why we forget things. And I believe that since I guess we all are like lifelong learners invested in this book, we pay off for years. Yeah. Me give you any advice

Harpreet: [00:07:19] Right now other than grab this book and

Cristoff: [00:07:22] Try to learn how to learn.

Harpreet: [00:07:26] It is it is a really, really good book. I got on my bookshelf there and a huge fan of Jim Quick. And just to your point. Right, like when you learn something before,

Harpreet: [00:07:33] It's because it's

Harpreet: [00:07:34] Just the formulas that might not be in context have nothing to connect it to. But then when you revisit it later in your career, you've got a little bit more experience than you can kind of go back and make those connections. And I feel that that helps reinforce that the principles that

Harpreet: [00:07:48] Postop was talking about, like connecting it to

Harpreet: [00:07:50] Something and then making it more important in your day to day kind of work. I'd love to hear from Rodney on this. I've also got questions coming in from LinkedIn. Shout out [00:08:00] to our thank you for joining us. I got a question from Vivec. We'll get to your question. Vivek and I got a question here in the chat from Perth as well. So we'll get to both the AIs questions. Stay tuned. Definitely feel free to join in into the Zoome room as well. I've got a link right there on the comment section of LinkedIn where you can join in a Brodney. Go for

Rodney: [00:08:19] It. I well, I'm I'm just drinking my morning coffee. And then I've been answering Ben's question on LinkedIn about if you were starting data science and machine learning from scratch. What should you read? So I've posted all of the old classics, you know, going

Harpreet: [00:08:37] Back 50,

Rodney: [00:08:38] 60 years.

Harpreet: [00:08:39] Yeah, that's funny. I did post something about that earlier today. Yeah, yeah. Yeah, I recommend it. I recommend this book just because it's so comprehensive and like, for example, like information theory, right? Like I've never been exposed to information through before. And there was something recently that I started

Harpreet: [00:08:57] Looking

Harpreet: [00:08:58] Into, and I didn't realize how much probability was involved in information theory. And that helped me forge kind of a new connection, like going back to Jim Quix principle of having stuff connected and just starting to get a deeper understanding of like, what does entropy actually represent?

Harpreet: [00:09:14] Yeah. Well, a lot of

Rodney: [00:09:15] Statistical estimates are based on entropy, right?

Harpreet: [00:09:18] Yeah. You know,

Rodney: [00:09:20] As well as things like information selection criteria. Yep.

Harpreet: [00:09:25] Yeah, I remember I remember really getting exposed to that back at UC Davis. I was then there was his name. Russa's George Russa's class or mathematical

Harpreet: [00:09:34] Statistics being exposed

Harpreet: [00:09:36] To like the fissure information criteria. I think it was. And yeah, like really like it made no sense to me. I didn't understand. It was sort of like, what is this information stuff? And then when I share information matrix.

Harpreet: [00:09:47] Yeah. Yeah.

Harpreet: [00:09:48] Yeah. And then, you know, going back to stuff and revisiting it, it just worked. Any connection to start?

Harpreet: [00:09:54] Yeah.

Harpreet: [00:09:55] Having a deeper intuition for it. But yeah, I'm a huge fan of always revisiting the basics.

Harpreet: [00:09:59] Yeah.

Harpreet: [00:10:00] Mark, [00:10:00] we pulled out a classic book, the John Tukey book there for regression analysis.

Rodney: [00:10:07] The exploratory data analysis

Harpreet: [00:10:08] Or Gijon was that that was

Mark: [00:10:11] The the green one data analysis and regression aside

Harpreet: [00:10:15] Six. Yeah. Yeah. Yeah. Anyway, I have

Rodney: [00:10:19] A have a look at that thread on LinkedIn been posted because I think that's going to generate some interesting stuff.

Harpreet: [00:10:26] So yeah, yeah,

Harpreet: [00:10:27] Yeah, yeah. They, I hope we mark that makes you feel better that

Harpreet: [00:10:30] Even, you know, even

Harpreet: [00:10:32] I, I'm not like the best data scientist or like, you know, stellar data scientist, but even I have to go back to the basics quite often. And I just try to make it fun, man. Like I try to make it as

Harpreet: [00:10:40] Fun as possible. Yeah.

Mark: [00:10:41] At the AFL stuff, I probably got some imposter syndrome going on when I revisit these things, so.

Harpreet: [00:10:46] Yeah. You know,

Mark: [00:10:47] It never goes away. It is. You get better at dealing with it.

Harpreet: [00:10:51] Yeah.

Harpreet: [00:10:52] I mean, I'd highly recommend like, hey, I got like 10 or 12 of these like cartoon. I got cartoon guide to economics, to physics, to the universe, to electronics, like all of the laska, all this stuff, just to

Harpreet: [00:11:04] Just to keep in

Harpreet: [00:11:05] Touch with those fundamentals and basics. I mean, I just wanna understand the world at the end of the day, like just understand explanation's that science has provided. And so I feel like just sampling from a little bit of everywhere and then trying to connect everything to what I already know is helpful. Cool. So let's let's first go to the question that I got from. Vivec here on LinkedIn, and then we'll get to Pirates' question then, if anybody else has questions, feel free to drop them into the chat or the comment section, wherever it is that you are at.

Harpreet: [00:11:34] Tamira on on LinkedIn

Harpreet: [00:11:36] Has a got

Harpreet: [00:11:37] A

Harpreet: [00:11:38] Suggestion here from Mark called a psycho by tone. Addicks on how mind works on a subconscious level. So that might be

Harpreet: [00:11:46] A good

Harpreet: [00:11:47] Resource for you. Looking to Eric on LinkedIn AIs asking names of the monga. So everything is just the monga guide. So I've got the Monga Guide to linear algebra, the Monga Guide to statistics, the Monga Guide to [00:12:00] regression analysis, the manga guide to calculus, the model guide to database's. Then I got cartoon guide to calculus, cartoon guide to statistics, cartoon guide to physics, cartoon guide to computer science. Those are all really fun books, really entertaining to read as well. So the question coming in from from Dubček here on LinkedIn is he has started to prepare for a by analyst type role. What would the skills help me cover the skills required for a business analytics?

Harpreet: [00:12:31] Your your.

Harpreet: [00:12:32] Okay, so I guess the question is you're trying to become a buy analyst and trying to figure out what skills you need to learn. And you're saying you're learning sequel, XL Pipeline and Tableau. I think you got all that covered.

Harpreet: [00:12:44] I think those are pretty much the

Harpreet: [00:12:45] Essentials for any

Harpreet: [00:12:47] Data related role.

Harpreet: [00:12:49] As a buy analyst, I'm not sure exactly what it is that you would be working

Harpreet: [00:12:53] On, but I'd

Harpreet: [00:12:54] Reckon it'd be heavy on the reporting aspect and the reporting side of things. So to that extent, I think Excel and SeQual probably going to be your best friends, maybe even Power BI. What do you think, Mark? What do you think?

Mark: [00:13:07] I was going to go into more like non obvious things that you probably can't get online, but like being able to communicate with business stakeholders and asking the right questions, figure out what exactly is the question they're trying to answer. So many times you have a business professional like, hey, create me this report for X, Y, Z, right. And if you take it at face value, many times you'll build a dashboard that's not useful. And so being able to ask the correct questions from the business stakeholder to determine like what's their real need, what's the business context around it that's going to set you up for success for after you get all those technical skills down. Right. That's going to set you up for success. Apply those toolbox

Harpreet: [00:13:48] Of of

Mark: [00:13:49] Technical stuff really well.

Harpreet: [00:13:51] Yeah, I have like an entire section in my upcoming course on how to ask questions and how to frame a question and how to generate hypotheses [00:14:00] and things like that, which I think will be helpful for anybody that's made analytical type of role. But they hope to have a mailing list.

Mark: [00:14:08] Do you have a mailing list for your your thing? Yeah, because I want to sign up on purchases and as possible.

Harpreet: [00:14:13] Yeah, I've got a I've got I've been slacking on the newsletter for for for my podcast just because I'm mentally on summer break do like I just like the last couple of weeks that I've not been at work. I just been

Harpreet: [00:14:25] Like like just relaxing

Harpreet: [00:14:27] And it's been great.

Mark: [00:14:28] Oh, no, no. Not not saying newsletter sign up like I get like an announcement like it's like now.

Harpreet: [00:14:34] Yeah. So that's that's going to be happening as well. That's part of the work that I need to get done on working on the website right now and and the landing page and mailing list. But it'll be fed into like everybody that's on the newsletter emailing list will get the announcement for for that as well. So both those will be will be out. So, Vivek, let us know if you any follow up questions. And, you know, by all means, man, you are more than welcome to hop into the chat here with us. I've put the link there in the comment section on LinkedIn. So if you want to ask more questions, I think that'll be helpful. But in general, man, those skills that you listed, sequal excel for by analysts or probably prioritize equal in Excel and then maybe Power BI, because I mean, not a lot of

Harpreet: [00:15:17] Companies might have

Harpreet: [00:15:18] Tableau, but I know a lot of companies will have Microsoft products and you might be

Harpreet: [00:15:24] Better off

Harpreet: [00:15:25] Learning.

Harpreet: [00:15:25] Harbi, I don't know any anybody has.

Harpreet: [00:15:27] Yeah, Rodney, go for it.

Rodney: [00:15:29] I read something recently

Harpreet: [00:15:30] That

Rodney: [00:15:31] Some people are shying away from Pabbi because it's not cross platform. So so one argument is that

Harpreet: [00:15:39] The for Tedlow is,

Rodney: [00:15:41] Is that it just cross platform, although it is a lot more expensive. So I'm not sure that's a choice. I think you sort of have to look at both start with one, obviously, but.

Harpreet: [00:15:57] Yeah, I

Harpreet: [00:15:58] Think I think also like when you're talking about [00:16:00] the tool Tableau or Pioppi, I typically using it for designing dashboards and communicating information visually. So beyond just the tool itself, there's principles to communicating data visually that I think are probably more important to prioritize in the learning journey than just the actual tool itself. Yeah, to that point, there's a really excellent course on Coursera taught by a couple of guys. A shout out to sick brother, a friend of a friend of mine who's one of the teachers in that course, and it's hot out of UC

Harpreet: [00:16:30] Davis, and I think it's it's all about

Harpreet: [00:16:32] Data visualization using Tableau, but they spend quite a significant portion on best practices and how to think about creating visualizations. I think that might be helpful for you as well. But, Vivek,

Rodney: [00:16:44] Another thing people shouldn't neglect is something like Data

Harpreet: [00:16:48] Studio, Google

Rodney: [00:16:49] Data Studio, which is in the same space. And so that's that's what we use at

Harpreet: [00:16:56] Work, mostly

Rodney: [00:16:58] Because of security issues of which which blocks certain functionality of how the AI and of Tableau.

Harpreet: [00:17:08] So, Mark, let's hear

Harpreet: [00:17:09] From you on this, because you started busting out laughing when it came to this Data studio. Go for it.

Mark: [00:17:15] We use Data studio in my job as well. I like it because it's easy to get your data and it's similar things, security, security reasons all within the Google cloud framework. I absolutely hate Data studio now. So much of it. Like there's some times where I'm like, I'm just going to create an hour because this visualizations too ridiculous. To give you an example, you can't label your X and Y labels can or you can only label your X1, but not your Y.

Harpreet: [00:17:44] And then they won't allow

Mark: [00:17:45] You to rotate text. And it's just it is, as this soldier was told, to make a dashboard in control by like through like the game of

Harpreet: [00:17:54] Telephone where like

Mark: [00:17:55] They got pieces of it. Right. But pieces of it really wrong. And it's [00:18:00] it's a living hell of mine. That's been my life for the past three months. I'm glad to be done with that project.

Rodney: [00:18:06] Yeah, but you got to work with, you know, what's going to work. And and the I.T. security that guys sometimes put constraints on you that then force you into those sorts of situations. I agree with

Harpreet: [00:18:20] You that, yeah,

Mark: [00:18:21] It's a great tool for what it is. But like I feel like if you know how to do do it in art and or do in Python, it feels like you're doing visualizations. You're one hand tied behind your back.

Harpreet: [00:18:33] That sounds very, very unfortunate and like I,

Harpreet: [00:18:36] I don't do a

Harpreet: [00:18:37] Lot of dash boarding and you know, I haven't done much dash putting my career at all like what I do visualizations is just for me to actually like get a sense of the Data. So I never think about having that flexibility in a

Harpreet: [00:18:49] Tool like like a tool

Harpreet: [00:18:50] For visualization that does not have those flexibilities. I feel like would literally feel like we're trying to do visualizations with Galileo, said Mahabharat back. That's that's quite unfortunate that I would not have an.

Mark: [00:19:02] Yeah. Also, another thing I learned, too, is if you transfer ownership to of a dashboard to another person, when someone leaves, all your dashboards will then fail. And you have to go through every single individual dashboard and update the credentials manually. Also part of my life the past few months. Yeah.

Harpreet: [00:19:21] Yeah. That's that's quite, quite unfortunate, man. So Vivec, hopefully I hopefully we've provided you enough insight and reasons to not use Data studio as well. Yeah. Mark mentioned Danny Moore's sequel, of course, is great. Yeah, definitely. Check that out. I haven't got a chance to check that out, but I heard it is free and quite good. Right. Is that free one or is that there's a paid version of that one?

Mark: [00:19:43] It's like 40 bucks by. It's really good. And my colleagues and I, we just all bought it individually. And we spend an hour each week doing it together and we really like it.

Harpreet: [00:19:53] That's awesome. That's awesome. So definitely check that one out as well, Vivec. All right. Cool. So let us know if we got any follow up questions. Shout out to everybody [00:20:00] else. Got quite a big audience watching AIs LinkedIn. So if you guys want to join in on LinkedIn, scroll up. You'll see a link to

Harpreet: [00:20:06] The zoom room. Come and join

Harpreet: [00:20:08] Us. Shout out to a few people who just joined us recently. Chris, good to see you again, my friend. Natasha, good to have you here. I think we've met. So thank you for joining in. Joshua, how curious if the same Joshua that hangs out on Fridays off so good.

Harpreet: [00:20:21] See, again, my friend,

Harpreet: [00:20:22] Let's go into pirate's question. But go for it, Heidi.

Harpreet: [00:20:29] So this

Mark: [00:20:30] Question is very specific, and it may deviate

Harpreet: [00:20:33] From the overall topic of machine

Cristoff: [00:20:35] Learning

Harpreet: [00:20:36] Itself. But still, I

Cristoff: [00:20:37] Would like the future to go for it as if anyone has worked

Harpreet: [00:20:42] With RSS

Cristoff: [00:20:43] Feeds. And Python here are just like work programs with RSS feeds programmatically in general. Then the question is like, how do you

Harpreet: [00:20:55] Put all four of Data changes

Mark: [00:20:57] On the RSS feed, like via a program or via some script

Harpreet: [00:21:03] Are called.

Harpreet: [00:21:06] Yeah, that's a good question. I don't have an answer to the top of my head, but one thing I did find I a quick Google search was the Feed Pastor library, which looks like it's quite up to date. Data have a recent release, the most recent release, version six in June. Twenty twenty one. So that's feed pastor. Have you looked into that library at all? I don't know much about.

Mark: [00:21:27] I was just getting started with that Mortlake. If anyone has done something which is, you know, battle tested,

Harpreet: [00:21:35] Then I, I mean, I

Harpreet: [00:21:37] Was just

Harpreet: [00:21:37] Looking for some

Harpreet: [00:21:39] Snake that. Yes, if anybody has any expertize doing that, anybody in the room worked with Python for RSS feeds.

Mark: [00:21:44] Well, I have. But I found that tutorials do a quick Google search. I also mentioned the parser and they gave you some simple code to to follow along with. Yeah, it sounds like your your best bet is just don't try to find the best thing. Just try to find something that the tutorial [00:22:00] and just try it out and learn every meet their needs. Awesome. If not, then you can find something else. But like it's helpful to go through

Harpreet: [00:22:07] That pain point. Yeah. I was trying to

Harpreet: [00:22:09] Mess around and find some other people calling what works. So let's see what happens. If we do file type python notebook, nothing comes up.

Harpreet: [00:22:17] Uh, Python, maybe pewee then. I mean, just Python

Rodney: [00:22:25] Rss brings up a whole lot more

Harpreet: [00:22:27] Python RSS.

Rodney: [00:22:28] Yeah, you get there's a there's a number of the python packages that will read in RSS feeds and there are a number of tutorials. Okay.

Harpreet: [00:22:39] Awesome. Well, yeah, definitely put some of those links in the chain separate. Hopefully that would be helpful. So by my Murman. Sorry we couldn't help you out more than that, but looks like we've got one library and then a couple of tutorials that will be coming into chat for you to check out our pre.

Mark: [00:22:56] I think some might be really helpful as you broke down how you did that Google search, because that was like some cool Google searching exercise stuff there. Yeah, that would be helpful for people.

Harpreet: [00:23:05] Yeah. So I just I get really proficient with my Google searches. So I mean, just searching, being able to search I think is a very important skill as a data scientist, that resourcefulness. Right. So if you're not sure how to use Google's advanced search. Uh, let me just pull this up real quick. This is a theeye search so much that Google always thinks I'm a bot. I don't understand why I always get that message several times a day. But if you go to Google dot com or whatever slash advanced search, this is a template that you can use to fill out. Um, but then once you start getting used to using this template, you'll know exactly how to search stuff up yourself. One thing that you could do is you can restrict your file type that you're searching for on Google. Right. So for example, let's say that I'm interested in,

Harpreet: [00:23:54] Um, let's

Harpreet: [00:23:55] Say I'm interested in generative ad for burial. [00:24:00] I can't spell, but Google will correct that. And then let's say I was looking at that for a

Harpreet: [00:24:06] Generative

Harpreet: [00:24:07] Music or, uh, music. Right. In general. And then I wanted to see if there's any white papers on that. So I restrict myself to PDF because that's typically the format of white papers. And I see okay, there's some symbolic music generation with Transformer Gans and then, you know, look through this. That's this half what I want. And it's usually helpful if you have on the top of made a couple of keywords that you're looking for in particular, let's say that I'm looking for

Harpreet: [00:24:33] Recommend recommenders

Harpreet: [00:24:35] Or something. I'm horrible at building up menders. This one doesn't mention it. All right. So that that sucks. But one thing I would recommend when you're looking at a research paper is once you've gone through the research paper and read it, like go through the references and check out some of the references that they have, because that's that will help you link to other important concepts or other important ideas that you might be searching for. So, for example, we're talking about, okay, general generative adversarial networks to generate music. Now, what's the first, Kirsan? I wonder if anybody's done like a project on that. Then I can restrict myself to search the Internet for I python notebook, I think file type colon opei notebook and just kind of see what happens. You know, we could, but that's probably too specific of a search. So I've got to find something else. So here's something. Somebody has a project on generative music. Let me see what this is all about. Right. So that's kind of how my Google searching process works. And it's all just based on Google advanced search. So let's say, for example, I was curious

Harpreet: [00:25:38] About something

Harpreet: [00:25:39] More basic linear regression. Right. And I don't want to,

Harpreet: [00:25:44] You know, have

Harpreet: [00:25:46] Just a medium blog post because I don't trust people on medium

Harpreet: [00:25:48] When they write about the

Harpreet: [00:25:49] Fundamentals. I want to restrict myself to just university websites so I can put say, you know, like just search only educational websites, like university websites and [00:26:00] then, you know, kind of

Harpreet: [00:26:01] Go through them and

Harpreet: [00:26:02] See what happens here. So that's kind of my process. For Google searching, so start by going into Google dot com or slash advanced search,

Harpreet: [00:26:13] And then you can once

Harpreet: [00:26:14] You start understanding how to do the searches out of here, you can just do them yourself straight out of the search bar. Radhi says someone should offer a course on Google search. Yeah, I will be actually including that as a bonus in my my upcoming course. I'll be having a quick, more structured lesson on how to Google search more efficiently and effectively.

Harpreet: [00:26:36] It's been a life

Harpreet: [00:26:36] Changer or a game changer for for me and how quickly I can find information. Got out to a.. Just joining us A.. Good to see you, my friend. Fowler says I'll see if there's any other questions coming in on LinkedIn or anywhere else. If you guys have questions, do let me know. Looks like Asha has just joined us. Isha, good to see you again.

Harpreet: [00:26:55] How are you doing?

Asha: [00:26:58] Oh, did I say that?

Harpreet: [00:26:59] How you doing? Yeah. Yeah. No dogood. Yeah. Thank you. Thank you. Good to have you here. Yeah, we just were just talking about how to Google search efficiently and how to look stuff up. Um, there's actually a question that came up from a remark earlier today. I think I'd love to get your perspective on this issue. And it has to do with essentially sometimes we forget things. How do you handle that? How do you handle having to go back to basics and

Harpreet: [00:27:24] Having to keep information

Harpreet: [00:27:26] Fresh in your mind? Like what do you do to to make that easier on yourself?

Asha: [00:27:30] The forgetting, you can't help it. Some things just tend to go. But one thing I've learned is I tend to forget less when I focus on exactly what I'm reading. I used to have a habit of just reading so many things that you don't even use them. So you keep practicing. You keep going back to it.

Harpreet: [00:27:45] That's how I don't forget. You definitely keep

Asha: [00:27:47] Practicing

Harpreet: [00:27:48] On it. Yeah.

Harpreet: [00:27:49] One thing I've been really like trying to make the most of is a better note taking system, because like you're saying, like I'll just read stuff. And my previous methodology was just reading [00:28:00] and just putting flags in the notebook or rather in my actual book. But I'm getting a little bit more smart on how to take notes because of this wonderful book, Smart Notes. That's been helpful. But I've been really using this program called Obsidian lately. It's quite nice. Just a text editor with a couple of bells and whistles on there. But it makes it easy to do bidirectional linking so you can connect ideas and use tags to them thematically sought ideas, and I highly recommend checking it out. Great number of great tutorials on Obsidian as well. One is a YouTube channel called Linking Your Thinking, and another one is Brian Jenckes. He does an entire in depth recording on how to use Obsidian. All right. The minute he has got questions or anything, I'm I'm keeping an eye out on the chat on LinkedIn cup of comments coming up. Mark, looks like Tamra

Harpreet: [00:28:51] Says they want to connect

Harpreet: [00:28:53] With you because they want to use your expertize on quantum

Harpreet: [00:28:56] Mechanics. Data dashboard.

Harpreet: [00:28:57] All right. That's interesting. So you can see the comments there on LinkedIn to reach out to to Tamara.

Harpreet: [00:29:05] And Vivec

Harpreet: [00:29:05] Said he's

Harpreet: [00:29:06] Taken up Osy

Harpreet: [00:29:08] Portillo's school course already. Purtill is cool, man. I remember using a lot of his stuff when I was first getting into Data since he was like one of the first people that I started learning from on Udemy. Cristoff, go for it.

Cristoff: [00:29:22] I have a question I want to know how important

Harpreet: [00:29:28] A bilker is

Cristoff: [00:29:29] In job of machine learning engineer or maybe whose job it is to

Harpreet: [00:29:36] Deploy and

Cristoff: [00:29:37] Maintain machine at any moment.

Harpreet: [00:29:40] Yes, I think for machine learning engineer, it's

Harpreet: [00:29:42] Crucial to know, doctor.

Harpreet: [00:29:45] I mean, even as data scientists, just knowing the fundamentals, high level overview of doctor is is is good to know just what it is, what purpose it serves and why you need it. But the actual deployment part, I would say, that falls on the shoulders of a machine [00:30:00] learning engineer or machine learning architect to help plug that into the overall system. What's the second part of your question?

Cristoff: [00:30:09] So he was deploying the models.

Harpreet: [00:30:10] That was Japaridze, or the first question was how important

Cristoff: [00:30:15] Docker is in terms of machine learning engineer. And the second one was whose job it is to deploy it.

Harpreet: [00:30:21] Yeah.

Harpreet: [00:30:21] So so definitely very important to know Docker for machine learning engineers. And I would say the job of deployment does fall on the shoulders of machine learning, engineer, machine learning architect or software engineer, even though a data scientist should write their code in such a way that it's deployable. Right. Deployment, ready. So just having machine learning engineer and notebook is not a good idea. That'll be a

Harpreet: [00:30:46] Not very helpful. Mark, go it.

Mark: [00:30:48] Something that's been popping up as well from my friends and my engineering. And maybe this is that might be biased because they're more so on the consulting side. But TerraForm, which is code for infrastructure, for cloud. That's really important, because many times when you are doing something along the lines, it's probably going to be in the cloud. And so that's a lot of the infrastructure around as well to deploy it. And so that's been a key thing I've been that I've been hearing from my friends who are in that space of things. They've had to learn on the

Harpreet: [00:31:18] Job, though. Yeah. And that type of stuff.

Harpreet: [00:31:20] Probably easier to learn on the job because you're you're dealing with real life, real

Harpreet: [00:31:25] World infrastructure

Harpreet: [00:31:26] And and things like that, rather than just kind of your sandbox environment. Rodney's mentioning that kubernetes here as well. Yeah, that's very important as as well.

Harpreet: [00:31:37] Um, any other insight

Harpreet: [00:31:38] On that, Rodney?

Rodney: [00:31:39] No, not particularly. Just as an alternative to Docker.

Harpreet: [00:31:43] There's a really cool website that my friend Mickey Kaus recommends, and that's called Full Stack, Deep Learning. And it's a lot to do with how to deploy things into production. It's all about shipping projects. I'll go ahead and pull this up and I'll show the link here. Maybe [00:32:00] you could find some benefit on on checking. Checking that up. Yeah, full stack. Deep learning. I think it's

Harpreet: [00:32:06] I think it's free. I'm pretty sure it's free. Yeah.

Harpreet: [00:32:09] All lectures and labs are free. And this is all about how to deploy things into production. And you can look at the at all the course material for that. And right here is what they get into the deployment aspect of stuff. Their stuff is not helpful to answer your question at all.

Harpreet: [00:32:24] Yeah, it does.

Cristoff: [00:32:25] I'm just wondering right now, is it a

Harpreet: [00:32:27] Thing I should know before

Cristoff: [00:32:29] I get a job or I can just add anything like few weeks when.

Harpreet: [00:32:34] Yeah. So I think it's definitely something you should know

Harpreet: [00:32:37] About and kind

Harpreet: [00:32:39] Of conceptually understand that, like, you know, a 10000 foot level, like, okay, what is Docker? Why do we need Docker? What is deployment? What to that? What does that mean? What what's that look like in real world? But then

Harpreet: [00:32:49] You don't need to like the super

Harpreet: [00:32:52] Nitty gritty Hands-On with the until

Harpreet: [00:32:53] You get the job.

Harpreet: [00:32:54] It's good to just have an awareness that these things exist, what functions they serve

Harpreet: [00:32:58] And how they can make your job of

Harpreet: [00:33:00] Getting your idea from an idea to production easier. Mark, go for it.

Mark: [00:33:06] Let's say we really want to impress people is take your old portfolio projects and put it into production using Docker where it might be. So that way show the initiative of like, hey, I've learned it. I would love to learn on a production system.

Harpreet: [00:33:20] Yeah, it's not difficult

Cristoff: [00:33:22] To to do that. Right. I mean, it's like so commonly used there. It must be like I know that is like Docker file

Harpreet: [00:33:30] Where you put all

Cristoff: [00:33:31] Those Busche comments or and I'm not sure I now I I've seen this. I don't remember.

Harpreet: [00:33:38] Yeah. It's not not terribly not terribly difficult

Harpreet: [00:33:42] At all

Harpreet: [00:33:42] Conceptually. Like, I mean, once you get deeper into it, it's a lot more difficult. But yeah, I mean, just creating a Docker file and composing it, you know, a Docker image, it's, you know, and running it at a few commands that you need to know, like just to do it yourself. But once you start having to troubleshoot and get nitty gritty and stuff, [00:34:00] I'm sure it's a lot more difficult doing that.

Mark: [00:34:03] I guess what I found from writing like production code is that many times is actual chwat self-learning.

Harpreet: [00:34:09] It is actually the easy part.

Mark: [00:34:12] The hard part is integrating it with all the other moving pieces within the code base. And that's that's the part I spent a lot of the times. I built this like modular unit. Connect the pipes like my engineer colleague, they've built this component and how to get this output out to another component. Another engineer friend made. And so that's the hard part, is like connecting all the pieces and integrating it.

Harpreet: [00:34:34] That's where the engineering

Harpreet: [00:34:35] Comes in handy. That's where the

Harpreet: [00:34:36] Engineering skill comes in handy. All right, guys. So, Christophe, hope that was helpful. I think there's that there's a really good Dogru class taught by the folks of Super Data science on Udemy. That might be worth looking into.

Harpreet: [00:34:51] I mean, not a good

Harpreet: [00:34:52] Tool to kind of know

Harpreet: [00:34:53] About is, you know, airflow, but

Harpreet: [00:34:55] Beyond airflow, just, you know. Directed a cyclic graphs like are they fit into the

Harpreet: [00:35:02] Yes deployment

Harpreet: [00:35:03] Ish aspect of of sheet learning project? We can do that as well as a really good class on Udemy.

Harpreet: [00:35:10] That's super in-depth on air flow.

Harpreet: [00:35:13] So when you get to

Harpreet: [00:35:14] That point, let me know.

Harpreet: [00:35:15] I can send you a link. I kind of just know at a high level, but not super in-depth. All right. Checking the comments

Harpreet: [00:35:21] Here on LinkedIn

Harpreet: [00:35:23] And on YouTube, don't see anything coming in. So if anybody has a question, now is the time to go for it.

Harpreet: [00:35:28] So usHe go for it.

Asha: [00:35:29] So I have a question. If you could go back to your younger self when you were starting up. What advice would you give yourself in terms of the walking environment? Did I freeze up?

Harpreet: [00:35:40] No, no. You get you get what? You just froze up now because I freeze up. No, you're good. We could still hear you say you said. What advice would you go back to give your younger self?

Asha: [00:35:50] Yes. Like in terms of career progression and working environments and places you pick.

Harpreet: [00:35:55] So how young we talking? Because I go back, I tell myself a lot of [00:36:00] stuff, like

Asha: [00:36:01] I suppose you go back. Oh, the advice would be helpful.

Harpreet: [00:36:05] Yeah.

Harpreet: [00:36:06] And we're bite Navarra. We got advice because it's just been ringing true in my in my head so much over the last year at least. And it's just that hard work is no substitute for who you work with and what you work on.

Harpreet: [00:36:19] Right.

Harpreet: [00:36:20] So I'd go back and and tell myself

Harpreet: [00:36:22] That as as a

Harpreet: [00:36:23] Younger buck. Yeah. You might like being a lone wolf and sitting by yourself and just working hard. But if you really want to do great things in this world, you sitting there alone by

Harpreet: [00:36:32] Yourself, working hard.

Harpreet: [00:36:35] That's just one leg of the stool. You still have to work on something worth working on. And you have to work with people who are high energy, high integrity, high intellect so that what you are working on can have maximal impact on the world. I'll go back and tell yourself that.

Harpreet: [00:36:51] But I would be like you trying to be Mr.

Harpreet: [00:36:54] Lone Wolf, isolated, doing your own thing. You're not going to accomplish shit or impact

Harpreet: [00:36:59] Anyone just by yourself.

Harpreet: [00:37:01] Right. The hard work that's spinning of your wheels, that's not that's not the

Harpreet: [00:37:05] Entire, you know,

Harpreet: [00:37:06] Equation. You still need to work on something worth working on and need to work with people worth working with. And I'm super excited, man like Austin just taught. You know, Austin showed me who I'll be working with on on my new team at at Comit. You know, that this core group of folks that I'm working with, Mannum Superpipe like these, these two other people that I'll be working with are supersmart. Like I'm looking at the portfolios. I'm like, holy shit, man, like the dumbest motherfucker on this team. I'm excited to be learning from them. Like I'm like, damn, I'm pumped up.

Harpreet: [00:37:37] Right. Like I for the

Harpreet: [00:37:38] First time in what feels like a long time. Right when I first started, I bowled. I work with

Harpreet: [00:37:44] A lot of great, wonderful people

Harpreet: [00:37:45] Aboard. But that that team was just so young. And, you know, we didn't have I was like the only Data scientist was machine learning engineer and then a software engineer who knew it about machine learning. But we couldn't really impact much.

Harpreet: [00:37:58] We couldn't

Harpreet: [00:37:59] Do much. And then our price [00:38:00] being the only data scientist like that was hard. And now here I am. I'm working with like two other really, really

Harpreet: [00:38:05] Accomplished data scientists,

Harpreet: [00:38:07] Three other really accomplished scientists doing some awesome stuff, or Sub-Group. And we expect to work Woodroofe and then a couple other people who will remain nameless right now.

Harpreet: [00:38:17] I'm pumped,

Harpreet: [00:38:17] Man. Like I'm excited. Like I'm going to be working on cool stuff with awesome people. For now, I feel like all that hard work that I'm doing is going to have a massive impact. So that's that's that's a long rant for me. Let's toss it to a tamarack and then let's hear from anybody else that wants to to get in on this.

Mark: [00:38:36] Yeah, this isn't sound counter, counter counterintuitive, but like hard work

Harpreet: [00:38:39] Is a trap because the thing

Mark: [00:38:40] Is like this downstream, like hard work and smart work. Many times you're doing hard work. You're just keeping yourself busy. And that that like progression makes you feel good. But like, ah, you actually have driving impact and there's more. So USPSTF, it's like a career in data science, because I feel like for progression occurring data science, you have to be very you have to have the impact

Harpreet: [00:39:02] That critical thinking. And that requires a lot of smart work.

Mark: [00:39:05] And so from all of that, I think the thing that really started, I start to see a lot of progress in my career and start going like that hyperdrive feeling was when I start having patients. And so like don't jump quickly to every new thing or every new project, everything you can help out on, just being really patient, reading the room, reading situation, reading what's happening in your career in the market, and make key decisions on what exactly you want to work on. Similar to a Harp reset, like, you know, what you work on, who you work with. I've seen for the past year that's really been the key differentiator between what I was doing the past and what I'm currently doing now before I was really spread out and trying to do everything. And because I felt like if I'm busy, I'll feel good and I feel like I'm making progress and now I'm less busy, but driving way more. And B. Cause now I'm like actually relaxing, actually [00:40:00] taking time for myself to refresh and think critically about like what's the most impactful thing. And so now instead of me running everywhere, I'm running to run into the bag. You know, I know exactly where I want to go to now for this point. Oh, this gold lines with this. I'm gonna go directly there and cut out everything else. And so that's that's the key thing is just patience and being very critical, like what exactly you work on, because you don't need to do everything. Looking back at a lot of the stuff I did was just extra and anything, just a drain on my mental health and wellbeing.

Harpreet: [00:40:33] Yeah. Another good advice from Devourer, because is just the things that you work on. Like, first of all, just start out by setting a really high aspirational hourly rate for yourself. Right. And then think about, okay, I value my time at this many dollars per hour. Now, if I go do a task that I can outsource for less than that amount, I should probably outsource that task because it's going to take me away from the actual work that I should be focusing on. Right. So, for example, I hire people to clean my garage to

Harpreet: [00:41:01] Like pull out the

Harpreet: [00:41:02] Weeds, to come clean my house, all that stuff, because that's important work that needs to get done. That's hard work that needs to get done. Not doing anything important if I work on that stuff. Right. So I either just hired people to come and do all that

Harpreet: [00:41:14] Stuff for me. Right. Because then I could

Harpreet: [00:41:16] Just focus on the actual work I need to do. Right.

Harpreet: [00:41:19] So I don't know if

Harpreet: [00:41:21] That fit in with what Marcus and I kind of felt like it did. But now that

Harpreet: [00:41:23] I talk about it, maybe not.

Harpreet: [00:41:25] But, you know, a bit of advice that a high aspirational hourly rate for yourself charged that high aspirational hourly rate, anything that you have to

Harpreet: [00:41:32] Do that that

Harpreet: [00:41:33] You can outsource for less than that rate. Outsource it, Cristoff. Go go for it.

Cristoff: [00:41:39] And so I love what you mentioned, Harpreet and Mark. And I ask myself this question pretty

Harpreet: [00:41:46] Often, am

Cristoff: [00:41:48] I being busy or am I being productive

Harpreet: [00:41:51] Right now? And I think this

Cristoff: [00:41:52] Is the great question to ask, because many people don't see the difference. And the difference is really huge. And [00:42:00] another thing you guys mentioned is when you get to choose who you work with and what you work on. And I think another thing is when you work on it, this is like another level of freedom and flexibility when you get to choose the time when when you work. But going back to your question, I suppose I and I tell myself the difference between fixed and growth mindset, because this is what I learned at the age of 32 and I've wasted like 15 years of my life, at least not trying to learn new things, because when I was younger, I was pretty good at math. So I didn't have to learn it. I just got it. So every time I struggle with anything to to understand, I felt it wasn't my thing. So I wasn't able to learn it or I was just wasting my time with something, though,

Harpreet: [00:43:05] That would change my life

Cristoff: [00:43:08] Drastically if I had this mindset when I was younger.

Harpreet: [00:43:11] Yeah, that's an excellent, excellent point. Yeah. Growth mindset, super, super important. And actually, I'd love to hear from Natasha on this as well.

Harpreet: [00:43:19] But while Natasha gets

Harpreet: [00:43:20] Gets ready, I want to put that point Cristoff made about being busy and productive. I have a quote here from Seth Godin right next to my calendar that I'm literally going to pull off my wall that I look at every day. And I just printed printed it out. And what it says is everyone who wants to be busy is busy, but not everyone is productive. Busy is simply a series of choices about how to

Harpreet: [00:43:44] Spend the next minute.

Harpreet: [00:43:46] Productive requires skill, persistence and good judgment. Productive means that you have created something of value. Perhaps yourself created busyness is causing you to be less productive. And so that's stares at me every single day when [00:44:00] I come and sit down

Harpreet: [00:44:02] In my chair

Harpreet: [00:44:03] Right here. Natasha Furat, you're still around. Yes, you are. I'd love to hear what advice you would give to your younger self.

Asha: [00:44:11] Hi, everybody. I think my first one, Tezuka. Oh, so thank you for coming. If so, for me, Lcy is especially since I started doing data science last September. And you get I get so caught up by times trying to learn everything, trying to understand what is this goretti means, trying to participate in all the projects that I can at the same time. But but it ends up being not productive at all, because you're you're busy being caught up trying to get all the work done, but not really understanding what you're doing. So you end up. So I know I'm pretty just maybe two months back, I ended up just putting it in a notebook, but not really understanding what is good Lean's and and and what it will it will do more deals. So for me right now, I'm just taking the time out and doing things one at a time, taking up projects at a time just to understand what is it that I'm doing, so that at the end of it all, I'm productive and not just busy and not just getting everything I Tuggle.

Harpreet: [00:45:18] Yeah, absolutely. Love that. Focusing on one thing at a time. And that's that that's been a game changer for me as well as I used to want to do, like three things, four things in a day or, you know, in an hour. And I was just like, I'm doing this one. Maybe two things, if I get that

Harpreet: [00:45:34] Done, feeling good. A good piece

Harpreet: [00:45:36] Of tape here coming in from Hamrah on LinkedIn thing, develop discernment to be able to walk away from toxic people. Don't get burned out and leverage the success in your network

Harpreet: [00:45:48] And create your own lane.

Harpreet: [00:45:50] Absolutely. Marion, Rodney, Joshua, A. Anybody want to

Harpreet: [00:45:55] Give some advice to their

Harpreet: [00:45:57] Younger selves by imagining that [00:46:00] their younger self is go for it, man?

Cristoff: [00:46:03] Actually, I would like to question the question. So because it's

Harpreet: [00:46:09] There is nothing productive to think

Cristoff: [00:46:11] Of be. So if 10 years ago, I think the question is bohlig, the question is more like, OK, let's sort of stop what we're doing and try to evaluate

Harpreet: [00:46:25] What progress

Cristoff: [00:46:26] We're making. I mean, sort of putting things in perspective.

Harpreet: [00:46:30] Currently on our

Cristoff: [00:46:32] Blessings of goals,

Harpreet: [00:46:34] Evaluate and say, hey, listen, if you want to

Cristoff: [00:46:38] Achieve this, I'm not doing it litho.

Harpreet: [00:46:40] I'm doing it. What can I do better? That's sort of just a

Cristoff: [00:46:44] Comment on the question. It's not

Harpreet: [00:46:46] Like I'm going

Cristoff: [00:46:47] To advise myself when I

Harpreet: [00:46:48] Say to do this or

Cristoff: [00:46:50] Don't do this. It's more like,

Harpreet: [00:46:52] What would

Cristoff: [00:46:53] I have to think in the context

Harpreet: [00:46:54] Of the moment?

Cristoff: [00:46:55] You're trying to achieve something. So what it is that you're doing is that helps you do that.

Harpreet: [00:47:00] If it doesn't

Cristoff: [00:47:01] Help you, just

Harpreet: [00:47:03] Figure out what you

Cristoff: [00:47:04] Need to do to

Harpreet: [00:47:05] Do it.

Cristoff: [00:47:06] That's a common.

Harpreet: [00:47:07] Like that philosophical question. I think you very much married. Anybody else want to give some tips here for the younger selves

Harpreet: [00:47:13] Or we got some questions

Harpreet: [00:47:15] Coming in from A.A.? Get to I get to see you in the room. Anybody else want to chime in here? If not, we can go to Ante's question. Then there's a question coming in from from YouTube.

Harpreet: [00:47:25] Oh, I should go for it.

Asha: [00:47:26] I'm beginning to see a notification on my Internet and my Tresa. OK, you mentioned knowing today that.

Harpreet: [00:47:33] No, no. We get to get you

Asha: [00:47:36] Mentioned knowing your exact price and setting your exact price and going by it. How do you exactly decide whether. Sometimes you might lowball yourself because you feel like you want the opportunity, you want to learn. How do you exactly stand by?

Harpreet: [00:47:51] Because I know you get. You get. Yeah. So how do you figure out what price to set for yourself if you.

Asha: [00:47:56] Most of my video we did help with it, told them,

Harpreet: [00:47:59] Oh, you actually you haven't [00:48:00] you haven't cut out or at all or anything. You've been completely stable there on your Internet. So it's always there. So the question was how it should just feel absurdly high. Like so check this out, people. I get messages from CEOs all the time or, you know, PR firms like, oh, we'd have the perfect s they come on your podcast. And I'm like, great. I would absolutely love to talk to your person. Here's what it cost per hour to come onto to my podcast. Right. And the first time, like the first person that accepted that, I was like, who? Too easy? That was too, too easy. So I

Harpreet: [00:48:31] Raise the prices and I

Harpreet: [00:48:33] Raise the prices pretty high. Right. So now those CEOs listening, if you want to come on my podcast at the current moment and you want to talk about your company, it cost you thirty five hundred dollars for that one hour. Right. Like, that's it. So the first person that that paid me was a fucking I'm like when I give a shit. Who was it? It was it was analytics IQ. I charge him twenty five hundred bucks to come on the podcast. And I was like you said, yes. To twenty five hundred bucks. Like it was nothing. All right, cool. Next person that comes is thirty five hundred dollars to come on my show. And I know some people will take on work that, you know, they'll charge like five, six hundred bucks. Not like that's that's not enough for me, like five or six hundred bucks for me to put in the effort it takes to review like research. You research a company, ask my questions, get in front of my audience, do the editing, create the the,

Harpreet: [00:49:22] You know,

Harpreet: [00:49:23] Social media posting all that stuff. That's not worth it for me. So I'd rather I'd rather have, you know, people come on my show few and far between and just have high clientele and and then pay me that good money rather than having, you know, seven or eight people come in at at 500 bucks. Right. That's just more work for myself. So I just set my price extremely high and I'll probably go up again. I'll probably start charging, you know, five thousand dollars to come on the show.

Harpreet: [00:49:48] And, you know,

Harpreet: [00:49:49] If if all I get is one person a month or whatever, that's cool. That's, you know, just this is what it is. So it feels absurdly high. But the first time was too easy. So I said it higher.

Harpreet: [00:50:00] I [00:50:00] mean, comment

Harpreet: [00:50:01] Oamo got a good deal out of me. I should have I should have I should set the sponsorship for this happy hour, Orakzai a little bit higher. But hey, man, you guys hired me and ended up in a job.

Harpreet: [00:50:11] So that's that's you know, that's cool. But yeah, I just super again, any CEO

Harpreet: [00:50:16] Is listening great right now. It's thirty five hundred bucks to come on my show

Harpreet: [00:50:19] For an hour interview. After that, I'm

Harpreet: [00:50:22] This time next year might be triple the price. Who knows? But that's what it is. Yeah. Just make you feel absurdly high. So open and shoot myself in the foot with that. But I mean there's. Feels listening. You get rich, you get great questions, you get great coverage for your company. And what's the big deal? You got a marketing budget. That's the whole point of using it. So, um. Yeah. Great tips coming in here from from A.. A.. Do you want to tell us this tip here, or should I read it out? Let me know. I think you might be like I think a.. Is that flipping cars over, doing squats,

Harpreet: [00:50:57] You know, like lifting cars

Harpreet: [00:50:58] Over his head and pressing them is left in this fitness thing. But he's saying that he would just add,

Harpreet: [00:51:04] At least I wish that

Harpreet: [00:51:06] A younger version of myself would have that you should try anything, at least try for a while before deciding is too hard to learn. I've had so many surprises so far. Of course, there have been times when things have been harder than I thought beforehand. In any case, I don't stand in the way of learning and success. There you go. Am I going to interview Neil deGrasse Tyson? I've emailed him three times to no avail. But it's all good. I mean, one of these days people are going to be like, damn, I should come on your podcast before. Like I said before, man, like like my competition for this podcast is not they're not super Data science. Duncan, you're a good guy. I love you. You know, but you're not my competition, Avery, you're not my competition. Kenji, you know Libya as well. But now competition. Competition is Joe fucking Rogan.

Harpreet: [00:51:52] So it's going to

Harpreet: [00:51:53] Be to the point where you can't talk about podcasting without talking about me. That is my ultimate goal. You [00:52:00] cannot talk about the most amazing podcasters without mentioning Harpreet Sahota in the conversation. And I was just I spent the weekend. I listened to some old

Harpreet: [00:52:09] Episodes, you know, and I was like, damn,

Harpreet: [00:52:12] I'm actually pretty good at this. Like I was listening to that song I did with Jonathan tested the episode. They were Jacquelin Weils, a couple of other ones. I was like, Pam, pretty get it up pretty good. So they go, man, what am I getting that Spotify deal, man? Soon, I hope. I hope that would be amazing. Yeah. Mark has a question about podcasting. Go for it and then A. will get to your question and then you am on YouTube.

Harpreet: [00:52:38] I'll get you a

Harpreet: [00:52:38] Question as well. But go for a mike.

Mark: [00:52:39] Yeah. So my my friend and I are actually working on hopefully creating a podcast soon. Not actually Data really at all. Thanks for the quick sneak peek, the kind of Taissa last question. It's called Dear Past Self, where we're going to be interviewing ourselves and other people of like how to model good behaviors around, like thinking about your past where and both positive and negative. And so one of the key things, what metric is saw like the get to the top one percent ballpark and you have to create 21 episodes because most people quit before then. So I guess like in your first twenty one episodes, Harpreet, what was the kind of key steps you made or decisions you made to like keep on going?

Harpreet: [00:53:22] Yeah, so I recorded it, I released 12 episodes at once. And then at some point during the during the podcasting journey. Right. I read Brian Holiday's book, perennial seller. And he said something in that really stuck out. And it was only is better than best. Right. So I was like, OK,

Harpreet: [00:53:39] So only is better than

Harpreet: [00:53:41] Best. So I should make my podcast, the only podcast that does something for a specific group of people. Right. So for me, that was the only self development self-improvement type of podcast, specifically for data scientist. Right. And they're not only that, I'm going to be the

Harpreet: [00:53:56] Only podcast

Harpreet: [00:53:57] That has things like this where the community comes [00:54:00] in and ask questions, and they could chat and they could do stuff right. I don't know if that's answering a question or not, but

Harpreet: [00:54:05] I'll pause there and you know,

Harpreet: [00:54:08] Stuff that that was.

Mark: [00:54:09] Yeah. No, I was definitely curious just about what what was that

Harpreet: [00:54:12] Kind of a key differentiators

Mark: [00:54:14] For yourself in the beginning when you're starting your podcast, given that after that remet that ran a statistic that most people quit after the first episode. Five episodes. Yeah. Only one percent gets to 21. Yeah. How do you keep up that that momentum and be like, yeah, this is this is a thing I'm going to do and like build from those mistakes?

Harpreet: [00:54:34] Yeah. I also read that statistic, too, and I told myself that's

Harpreet: [00:54:36] Not going to be me. Like I'm not going to

Harpreet: [00:54:38] Quit after a while. Like I told myself, I'm just gonna keep doing it.

Mark: [00:54:41] I would just like twenty one episodes.

Harpreet: [00:54:43] That's it.

Harpreet: [00:54:44] We episodes is hard to do, man, because it's a lot of work to do the editing to like put the effort in. And you know, like I mean, you know, there's a couple of, you know, a couple

Harpreet: [00:54:53] Of friends, for example,

Harpreet: [00:54:55] That have started podcast and then did like three episodes. And we're just like, there's too much. I can't do this after hyping it up for for quite some time. Right. And, you know, we expect to have these great conversation that is like much.

Harpreet: [00:55:06] But you have to

Harpreet: [00:55:07] Love it, right? You have to actually enjoy doing it. You have to

Harpreet: [00:55:09] Love doing it. And for me,

Harpreet: [00:55:11] It's just like, OK, I'm going to do this. I'm not going to be a statistic. You know, just coming out of Valley High School, I was like, you know, destined to be a statistic anyway. So I was like, I I'm not going to be a statistic, that's for sure. And just continue to go on with there, continue to push it. And once I started just getting I sort of there's a time where I was just I was like, okay, I'm just gonna reach out to every single author that I have on my bookshelf, you know, reach out to these people. I'm of them still to come on and all I'm sort of saying yes. And I was like, what? This is my handbag. This is crazy. All these people just started coming on the show, New York Times best selling author and things like that. And I was like, OK, well, this is this is cool. I'll continue doing it. And then I remember having conversations with like my Grossmont, for example, my Grossman. You guys might know her. She's on LinkedIn as well.

Harpreet: [00:55:59] She wrote [00:56:00] that book Invaluable.

Harpreet: [00:56:01] And she was giving me you know, she's like, man, you got like a really authentic style, got authentic voice like, you know, you keep at it. People really going to connect with what you're saying and what you're doing. And I was like, that's a good cosign. Same thing with Deborah Brubaker's like she gave me you should I do your pocket so fresh, so refreshing, like the way you mixing it, like all that stuff. Like, listen, if you're upset, it's really good.

Harpreet: [00:56:22] And you know that cosign as well,

Harpreet: [00:56:23] Because she's been on TV, she'd had TV shows on Discovery Channel and things like that. And I was like, damn right. Well, these people are

Harpreet: [00:56:30] Saying that, you know,

Harpreet: [00:56:32] What I'm doing is is good. So I should keep

Harpreet: [00:56:34] Keep doing it. And I mean,

Harpreet: [00:56:36] There's this line for this big Sean song. He says he said something along the lines of they say, my music beaten because I follow my heart. Right.

Harpreet: [00:56:43] And I was like, I, I always had

Harpreet: [00:56:45] Faith,

Harpreet: [00:56:46] Like

Harpreet: [00:56:46] Who swallowed the dark? Something like that. And I was like, all right. Well, people are resonating with my podcast because I actually just pouring all of myself into it. I just completely just giving everything I have to an episode like the research, writing an intro. I think my three or four hours write an intro. It takes me like 12, 15 hours to do the research.

Harpreet: [00:57:06] Um, you know, it

Harpreet: [00:57:07] It takes a lot of effort to create a good quality episode. And I just poured everything into it and just did it

Mark: [00:57:13] Quite quick follow up question. And and if you if we have to go on another. I want to let me know. I can just hold it.

Harpreet: [00:57:19] But go for it. Go for it.

Mark: [00:57:21] Ais versus like again, I posting with like this MVP approach. I'm kind of like I start with the my own mentality is like my friend and I like this. Don't worry about the mixing or making it perfect. And it's just like put content out there and ideas that we create genuine good content like people we gravitate towards that. That's the hypothesis. And the other mixing and nice connections will come along the way. But that way we reduce the time to put content out there and get those episodes in.

Harpreet: [00:57:49] Do you feel as

Mark: [00:57:50] If for the podcasting format, like is that a approach where taking the startup perspective may not be the best thing, or do you feel like you like it's OK for the [00:58:00] beginning to have like really raw stuff?

Harpreet: [00:58:02] Yeah. So for me personally, I just had a vision for how I wanted the entire episode to be pieced together. Right. And I made sure that I did the like for the first 40, 50 episodes. I probably did everything myself. And then I figured, okay, if I do the editing, the mixing and I have

Harpreet: [00:58:17] Everything out I can

Harpreet: [00:58:18] Outsource and then whoever I outsource that to now, you know. She does a great job, she's been out these done hundreds of episodes for me now. And he just has a good blueprint. He knows my style.

Harpreet: [00:58:28] He knows how I want to edit stuff. So this is this like

Harpreet: [00:58:33] An art form is your art

Harpreet: [00:58:35] Work? Right.

Harpreet: [00:58:36] So you have to approach it with that krasnow mentality, with that artist kind of mentality. Because at the end of the day, like I do the podcast for myself. Right, like this is like almost like therapy for me when I do the research and I do like the conversations and stuff like that. So I treat it like it's my art form, like it's actual artwork. And I don't know, like the MVP thing, like thing works with product. I don't know if it works with art.

Harpreet: [00:59:01] Right now that that

Mark: [00:59:02] That 100 percent makes sense that I biased towards the MVP sort of approach. But also like I used to be a dancer, so I get the hardest thing. Right. And so it's like a battling between the two. And I mean, there's I don't want perfection get in the way of getting something out there, because I can definitely get into that trap as well.

Harpreet: [00:59:20] Yeah, man. I mean,

Harpreet: [00:59:21] There's there's another track I was listening to by a

Harpreet: [00:59:23] Called Blood by Anima.

Harpreet: [00:59:25] And then that track that how about like how art is work. Work is love. It hurts to give yourself to it. And then they talk about how in the same track that once you create it, you might be too good to appreciate what it is that

Harpreet: [00:59:42] That you've created. So I don't know that made sense.

Harpreet: [00:59:44] But somebody's asking here, what what's my reach on my podcast? I think I just passed 80000 downloads, which I think is pretty big.

Harpreet: [00:59:54] I was looking

Harpreet: [00:59:55] At like on Chartable. I was like I was ranked like podcast number three hundred or something, which [01:00:00] is huge considering that there's how many thousands of podcasts out there. So I think I'm definitely in the top 20 percent of the distribution for podcasts in terms of reach and number of downloads and things like that. And hopefully just try to get that bigger, a bigger and much more. But yeah, like for me, like when I create the podcast had a very like this is how I wanted it, I wanted to have that. But that Sizzler in the beginning was just like a key moment from the show that kind of encapsulates what the with that I thought it was about that I wanted to have music and want to have my intro talking about the podcast. And I wanted to have, you know, the introduction of the guest. And I want to have this huge of questions. And I wanted to have the random answer. I like everything. Like I wanted that kind of structure so that when you listen to my episode, you know exactly what to expect and how things are going to be pieced together. So I was very deliberate

Harpreet: [01:00:47] About that part. But Markovsky, that was helpful.

Harpreet: [01:00:51] Um, let's go to our anti's question. And he had a question a while back about cookie cutter. But for cookie cutter, Data science cookie cutter is just a framework for the repository structure itself. I think it's agnostic from the actual language that you're using.

Harpreet: [01:01:07] So granted, you know, they've got

Harpreet: [01:01:09] It set up in Python, but that just makes it easier for you to install the new repository structure, clean repository structure. But I think that actual structure itself, it works whether you're using

Harpreet: [01:01:20] Our Java, Julia,

Harpreet: [01:01:22] Whatever

Harpreet: [01:01:22] Octave MATLAB,

Harpreet: [01:01:23] Whatever you're using, I think that repository structure and that framework

Harpreet: [01:01:27] Is applicable.

Harpreet: [01:01:28] Another structure that I like that integrates the Data engineering aspect with the data science

Harpreet: [01:01:33] Aspect of it and creates like a really

Harpreet: [01:01:35] Clean pipeline is quadro. That's a lot more opinionated.

Harpreet: [01:01:39] It's a great

Harpreet: [01:01:40] Framework. So I highly recommend that. What about what do you guys think? So there's comments here around cookie cutter data science, the cookie cutter data science. For those you don't know, it's just a way to structure your project. And probably the most common that I've seen, um, you know, the teams that I've been on their staff. Thank you for joining, Matt. Appreciate. Appreciate you.

Harpreet: [01:01:58] Um, what about what

Harpreet: [01:01:59] About you guys? [01:02:00] What do you guys use for your project structure, Mark or Rodney or Asha?

Mark: [01:02:04] I wish I knew about this cookie cutter thing earlier, but I make my life a lot easier. Yeah. The way I structure my repositories is, you know, I

Harpreet: [01:02:13] Have like my main repository

Mark: [01:02:15] Readme file. If it's all my GitHub

Harpreet: [01:02:19] Potential, like Bison's file,

Mark: [01:02:22] If it's something like something I'm sharing out broadly, and then I'll have like my code and then I'll have my tests. And that's kind of like how I structure kind of my my work

Harpreet: [01:02:34] Thing I don't do enough is

Harpreet: [01:02:36] Testing my

Harpreet: [01:02:36] Code.

Harpreet: [01:02:37] That's probably something I should focus on doing a little bit more. What do you use for testing is a great expectations

Mark: [01:02:44] For for testing. So I'm also thinking about like my work at home. But essentially for my tests, like many times, like when I'm building out a I can give an example for like my NLP pipeline thought my job. I didn't have access to any of the data. And so the very ends, I had to build up this whole pipeline to work and plug back in.

Harpreet: [01:03:06] I had no

Mark: [01:03:07] Data to work on. So I just took out a whole bunch of office quotes and and put it in there. So we have a code baseball office quotes now, which is great. Which I love. I love that. Oh. And I ran my NLP pipeline on like Michael Scott. I declare bankruptcy all throughout. So that's that's why I use test scores. Actually, like I create this piece of logic. I'm expected to do X, Y, Z. You know, and that test is going to end. And I know there's different like tools for that. We have our own in-house testing things. I can't really go into that.

Harpreet: [01:03:43] But that's where the units

Mark: [01:03:44] Has really come into, is like when you don't have

Harpreet: [01:03:46] Access to data, but you need to

Mark: [01:03:48] Build out logic to put into production. Those unit tests essentially assured me that by by the time you get to the end this project, it will work. Oh, my. I'll have to go back like two months.

Harpreet: [01:04:00] Yeah. [01:04:00] If we have high

Harpreet: [01:04:01] Test, I think it would be good. Good for that as well. Great expectations. Sorry. That's typically just for a pipeline cleaning up. So that's a great package for that. Brodney, any tips on on this?

Rodney: [01:04:11] I don't do a lot of testing. Well, I do, but

Harpreet: [01:04:15] I don't do

Rodney: [01:04:16] Formal unit tests just because of the nature of most of

Harpreet: [01:04:20] What I work on. It doesn't play that

Rodney: [01:04:22] Big a role. And and then I just structured around

Harpreet: [01:04:27] Projects usually

Rodney: [01:04:29] Because everything comes more or less as a project. And then I, I just do it that way.

Harpreet: [01:04:34] Yeah. We got

Harpreet: [01:04:36] A.. To answer a question, I think that cookie cutter Data science repository

Harpreet: [01:04:40] Structure is I'm a huge

Harpreet: [01:04:42] Proponent of it. I swear by it. And I think that

Harpreet: [01:04:45] Is it's absolutely agnostic

Harpreet: [01:04:47] Of what programing language you use, because it's just more philosophy on how to structure your project. And your code is good. I guess I test my code as I build it so I don't do formal unit tests or anything like that. But for a formal unit testing and looking to PI test, then to test your actual pipelines, you can look at great expectations and an to other data science that is strictly for Python is called Tedrow HETEROS. Great, extremely opinionated, but it ties together Data engineering pipeline as well as the

Harpreet: [01:05:17] The the data science

Harpreet: [01:05:19] Pipeline and the makes using notebooks a little bit easier because the Kedron notebooks are quite they have this thing called the Kittrell context, and you just save a lot of keystrokes with that. So Tedrow for deployment ready

Harpreet: [01:05:31] Projects is what I

Harpreet: [01:05:32] Recommend, like outprice, like the project that we deploy. The production was a Kendrell project, just because it made things a lot easier. So I'm looking for that. There's a question coming in now. Let's switch it up A. Hopefully that's helpful question coming in from a YouTube from um.

Harpreet: [01:05:48] What are your thoughts

Harpreet: [01:05:49] On a B.S. the in data science and programing by Indian Institute of Technology Mudras? Are there any job opportunities after pursuing it? Please reply. I'm going to apply for it. All right, man. Well, if you're going to school [01:06:00] just to get a job and

Harpreet: [01:06:02] Think, what are you going to school? Um, you know,

Harpreet: [01:06:04] Oh, look, I don't know anything about the Indian Institute of Technology, Madras. It might be a good school. May not. I've never heard of it. Don't know anything about it. Are there job opportunities after pursuing it? Yeah. Job opportunities are abundant. I is learning Data science and programing skills that you should probably learn. I think so. Especially, you know, when you're my age. I'm assuming if you're just now going to school, you're probably 18. So 20 years from now, you'll be 38 years from now, I'll be damn near 60. You've been your 16, 20 years. The landscape is going to be different, like jobs that exist today might not exist because of the technology that people like us are building. But it is going to open more and more jobs that we could not even imagine

Harpreet: [01:06:48] Because of the work that we've done. Right. Just like,

Harpreet: [01:06:51] You know, when we when the electricity came around, right when electricity came around, they put a lot of

Harpreet: [01:06:56] People out of work. Right.

Harpreet: [01:06:58] Because easily people who had to walk around and the

Harpreet: [01:07:00] Lamp, the light,

Harpreet: [01:07:01] The lamps with with fire and stuff like that now would just turn on the switch and things get brightened up. Right. But those people found other jobs.

Harpreet: [01:07:08] Right. So I think you should study it as their job opportunities.

Harpreet: [01:07:13] Yeah. But you're the one that has to be employable. Right. So you can get a degree. I know a lot of people who got degrees and in fact, I've gotten degrees.

Harpreet: [01:07:22] And I was

Harpreet: [01:07:22] Not employable because I just didn't know how to work. But eventually I did become employable because I knew how to work apart there with my rent. That's exactly what other people have to say. Asha, what do you think? Let's hear from Rush on this.

Asha: [01:07:36] Oh, no, I guess you can't judge it according to a school. A lot of us, most people in this field moved away from their backgrounds. A lot of the people I work with moved away from very different backgrounds, then came into it. But in terms of getting a job, I think you are right. I saw it firsthand abilities. It's a skill. Interviewing on its own is a skill. I've seen two people, one who was over [01:08:00] overweight if I had, and one

Harpreet: [01:08:01] Was not, and they got

Asha: [01:08:02] The job. It's definitely a skill. So the jobs are there.

Harpreet: [01:08:07] But you

Asha: [01:08:08] Can be in a school

Harpreet: [01:08:09] Like. Yeah, I like. That's an excellent point, Mark. I like this comment here.

Harpreet: [01:08:13] Mark, tell us about this.

Harpreet: [01:08:15] Mark says data scientist is eventually going to be an antiquated term exclamation point. Talk to us about this,

Mark: [01:08:21] Because I see some tech OG's and they're constantly telling me like, yeah, like data science is hot right now, so be prepared when it's not going to be hot. And it's always the older folks who've been in tech or at least 20 years saying this to me. So I feel like there's some truth to this because it's a common pattern.

Harpreet: [01:08:42] But you're also kind of

Mark: [01:08:43] Seeing this as well, where like you were a hard data scientist and like, what does that even mean? So that's why you see that fractions like my engineer, data engineer, analytics engineer, data analyst. Right. So that's that one component. And then going back to the original question about the school, like I feel I don't focus on the school or the degree focus on the type of problems you want to solve in the world. What problems are your kind of passion or at least an interest in? And they'll better guide your learnings. And you can do it with that degree. But that way, you're not stuck with the job opportunities or go away in 20 years, you know, focus instead on like what's the big problem? And that problem will change over time as well.

Harpreet: [01:09:23] Yeah.

Harpreet: [01:09:23] So focus on the skill set

Harpreet: [01:09:24] That that

Harpreet: [01:09:25] Are going to be kind of timeless skill sets on

Harpreet: [01:09:28] This case, logic, mathematics,

Harpreet: [01:09:31] Programing, problem solving. These are skills that

Harpreet: [01:09:34] Are important that

Harpreet: [01:09:35] I don't think will be automated away, that you should probably develop that kind of like the along the lines of which saying there.

Mark: [01:09:43] I think more so long as I give you examples like my my ultimate goal, a big problem. Why is like I'm trying to improve well-being for as many people as possible, especially around marginalized communities. That's like that's my vision. My mission. Right. Originally, I thought I had to be a doctor to do that. Then [01:10:00] I thought, I do community health. Right. And then I found Data and I was like, oh, I can do this at scale and build. It is repeatable and I'll do it by patient. By patient. Right.

Harpreet: [01:10:11] And so, like having the ultimate

Mark: [01:10:13] Problem I want to solve and being flexible enough about how I do it, I'm still solving the same problem. Another avenue which I solve, it's much more clear to me. It just so happens to be Data.

Harpreet: [01:10:25] Awesome, Brodney, what do you what do you think? And I'd love to hear from anybody else here, either Josh or Natasha or Chris on this on this topic as well. I think,

Harpreet: [01:10:35] You know, things change

Rodney: [01:10:36] Names. That doesn't necessarily mean a lot. And the use of Data in business has been around for a long time. Well before anyone was talking about

Harpreet: [01:10:47] Data science, where, you know, you had

Rodney: [01:10:49] Business statistics, for example, and then

Harpreet: [01:10:52] The tech industry started

Rodney: [01:10:54] Basically exploiting their log Data, I think it was. And so that then gave birth essentially to Data science. But it's not confined to tech. It's there are lots of things you can do in in other sectors, government and all these other sectors. I don't see it going away unless computers go away, and in particular, unless some unless the Internet goes away. So I think that it's it's always going to be there as long as those technologies exist, because that's that's what's driving it. And so so, yeah, I mean, the name might change,

Harpreet: [01:11:41] But

Rodney: [01:11:42] The skills are still there. And so people like me have been sort of around a bit longer. Before this case came up, we were we were

Harpreet: [01:11:50] Learning and building these

Rodney: [01:11:52] Skills before Data science

Harpreet: [01:11:54] Existed, before machine

Rodney: [01:11:56] Learning existed.

Harpreet: [01:11:58] So it [01:12:00] it

Rodney: [01:12:01] It's it doesn't matter that she's still get. You still going to have a job?

Harpreet: [01:12:07] Yeah. Yeah. Yeah. Awesome. Hey, the start to wrap it up. If anybody has any last minute questions in the room, go ahead and let me know. I'm checking on LinkedIn over announced on LinkedIn that we are wrapping up. So if anybody has questions on LinkedIn, now's the time to ask

Harpreet: [01:12:21] Until you

Harpreet: [01:12:21] Guys think of a question to ask. When you just start filling some time by saying that you should tune into the podcast or at least an episode.

Harpreet: [01:12:27] David.

Harpreet: [01:12:28] Benjamin, I think if you listen to that episode, David Benjamin, coupled with the episode with Fred Pillared, kind of like back to back, I think that'll help you really

Harpreet: [01:12:36] Understand how to, you know, be a bit more

Harpreet: [01:12:38] Strategic, how to think about solving problems. I think those two episodes really complement each other really well. So definitely check that out as as we listen to episode two with Jonathan

Harpreet: [01:12:48] Tesser a couple of days

Harpreet: [01:12:50] Ago. I thought that was a good episode. So let me check that out as well as the one with them, Jaclyn Wells next week, Korogocho coming up next week, I think next week on the podcast. I've got episodes actually cued up and ready to release until March of next year. I got a ton of content coming out and I stopped recording at the end of May. So that's a lot of episodes that I got out in the queue. Next up, coming

Harpreet: [01:13:13] Up, there's going to be dope,

Harpreet: [01:13:14] The one that's happening this Friday with Jeff Lee. I really enjoyed that conversation. I had Jeff, Lisa, Jeff, Lisa, data scientist at Spotify. He was also on I think he's currently Kenji's roommate. So Jeff lives on Kenji's podcast as well. So the

Harpreet: [01:13:29] Opening like 30

Harpreet: [01:13:31] Seconds of that podcast, my little Sizzla. It's some special, some difference. I hope you guys tune in and give that a listen. But I remember reaching out to Jeff like he was one of the people are trying to reach out to on my podcast before I even had a podcast, because I came across his blog, I came across his work and was like, dude, I really like your your works. And when come on, it took a while, but eventually came on the podcast, so excited to have the conversation with me. And we touched on a wide range of things. Then after that, [01:14:00] I've got an episode with Tiffany Shlain

Harpreet: [01:14:02] Coming out on

Harpreet: [01:14:03] September 3rd. Tiffany Shlain is the woman who invented the Webby Awards. So that was really awesome. We talk about her book. Her book is the book called Twenty Four Six, and it's about taking the day off. And after that episode with Max Frenzel, who's a researcher. Talk about his book Time Off, about developing a I think

Harpreet: [01:14:23] Doesn't look like

Harpreet: [01:14:23] Any questions. Don't see anything coming in from LinkedIn or here in the chat. Actually, there's last minute question here. Can you tell the sources where I can start to learn Data science from basics person with highest level skills? There's a lot of places you can check out. So a couple of good

Harpreet: [01:14:38] Places is, you know, there's Andrew Jones program, Data Science

Harpreet: [01:14:43] Infinity. That's a great program. Like personally, I've gone through the program myself, like looked at the course materials, quite comprehensive. Check that out. Avery's got

Harpreet: [01:14:52] A

Harpreet: [01:14:53] Course that he launched Data career jump start. I personally haven't gone through that course yet, but I've seen

Harpreet: [01:14:58] The syllabus,

Harpreet: [01:15:00] For lack of a better word for it. So definitely maybe check that out. Anti's facing data cap. Definitely check that out. But at the end of the day, men like if you don't need the online course, there's probably three books I can recommend for you that will get you pretty much everything you need. First book is a SQL Queries for mere Mortals by John something, right? That book will teach you how to do

Harpreet: [01:15:21] Sequal like

Harpreet: [01:15:23] This through and through. Then there's Python for Data analysis by Wes McKinney, who is the guy that invented the panda's library. That is the book that I use to learn Python and Pandas. It teaches you not only the fundamentals of Python as a pure programing language, but it also gives you exposure and

Harpreet: [01:15:45] Experience

Harpreet: [01:15:46] And develop a intuition for the Pandas library. That's an excellent book as well.

Harpreet: [01:15:51] We learn how to do not only know

Harpreet: [01:15:54] This, but some data analysis and things like that as well. Another book that I recommend is Introduction to Machine [01:16:00] Learning with Python, because through that book, again, you'll get a better understanding of Python as a pure programing language, plus the experience with the psychic learning API, which is important. So those would be my three

Harpreet: [01:16:13] Books that, you know, you spend one month

Harpreet: [01:16:16] On each book within three months. I think you'll have a baseline level of skill.

Harpreet: [01:16:20] And there's some great comments

Harpreet: [01:16:21] Coming into here. There's a exploratory Data analysis by John Tukey. So if you want to get into the artistic side, a more in-depth level, that is a good route to go. That probably a good book to couple with Python Data analysis by Wes McKinney. Ten minutes to Penders. I've never heard of that, but ten minutes do. That's not it's not a lot of time. So that we check that out in an open intro. Statistics combined with Data camp. And then to that, I'd also add python principles. That's my new favorite resource to direct people to to learn Python if they're brand new to Python Python principles, because you'll learn Python as a pure programing language and get exposed to all the fundamentals. And they have a lot of fun, free challenges. The entire thing, I think, is currently free. But there's a lot of really cool like word problems and things that you could do to develop your problem solving skills using Python.

Harpreet: [01:17:13] Right.

Harpreet: [01:17:13] So hopefully that was helpful to say, Ali. And I guess we'll begin to wrap it up. So I'll be launching my course hopefully by the middle of September. It's been it's been a long time in the making. I started working on the course some long time ago. Too much longer than I care to admit. But then in the in the works for quite some time. So I do my best with that and do my best to kind of fill a gap that I see in the market, because I don't see a lot of stuff. I can see a lot of a lot of courses teaching you how to use tools. But I don't see a lot of courses that teach you how to think and work and carry yourself as a data scientist. I'm hoping to fill that need

Harpreet: [01:17:51] That that I

Harpreet: [01:17:52] See in the market. And I hope you guys enjoy that. That's it for today. My friends, thank you very much for joining in.

Harpreet: [01:17:58] And remember,

Harpreet: [01:17:59] You've got one [01:18:00] life on this planet. Why not try to do some big cheers, everyone?