FAANG Data scientist reviews: Datacamp, Dataquest, 365 Data Science | Tina Huang

0:00When you need to build up your team to handle the growing chaos at work, use Indeed Sponsored Jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a $75 sponsor job credit at Indeed.com slash podcast. That's Indeed.com slash podcast, terms and conditions apply. Need a hiring hero? This is a job for Indeed Sponsored Jobs. Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate sensing and awareable. So your vital zap now with heart rate variability can tell you when something is off. And your readiness score can let you know when to rest and when to push. Here the story in every heartbeat with Apple Watch Series 12. The features described it for wellness purposes only and not for medical use. iPhone 11 or later required based on Apple conducted study of heart rate accuracy August 2026. Visit apple.com slash apple watch series 12.

1:01Visible puts the ultimate wireless hack in the palm of your hand with unlimited 5G wireless data and hotspot designed to keep you connected powered by Verizon's 5G network. And no contract holding you back. Plan started $25 a month or get the premium visible plus pro plan and save $10 on your first month when you use promo code hack. Make the switch at visible.com. Terms apply see visible.com for details. Hello friends, how's it going? So he'll love from India. Yo, isn't it like 330 in India? That's what I think Sabi told me. India time. Yo, it's like 331 AM. Oh my god.

2:01Thank you so much for for joining despite it being 330 AM. Really appreciate it. How's everybody doing? Rafael Brazil in the house. Hey, Sabi. Oh, you actually showed up. Oh my god. Some in me demoted music plays in background. No sleep. No sleep for you then. It's a game. How's it going? Thanks for showing up. Hello. See, Sabi was a sleeping and got some a 9 a wake. Wow. I really appreciate it. I really appreciate it. I will try my best to make sure that what we talk about today is is worth the lack of sleep. I'll try my best. Alright, so let's wait for maybe like four more minutes or so so we can start around like 505 to see if there's anybody else that's going to trickle in. Let's see.

3:03Sabi, how's life? How is your Saturday? My Saturday was good. Good. I kind of just chill one over. So I actually like one over some of the notes that I made for this livestream. So because we have a lot of stuff to cover. What else I do? I took my dog out for a walk and to the vet because he needed his vaccinations. Yeah, and that was pretty much it. And then I took a nap right before. So I'm all like refreshing. It was him. Tina Summoned you. Mr. X, how's it going? Hey, Farhan. Thanks for joining. How? Midnight in France. Thanks for joining. Really appreciate it. Hey Chris, how's it going? Hello, I'm not going to be a fan. You said hello from Morocco. Hey, do we have at least two Moroccans down? Um, you bring him here too. Luke, Katie just finished popping up popcorn. I'm ready for this. Oh, man, not a pressure now. Luke, yo, you. I have so much pressure. I got to be more entertaining.

4:03Um, you mind. Hey there. Jasmine, hello from Singapore. How's it going? Mohammed, hello from Morocco too. Hey, we have a bunch of Moroccans here today. Bridgette, how is data camp for learning data analysis? That is what we're going to cover today. We'll be focusing more on data science, but. I'll be going through the platform pretty in depth. So I think you will have a pretty good understanding of where data analysis is. Well, hijaja. I'm probably not sure the name wrong. I'm sorry. Hello from Indonesia. Hey, Bernadini. Bernadini. Hello Miss. Are you okay? Um, I'm pretty good. I am. I'm quite okay. Thank you. Alfredo. Hello from Columbia. Hey, Alan went through intensive econometrics project for the past few weeks. Now, bingeing on anime and relaxing. That is the best, isn't it? It's like after you work hard, then you feel, and then you start watching anime and you feel really relaxed because you deserve it. Unlike the one you're just like.

5:03Perkassinating and watching anime. Then it's like not those not feel as good. Mr. X, start. One more minute. One more minute. Let's wait for you to see if anybody else trickles in. Sebi. My first red eyes. If you fall asleep, Sebi. Let me know on Monday. Hello from London. Hey, the money came from public. Yo, we have such a great variety of country representing here. One a.m. in Kenya too. Oh my gosh. Thank you guys so much for joining me. You know what? I said you're recommended next time. I'll put up a poll for a time instead. I'm gonna go behind the actual video because I think that mornings for me might actually work better for most of you guys here. As opposed to 5 p.m. And then all of you guys are like, 3 a.m. Max, how's it going? Hey, love your blazer. Thank you. I'm trying to look up professionals today. I'm like, do I look like more professional than that normal? Martin hello from Northern California.

6:05Wow, Deutchland, Morocco. Oh, yo, this is awesome. And you, you work UAE. Thank you all so much for coming. And Miami pride from South Africa. All right, shall we get started today? All right, so I'm just gonna give like a brief intro what's up today and kind of how this livestream's gonna work. So we have a lot of material to cover, but I do want this still to be pretty interactive. So please leave comments, talk to each other in the livestream. I'll be monitoring it throughout as well. If I don't get to like, if I don't answer your questions straight out, please don't be offended. What I want to do is I after each course, I'm gonna be covering them one by one, but not course, like each like program, I guess, each resource, we're gonna have like a slight Q&A if it would be 10 minutes or so. And in the end, I'll stay behind and we can have more Q&As about just like comparisons between all of them. How does that sound? Does that sound good? But please feel free to keep like messaging in the livestream chat and just talk to each other and stream each other's questions

7:06if you're able to as well. All right, so we're gonna cover today. So I might go for an overview of each of these resources. We're gonna start off with data camp and then data quest and then we're gonna end with last, but definitely not least 365 data science utamies. Three things in my data science, utamie complete data science bootcamp. That's a mouthful. And for each of these, I'm gonna talk about the time dedication, the material. When you need to build up your team, the handle the growing Q&A set work, use Indeed Sponsored Jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a $75 sponsor job credit at andd.com slash podcast. That's indeed.com slash podcast, terms and conditions apply. Need a hiring hero? This is a job for Indeed Sponsored Jobs. Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate

8:09sensing and awareable. So your vitals app, now with heart rate variability, can tell you when something is off. And your readiness score can let you know when to rest and when to push. Here are the story in every heartbeat with Apple Watch Series 12. The features described it for wellness purposes only and not for medical use. iPhone 11 or later required based on Apple conducted study of heart rate accuracy August 2026. Visit apple.com slash Apple Watch Series 12. Visible puts the ultimate wireless hack in the palm of your hand. With unlimited 5G wireless data and hotspot designed to keep you connected, powered by Verizon's 5G network. And no contract holding you back. Plan started $25 a month or get the premium visible plus pro plan and save $10 on your first month when you use promo code hack. Make the switch at visible.com. Terms apply, see visible.com for details. We're going to dive in. I'm going to show you guys what it looks like. The learning style, whether it's project base for knock, because those of you who know me very well,

9:09I'm very interested in things being project based. And I think it's the best way to learn. We're talking about the support system, a help system that's available, the career prospects, and what kind of support they offer on the career side, as well as the cost. And then I'll talk about the pros and cons of each who I think it's best suited for. And some tips if you choose to use that resource, I kind of how that fits into your whole learning journey and what to do next after it, or what to do while you're using the resource, if that makes sense. And I just want to be completely clear with you guys. This is not sponsored by any of these companies. I actually bought data, camera data, quest myself with my own money completely. For 365 data science, I have a working relationship with them already, because I'm making a course with them. So Ned DCO was kind enough to actually give me a link. So I can look at the Udemy course without having to pay for it. But again, everything is completely my opinion. Nobody is telling me anything. Nobody's controlling anything that I'm saying. And I'm not obligated to say anything.

10:11So just get in that out of the way. All right. So also, if you see me looking down at this, because I actually wrote notes, just to make sure I cover everything that I want to cover. All right. Savvy, disclaimer. Yes, disclaimer. Just telling you guys, nobody's paying me to do this. I'm not even getting paid to do this. Even though I do have a couple affiliate links at the bottom. That's just referral codes and stuff, but all my thoughts. All right. Well, it looks like nobody has extreme objections here. And you do have an extreme objection. Do you have any? No. By seeing us, we're also likely it's all good. Let's get started. All right. So first one that we're looking at is DataCamp. So I'll just give you guys a very brief summary. And then we'll jump into the actual resource. So DataCamp is a self-paced data science, primarily data science platform, although they do have data analysis and data engineering courses as well. And it's a self-paced online platform that you go on.

11:12And it's supposed to be like, they'd be promised that if you use this resource, that you're going to go from zero to hero essentially, like you're going to go literally knowing nothing, you don't have to know anything and all the way up to being entry-level as a data scientist, as a data analyst, or as a data engineer. So that's their claim. So they all have, let's see what else I have right here. Blah, blah, blah. Yep, it's pretty much it. OK, let's just jump into the resource. Let's see. How do I show you guys my screen? Good? All right. So I think you guys can see my screen now. Let me just jump to here as well. Yeah, so this is what it looks like. This is just the dashboard they have. Ignore all the other tabs that I have open here. It's mostly just to make sure that I'm going through this sequentially, and I don't have to find stuff. But yeah, as you can see, I already started doing some of these.

12:14I'm working through some of these myself, just kind of getting a feel for it. I've been testing it out for around the past couple weeks or so so that when I'm doing this, I have already gotten a grasp of what it feels like. All right, so this is what it looks like. So first, it's very nice and clean, very pretty looking, I must say. And here we're going to look at the career track. So I'm most interested in data scientists with Python here. So that's what I'm going to start off by looking at. So they have a bunch of other ones, like our programmer, data science, our data analysts with our Python programmer, a bunch of different stuff. Like there's a lot of tracks, and there's also a lot of courses as well. Well, let's start with the data scientists with Python track. All right, let's continue to track because I already started. All right, so just kind of like a quick overview of this track. It's in Python, it's 88 hours, 23 courses, and you get six projects out of it. So it says it's 88 hours, but realistically, I would estimate it would probably take you like,

13:18I'll say like 10 months. I think 10 months is appropriate because working through everything, like working through the video lectures, working through all the projects and stuff is going to take you some substantial amount of time. And as you're going through this, I'll talk and do this a little bit further. But only you should just like exactly follow everything and then just like, you know, I'll be like, all right, I'm ready for a job now. Because there's other things that you should do afterwards as well. And while working on data count as well. So I think honestly, like to go from zero to hero, I would say like around 10 months is a good bet here. So okay, let's just quickly cover through each of the courses and then we'll like kind of double click into what it actually looks like, what it feels like. And then also like looking at some of the projects and stuff. Okay, so first, like, you know, they go from introduction to Python. Oh great, I scored 113. See, gamified. It's also very gamified, which I actually kind of like. So then intermediate Python, then we have a project over here and then data manipulation with pandas.

14:21Oh, they generally have like an assessment and a project every like one or two courses afterwards. And then we have joining data with Python with pandas or projects here, data visualization. And then they talk about Python data science toolkits where they talk more about like how to actually use Python and using the function things like that, object-oriented programming wise, but in a data science perspective. So yeah, part one and part two. And then we have data visualization with seaborne. And then importing data into Python, I thought I actually get your data into Python and how to clean it. So intermediate importing data into Python, cleaning data. When you need to build up your team to handle the growing chaos at work, use indeed sponsor jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications and more, spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a $75 sponsor job credit

15:21at and d.com slash podcast. That's indeed.com slash podcast, terms and conditions apply. Need a hiring hero? This is a job for indeed sponsor jobs. Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate sensing and awareable. So your vitals app, now with heart rate variability, can tell you when something is off. And your readiness score can let you know when to rest and when to push. Here are the story in every heartbeat with Apple Watch Series 12. The features described are for wellness purposes only and not for medical use. iPhone 11 or later require based on Apple conducted study of heart rate accuracy August 2026 visit apple.com slash Apple Watch Series 12. Visible puts the ultimate wireless hack in the palm of your hand. With unlimited 5G wireless data and hotspot designed to keep you connected, powered by Verizon's 5G network. And no contract holding you back. Plan started $25 a month or get the premium Visible Plus Pro Plan and save $10 on your first month

16:24when you use promo code hack. Make the switch at Visible.com. Terms apply, see Visible.com for details. Working with dates and time, this is something that's gonna come up a lot if you're doing like time series stuff. Writing functions and Python, like exploratory data analysis, statistical thinking, they have a couple of courses on statistical thinking. It doesn't cover like the ML statistics. It's more just like hypothesis testing and just like general stats that you kind of need like confidence interval and things like that that you need as a data scientist. And then after that, they end with machine learning. So they start off with supervised machine learning using scikit learn, unsupervised learning machine learning with tree based models in Python and then cluster analysis. And that's it. Then you get your statement of accomplishment. So it's pretty comprehensive. It really goes like all the way like starting with Python and just building it up and going through like all the steps of mostly a typical data science project. I'm just gonna stop right there and check the stream. See if anybody has any comments over here.

17:29I'll talk about the cost. I'll talk about the cost like in a bit. Like I'll, yeah. I'll talk about the cost structure. This is subscription based model. Yeah, they do have sebi. They do have separate tracks with our in Python. Yeah. And they also split between like data analysis as well as data engineering and data science. And sebi at 10 months need reasonable. Yeah, I think it would honestly take here on 10 months or so. It's, I max like data come is gonna take about 10 months for those who can't dedicate full time. I feel like, yeah, if you're like dedicated to a full time, you can probably knock it out in like five or six months. I want to say we're like a little bit less. It's just that there is a lot of material to cover and it's not like you can just like go through data cam and then you're done, right? Like there's like other, it's not just like watching the videos and doing the projects like a lot more to it as well. Sebi, you get a six months of data cam access to be used to the tools of GitHub. I think there is that one as well. I'm not a student anymore but I don't notice specifics about that.

18:31All right. So shall we jump into now? I wanted to talk a little bit more like kind of clicking and actually show you guys what one of the classes feels like and then also looking at the projects which I am pretty excited about. Let's just start off with introduction to Python. Why don't we? So let's go here. Okay. So the way it works is that I think I've done like a few already but it's actually video based. So you know, you have like a talking head video, you have a guy talking about like, oh, this is Python. You know, these are things you can do with Python. And it's like throughout the, throughout the courses, it's going to be the combination of videos as well as multiple choice questions and then just like very hands on based stuff as well. So you're like, okay, we've got it. And then you can see like it really starts off with like, hey, like this is the Python interface. You don't have to set up any environments which is quite nice. You know, sometimes it takes a really a lot of time and you run into a lot of bugs and setting it up. It's just like interactive. And then what you do is like you just do like whatever is

19:31over here and then use some major answer and you run the code. And then you kind of just have a combination of this and each time like when they introduce a concept, it's going to be like video based. So it's going to tell you about stuff and then you immediately get to implement whatever it is that you learn in that video. And it's pretty much like that throughout the entire thing, like throughout the entire track. You want to call that like all the courses. It's all pretty much based like that. So I go back, try to go back. I cannot. I go back to my dashboard. Okay, back on and I guess that's probably motivating as well. You're like, oh, like you know, I'm so close. I should probably go back. So they have like these kind of in-build little motivation triggers and stuff which I think is quite nice. Like if that's if they like playing games and having points and stuff is your thing, I think it's quite nice. Go back to where we were, which is career track. Yeah. And then I also wanted to show you guys like,

20:32for example, after this, you complete the course, right? And then it's like four hours. And then after that, you go and do an assessment. So I just kind of started off the assessment, you know, try to out like a few questions already. I'll show you guys what it looks like. Then you have like assessment like this. It's like, it's like fill in the blank. Like I don't know how personally, I don't think it's very, I don't really like that. It's a very fill in the blank base because fill in the blank, it's like, it's there's like a huge emphasis on syntax. And just because you notice syntax doesn't really mean that you know how to code. And it kind of puts the wrong emphasis in my opinion on like syntax as opposed to concept. But with that being said, I think it's nice that you know, you do get your practice here. So I guess in this case, might as well just do it. So define a function named hours to seconds. Okay. Like dev here and then put hours, right? And then you just really like submit answer. I guess, yeah, they don't tell you if you're right or not until you finish everything. But yeah, that's kind of what it looks like

21:33in terms of the assessment. Checking back to see if you guys have any questions, worried comments. Oh, also thank you mods. Thanks, thanks mods for being here. Please, please keep doing your mod roles. I appreciate it very much. So I boomer. I sure name. Sorry. So I boomer, doomer, low IQ, not a statistic. I agree with that completely. That's actually one of the cause I'll be talking about later. I think there's not enough like mathematical emphasis or at least like logical emphasis. It's very much of a sampling of different things. And it's very like coding based. Let's see. All right. So Bimal, I'll be talking more about like, you know, after we go to each of these platforms, it will be doing more of a comparison between them. So please stay tuned. All right. So I do want to show you guys like, you know, not that we saw the lecture. I want to show you guys what a project looks like as well. Because I think projects are the best way to learn. Yadia, yadia, yadia. They're amazing. This is how you actually. When you need to build up your team

22:34to handle the growing chaos at work, use indeed sponsor jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more. Spend less time searching and more time actually interviewing candidates to check all your boxes. Listeners of this show will get a $75 sponsor job credit at indeed.com slash podcast. That's indeed.com slash podcast, terms and conditions apply. Need a hiring hero? This is a job for indeed sponsor jobs. Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate sensing and awareable. So your vitals app now with heart rate variability can tell you when something is off. And your readiness score can let you know when to rest and when to push. Here are the story in every heartbeat with Apple Watch Series 12. The features described are for wellness purposes only and not for medical use. iPhone 11 or later require based on Apple conducted study of heart rate accuracy August 2026. Visit apple.com slash Apple Watch Series 12. Visible puts the ultimate wireless hack

23:35in the palm of your hand. With unlimited 5G wireless data and hotspot designed to keep you connected, powered by Verizon's 5G network. And no contract holding you back. Plan started $25 a month or get the premium Visible Plus Pro Plan and save $10 on your first month when you use promo code hack. Make the switch at Visible.com. Terms apply, see Visible.com for details. If you like, you know, get to simplify your knowledge. So in terms of projects, they have guided and unguided projects and let me show you what the guided project looks like first. So I'm showing you the first one, but I kind of click through all the different projects. They're all pretty similar in structure. They just kind of get a little bit more complex as as time goes on. But it's pretty much like, it's very, very kind of like they really, really hold your hands here. It's like, you know, you have your Jupyter Notebook and it tells you exactly like first, we need to import pandas and then we do this. And then here's the instructions for everything that you do, right? And you can also take hints with you on. And then if you actually notice here,

24:37everything is like filled out for you. So you just like, you know, you're like import pandas as PD. Like that, like it's very, very like guided here. And then it kind of gives you the next task. Like, oh, like if I'm done with this one, then you know, you click next task and you go on and eventually you complete the entire notebook. So that's about what the guided one looks like. I think if you are more like a, I really coded it all before, kind of like nervous starting off, I think it's quite nice that they do it so guided. Like it's very, very handheld. Let me also show you guys like what the unguided ones look like. For all of the guided projects, you can also do it unguided. My recommendation honestly is that you do it unguided. If you can, maybe doing like a couple guided first and then do it unguided. Because let me show you like the unguided one isn't actually super unguided either. It's actually still pretty guided in my opinion. Because it's the like first you need to create a map potlips scatter pot. And then you do like these things and you want to exactly make it red,

25:38exactly make it orange and make it like green. And you know, do all these different things. What you notice is that the only, it tells you like exactly what it is that you need to do. It's just that they don't like pre-fill the cells for you. But it's still like pretty guided in my opinion because it's like telling you like, oh, these are the exact things that you need to do. All of them reality like most projects are gonna be more like much more open-ended, right? You know, here's some, here's some data. Well, yeah, I feel fine with your own data and then you're trying to figure out some insights from it. But here it's still like very, very guided. Hold that means I'm good. All right, see if we have any comments or questions. View, new, data cameras felt too easy. Yeah, I can definitely see that. I think it's really tailored where it's people who just like both have no experience and are more like, I want to say like more nervous and they want to do things from like round up. So that is, I can definitely see why you would think that. Elon Musk, how's that on-guided? Yeah, I agree. I feel like it's still very, very guided.

26:38I think by on-guided, they pretty much just mean things are not filled in for you already. Like it's not exactly filled in the game, the fill in the blank. So I think it's still like pretty guide us. If you're gonna use data camp, I think you might as well just do it on-guided and just check your answers as opposed to just fill in stuff because that's like not very, like just you fill in everything you probably like still don't actually know. Like I gave you a data set, even though you did the guided one, or even on-guided one, you probably still have a lot of trouble like knowing how to approach it because you don't really get to like think of what it is that you need to do by yourself. Richard, not including any tracking access to have low courses from skills tab. Yeah, for sure. They also, this is just the data science tab. There's also like skills tab where there's actually like projects, start out projects. Like courses on a bunch of other stuff that you wanna do, that might not be covered under a computer or track. Cool. All right, so next off, what do I want to talk about? Just making sure I cover everything here. Ah, yes.

27:38So I wanna talk about, now that you kinda have a feel of the plahorn and what it looks like. So this is pretty much what it looks like. You have your progress, your groups, all of these things, and you have your assessments. And another one that I wanted to bring emphasis to is they support system that they have. So they do have a slot community over here. Let me see. I'm pretty sure I already joined a slot community. Let me just like do it off screen, just in case I reveal my password on someone's word. Let me give you a second. I just wanna show you guys what it actually looks like. It's telling me to select bicycle right now. So I'm just, I'm just selecting higher hygrids and whatever. Okay, we can come back to that. But I honestly just tell you, I'm sure most of you guys know like what, oh, there we go. It worked. So this is what it looks like. It's a slot community. It's really quite active.

28:38You can ask questions about like, oh, like, you know what this doesn't work. Like this someone will come back and help you. So it's very, very active there. There's also like jobs, but they do have like a, they actually recently added like a job, oh, job hub, I think they call career hub as well, but they do have a job postings as well. The people lists where they hire directly from people from Data Camp. Right, which I think is quite nice. I don't think it's like as active though. Like I don't know like how many people I should manage to get jobs from this. But it's nice that it's there. So yeah, you can ask questions and people will get back to you. Cool. And then in terms of like careers and prospects, I do want to like touch on that a bit. They have like a new career hub. I think this was actually like really recently added. They didn't use to have this, but they do now. It's quite interesting because, okay, firstly, you have to like make sure you actually like know everything. So I didn't complete this because there is like, you actually have to like finish stuff first, I believe, and then you know, you have to do all of these things. I didn't finish it myself. But what you have to do is I do each of these assessments

29:41and just to make sure that, you know, how to do stuff. They're making sure that you actually, you know, are at a point of entry level data scientists, or data analysts, or data engineer. And then afterwards what they say is that you get into your ready good resume reviews and once you want sessions with our experts. So that's how that sounds pretty good, right? So they actually give you one to one sessions and the review resume. When you need to build up your team to handle the growing chaos at work, use Indeed Sponsored Jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a $75 sponsor job credit at and D.com slash podcast. That's indeed.com slash podcast terms and conditions apply. Need a hiring hero? This is a job for Indeed Sponsored Jobs. Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate sensing and awareable.

30:42So your vital zap now with heart rate variability can tell you when something is off. And your readiness score can let you know when to rest and when to push. Here's the story in every heartbeat with Apple Watch Series 12. The features described it for wellness purposes only and not for medical use. iPhone 11 or later required based on Apple conducted study of heart rate accuracy August 2026. Visit apple.com slash Apple Watch Series 12. Visible puts the ultimate wireless hack in the palm of your hand with unlimited 5G wireless data and hotspot designed to keep you connected powered by Verizon's 5G network and no contract holding you back. Plan started $25 a month or get the premium visible plus pro plan and save $10 on your first month when you use promo code hack. Make the switch at visible.com. Terms apply see visible.com for details. For you to help you on the career chat. So this is still in beta. I would love to like keep an eye out on what happens afterwards. Although like I want to say truthfully, like I wouldn't really rely on data camp to get me a job.

31:45I'm not going to be like, I'm going to do data camp and then I'm just going to get a job after that. Like I wouldn't rely on myself. In fact, in my opinion, data camp is more like a starting point like where you start to learn a bunch of, you start to learn about data science, start to learn about just kind of like all the aspects of data science and going, you know, just like sampling here and there, but it's not really like comprehensive in my opinion. And it doesn't really, it claims I get to do the point of actual data scientist. In my opinion, I'm not so sure about that. I think you do need to do more things yourself. You've got to do more projects yourself to build your portfolio up more for people to actually like, look at you more seriously, even want to hire you as opposed to just doing it. So that's past kind of my opinion on that. All right. Going on to cost. So I have it open here. All right, so this is the find your plan, your personal plan page and they have like your personal file. You have free, you have standard, you have premium and they have the business ones.

32:46I'm assuming most of you guys are not interested in the business ones. So the free one, it gives you data camp a driver free, but honestly, it doesn't, like if you get a free, pretty much only get the first chapter of each course. So you don't really get to like learn anything. And I think you get to like see only the guided portion of one project. And that's really kind of, you know, where a lot of the theoretical stuff, like that they teach you and really like really all the things are coming together. So like that's really just the, it's really like a, what does it call like a lead magnet, I guess, like to get you to go buy something to go and go and buy something. So I personally bought the standard one, which is like $25 and then they also have one that's like $33 per month, which is the premium for learners who want to access all projects. I wouldn't recommend it just because the way that their projects are, even the guided ones feel, like unguided ones feels very, very guided and they're not really like realistic. And you put on your resume as well. People are not going to like, it's not particularly unique or interesting. So I would honestly just go with the standard one.

33:47And then after going through the projects and going through data camp, I would just go and do my own projects instead, like applying all the stuff that you learn and from the projects that you do have, you've done a data camp which is applying that to other data sets and things like that. That's kind of how I would go about it. We're not recommended the premium. All right, see, check it back on your livestream. See if we have any comments or questions. Max will build an only. Yeah, it is, right? So I honestly, I'm probably going to cancel personally. I'm probably going to cancel like after this month. Just because like for me, I don't really think there's a lot of stuff in there that would be beneficial to me. I kind of look at the courses and stuff and I either know most of it or I'm not very interested in learning about stuff. Like, yeah. But it is, yeah. I mean, it's subscription based. If you do a pretend month, you're looking at like $250, right?

34:48Which is, I don't think it's that bad. Like, I don't think it's that bad. I think it's not bad at all. But it is, like, it is subscription based. Let's see. Sorry, I'm kind of just scrolling through because I feel like I missed off a little bit. I see I'm going to scroll through to make sure that I'm getting those questions. That can mean I found Datacamp had an issue of learning that I'm forgetting. If you learn a lot of Harvard's easy to forget, will you just learn because of its generous handholding? I agree with that. I can definitely see that happening with Datacamp. It's very much like, it's like kind of filling a blank stuff and even the projects because they're so guided. It's like, oh, I know how to do these things. So it's really, really up to you to actually take what you learn and do stuff with it. Like, you can't just claim that, oh, I have like now become, I have now become a true data scientist I don't need very that real. Yeah.

35:49Sabi, thanks Tina for letting us know you're not a robot. What are you? When did I say I'm a robot? Oh, oh, when I was like clicking through stuff, yeah, I'm probably a robot because I failed. It was like, click the fire hydrants and I couldn't fire the fire hydrants. So I guess I'm a robot. Damn it. Duck me for using Datacamp, please do a lot of projects in your own time to how I remembered a thing's you heard. Very much a great, I'll emphasize that as well when I talk about what my tips are for using this resource. All right, let's just see if we have any other questions of things. Let's see. Shuhub, do you think we should go for Datacost? We will see once we get to Datacost. I'm being mysterious here. Sabi, why would they show her month if it's built annually? Where's the logic? It's a marketing thing. I don't think they're being deceptive by doing that.

36:50I think most places do that. It's just kind of like a marketing thing. It's like if they tell you a huge number, then you're like, boom, but if it's like, oh, it's only $25 per month, it's like all, that feels more reasonable. It's not that it's not $25 a month. It is $25 per month. You're just built annually for it. Yeah. I think the character you have a lot of typos. I have it for data, guided search, and guided projects and slides. The ML courses are decent. Yeah, I think the ML courses that give you a pretty decent sampling of it, it doesn't really go too much in depth. And I agree with the previous comment of it not having like too much, like how do I say it? Like it doesn't really show you the logic behind it. Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate sensing and awareable. So your vital zap, now with heart rate variability, can tell you when something is off. And your readiness score can let you know when to rest and when to push. Here are the story in every heartbeat

37:51with Apple Watch Series 12. The features described are for wellness purposes only and not for medical use. iPhone 11 or later require based on Apple conducted study of heart rate accuracy August 2026. Visit apple.com slash apple watch series 12. Visible puts the ultimate wireless hack in the palm of your hand. With unlimited 5G wireless data and hotspot designed to keep you connected powered by Verizon's 5G network and no contract holding you back. Plan started $25 a month or get the premium visible plus pro plan and save $10 on your first month when you use promo code hack. Make the switch at visible.com. Terms apply, see visible.com for details. It's more like this is how you use it and you know here are the things that you can do. Speaking of which, let us move on to pros and cons. All right, I like to wrote down some stuff here as well just to make sure I don't forget. So do bear with me here as I'm looking through my notes which with my terrible handwriting

38:53that I can barely read. Okay, yeah. So okay, let's talk about the pros. I think it's nice, I just like gamified system. You know, the platform is really nice. So you guys pop them again. That's what you can see. It's really nice and streamlined and it has like a very like modern feel to it which I appreciate. And it's also instant feedback and no setup. I think this is the reason why these like paste like these self paste like online platforms are really popular and a good reason for that is because they give you instant feedback, right? And a lot of times like when you set up environments and stuff, it can be really stressful. Like I still think setting up environments and just getting all your like things set up properly and not get errors is probably one of the hardest parts of coding in general. So the fact that as a beginner, if you have to deal with that, you're solving running to errors, it's really easy to give up but because it's not there, it's like you're already done for you. So I think that's quite nice as a beginner. Also very handheld, that could be a pro work on depending on the kind of person that you are. If you're like more of a nervous person and you like that, then I think this is a great option.

39:55And another pro is that they do have projects and I really appreciate that. Like the fact that they do have projects and then they try really hard to make the projects kind of realistic. Like the projects themselves are realistic. Like sometimes the stuff that they ask in their projects, I feel like they're not as realistic. But like the questions they ask is pretty, pretty good, I would say. And just like the flow of it itself is pretty good, even though it feels a little bit generic. Which is, I think it's kind of hard to make a non-generic to be honest, but the fact that they have projects, I think that already is like miles apart from a lot of other courses and a lot of other platforms that aren't necessarily project focused. Also they do have like seminars and they have that career tab that's available now to get the individualized career counseling and at $25 a month, although build annually, it is quite nice, I think. So I think you're getting pretty good, you're getting some pretty good stuff for that price point. So talk about the cons, all right?

40:56Also let me guys know. You guys let me know what you guys think about what the pros and cons are. And for those of you that have like tried it out, I know some of you guys have already mentioned it, but like kind of love to know like where everybody else's opinions are as well. So in terms of cons, so this is what I think DuckMe was the one that mentioned it, or was it soy boomer-dumer-lo-i-cue? Still love your username. It was like not this, in terms of the ML algorithms, and just in general, with the stats and math, really wasn't that great. If you don't already know stats and stats, and you're not just using as a refresher, I think you're gonna, you don't really cover it as much. I would supplement that with something else. Also with the algorithms is very much like, this is how you cycle learn, this is how you import a model. It doesn't really go into too much detail about how things actually work, from like a conceptual point of view, not even like mathematical, from a conceptual point of view, they don't really go into too much detail about that. Also just in general, feeling the blank, I don't think it's very realistic.

41:57It's somebody else, that me and I think mentioned, you would learn a lot of things, and you just forget about it. Because it's just, when you're actually coding, you're not feeling a blank at all. It has to call, confirm here, and they kind of cut that part out in their projects and in their assessments. Also, some other cons that I found, there's more like, they don't really cover APIs, we're web scraping. Web scraping, I kind of get that. If you don't cover, it's not that hard to pick it up yourself, but like the API part. I think it was really important. Because like a lot of data science, just like coding in general, it's gonna involve you using a lot of APIs. And they don't really cover that. They do cover like a little bit in terms of the integrated insight, but they don't really have a second way to get it dedicated to it. And I think they really, really should. It's such a big part of what you do as a data scientist that I think they really should have like actually an entire course dedicated to APIs, in my opinion. What else? What else?

42:57Oh yeah, and another point I had is that, this is kind of like a minor knit of mine. So maybe it's not that big of a deal. It's like they have different lectures for each of the courses. And some of them are good. Some of them not so good, I would say. Like they're not bad, but they're just like not as good and the flow of it doesn't flow as well. Because it just doesn't like the teaching style and these things, they don't really like mesh together very well sometimes. And because there is like that video emphasis to it, I definitely noticed that when I was going through a different courses. Let me know in the comments what you guys think as well. Ooh, good night, Resort. Thanks for joining in. I know it's like freaking for you. I'm really appreciate it. Maybe you can check back in in the morning as well and see if there's anything else. I mean, there will be more stuff, but check back in in the morning. See any pros and cons that you guys have. So pros, wide range of things to learn. You can learn a wide range of topics really fast from duck meat. Agreed, definitely agreed.

43:57See, so if I'm missing your comments and stuff, I do apologize because you're just like, a lot of comments I'm trying to scroll through. Let's see. One of my repeating. Let's see. All right. Guy, Guy and the Mad Joe FTW, Phil and the blank would be better in an exam for him to see us course at a university. I feel like just Phil and the banking general, which is not very, I just don't really like that very much. I just don't think it's realistic at all. Luke Burrows, I like to diverse selection, and courses that niche topics from data cap. Agreed, agreed. So there are like specific topics, I'd have a tab a little one, and there are ones are supposed to be really, really good as well. I kind of like looked into them a little bit, but I didn't go through them as thoroughly as this track, but definitely agreed on that. Herman, most online courses don't provide enough math knowledge when it comes to data sciences, not bad.

44:59I think it's a, okay, because the thing is like there has to be a balance, right? Like there's the balance between like, feeling like your instant gratification is really nice, like you're coding stuff, and you're really seeing results, has really motivating as a beginner, but at the same time, like that math knowledge, like well, he's like logical conceptual knowledge, it does need to come. So I think there has to be a balance between that. In this case, I feel like it's a little bit short, and you have to go and do a lot of work yourself to kind of like, pad that on. But I am, I kind of like, yeah, I wish that there was a little bit more here, or at least like tell people here the things that we covered, I hear the things that we didn't cover as much, so you should delve deeper into it yourself. I think that would have been nice, and I think people would appreciate that. Lonely sermons, all courseifies are the same. Yeah, pretty much, it's a very competitive marketplace, and I gotta say, all the things that we're covering today are very, like they're all very well done, right, for what they're trying to do. Tino, how can you be my mentor? Yeah, I mean, I would love to.

46:02I just don't have that much time right now, I'm afraid. Although I'm working with chronic odor people, just to like, you know, too to like, I'm like thinking about starting a little bit more official program for mentoring. All right, so shall we move on to, okay, so I, some concluding words, I'm also just keeping an eye on the clock. Here, so we're like around 38 minutes in, so I'm just gonna move on to, who I think this is best suited for, and some tips if you do choose to use this resource. Okay, so what do I have right here? Okay, so people who I think this is really good for is it's good for people who really appreciate that hand-holding, like I said previously a few times already, like if you're more nervous and you're really starting from scratch, I think you would actually appreciate that hand-holding and that gratification that comes from that instant feedback. I'm feeling like maybe not so great, but I appreciate the fact that they do help you like once that other time. It's also good for people who are looking to up to up skill

47:03as opposed to like, you know, just become a data scientist from scratch, for example, because they have that skills track. But let me show you guys again, what I mean by that. They do have that skills track. That you know, they cover our programming, there's Python, SQL, there's Shells, spreadsheets, Tableau, and Ethereum as well. Like there's a lot of specific, and there's finance fundamentals as well. But you know, if you're looking to up skill your ready-to-date analysts, for example, where data scientists and you're interested in like specific areas, I think it can be quite nice. Like for example, if I only know Python, and I wanna learn about R, you know, I can just take a few of these, so I can get started on them. Although me personally, I think you've been learning additional language. I don't actually need this anymore, because learning one language is pretty easy to learn something else. But okay, like say like, I don't know spreadsheets. Then I can be like, oh, like I can just learn, you know, spreadsheets here. So it's nice for up-skilling, so it's not necessarily just dedicated to career switchers. And finally learning style, if you like talking, had videos kind of thing,

48:04like data camp is good. You're gonna like data camp along, because that's kind of the style that they go by. And we look at data class later, which is really quite similar, you would say, like, but if you actually experience it, they're actually really different. And a lot of it is because we learn style. So some tips if you're gonna choose this resource. So first off, like I really, really want to emphasize, I don't think you should see data camp as a comprehensive platform. I know it kind of tries to market itself as such, but I don't think it is. I think it does a great job in terms of introducing it to it, and all the skills and just giving you a really great basis to work off as well as a career chat, and all these different things. But I think you still need to rely on yourself to actually take the things that you learn and apply them to projects. You can start off with Kaggle, if you like, even better, if you start doing more real life projects, I have some videos that are about that you can check on my channel. But like you really need to start applying these skills and actually making your own project, doing your own portfolio. That's gonna be how you go from just like that entry,

49:06just like starting off to actually like being employable or really like being someone that companies would want to hire and really like also cementing your knowledge that you learn from data camp. So that's like my first tip. My second tip is that if you're gonna go through data camp, don't be like, I must take notes on everything a little thing like, you know, like because I especially want to say this for data camp because it's so syntax focused. Like all the spreadsheets and stuff and all the courses are like very full of blanks. So it kind of makes you feel like, oh, like I need to remember all the syntax. I need to remember, you know, how to do all these things and you might be like taking notes about and try to remember them. And I think that's, that's because all of our really wrong signal. Like in general, when you're coding, you're doing data science and just like technical things in general, like, you're not really going to need to remember to syntax for anything, right? Like if I like literally don't remember the syntax for like really basic, pipa thing. So like for example, when I'm like trying to do a filter, like literally forget how to do a filter every single time and have to go look up the documentation for it. And that's kind of how it is. Like you're constantly going to be looking stuff up because the syntax doesn't matter.

50:06It's more about the knowledge and how to apply it that matters. So highly encourage you to not like get so caught up in the details and the syntax of things. Yes. What else? That's bad. That's about it. I think that would be like kind of my specific advice or data camp as a resource. All right. Let's check back in the comments. We're going to be moving on to data class next. Let me know if you have any feedback as well. Do you think the way that I'm approaching this, like going on data class and then 365 data science? I do think this is a good, am I covering on the things that you guys want to see? Do let me know now before I get started on data class. Also if you guys have any other like additional comments for questions on data camp, I think we can spare like maybe three or four minutes or so to cover that before moving on. Mm hmm.

51:12See. The only see I think I would earn a lot of Tina start some online course. I'm doing a SQL interview course. That's that's something to work for now. Wait, what is it that we want to know? Sorry, I'm like missing people here. Oh, I just by the way, I just wanted to like guys know. So I do. I'm sorry, I forgot this one. Super chats and the custom emojis enabled. I got feedback last time when I was doing the data, data analyst course like live stream that people really wanted me to have that open so that they're yes, it does cost. I think it can send me like a few cents for something. You know, but it's able to like, you know, prioritize your questions. So people ask me to open that up last time. I just wanted to let you guys know that is an option. But please no pressure at all. I promise. All right. I see.

52:13What is it that we want to know? Okay, so Manish, why data science courses doesn't have data structure and algorithm which is most asked to interview? I think there's a misconception that people ask you a lot about data structures and algorithms. I don't think you they, at least from my experience in the people that I know in the data science field, they don't really ask you that much about data structures and algorithms. Like you might be looking at some like lead code easy questions if you're into the lead code world. But data algorithms may be a little bit, but I've never actually in a data science interview seen people ask you about data structures outside of just like simple arrays, like hash tables, like the dictionaries of your, your Pythonic. I really don't see that very much. And the reason because it's because in data science like you don't really use those data structures that much. You might use it like in terms of like under the hood or maybe you're iterating through something. But most of the time the stuff you're using, you're not directly using data structures so they don't really ask you that much and interviews from my experience at least. Let me know if somebody else disagrees.

53:16Okay, so far so good, so far so good. Okay, everybody seems quite pleased. All right, you could try using slides. Let me think about that. I feel like if I'm using slides, I don't think it's like butter, if I just showed you directly all this stuff or you think like if I just like how to slide, that might be better. Let me know. Okay, well, everybody seems to be happy with me right now. We're glad. All right, so answer me a few more questions. Like why, why did a send, oh, I know already answer that one. Okay, pride, are you not expecting to know these synths? I think you mean syntax in a technical interview data science. That's an excellent question. I want to answer that. So in data science interviews, you, yes, you do need to know the syntax in a way because when you're actually coding, you can't just be like, oh, I don't know how to actually code. Then we just write stuff down a pseudo code. Like yes, you do need to know the syntax,

54:16but it's so much more important, that syntax will come to you. What if you actually practice doing projects and stuff, like you know how to do these things, like you know the syntax behind it. Maybe you don't remember the exact, like you know how the arrangement of how like functions work and stuff. And even in interviews, maybe forgot that, they're not going to be like, oh no, we're going to fill you because you forgot, like which arguments exactly go into this function, you know? Like you, they're not going to ask you stuff like that. So as a beginner, like that, if you've focused so much on the syntax, that's the, you're going to waste so much time just remembering the syntax as opposed to learning the actual stuff, right? Like actually like implementing things. So I really like say like syntax don't, don't, don't, don't focus on that. If you do have an interview, just brush up on the most common functions. I just try to remember them before your interview. And that should be fine. Let's see, no slides, no slides. Okay, then I'm sorry, we got, we got, we got, we got, I guess we're going with no slides.

55:17Democracy here. Let's see. Tina, what's your paint on Jolmatex course? I don't know, I haven't lived at Jolmatex course. I'm afraid. Oh, yeah, thanks everyone for answering. McFlurry McFee's question. Yeah, don't want to try to come and get me. That's why I don't reveal it. Not because I don't want to tell you guys. All right, show me, guys, show me, show me, move on. If nobody else, I don't see people having any more questions specific things. So I think we should move on. Sounds good. All right. Okay, let us move on to data class. I'm going to follow the same structure. Again, feel free to keep commenting, answering questions. I know a lot of you guys here have looked at these resources yourself. So please help each other out as well. All right, so quick, let me, let me change my thing. Sorry, where's my, where is my, okay, here.

56:21Okay, so I'm going to give just a brief overview of data class as well and then jump into the data class platform. So data class on the surface seems very, very similar to data camp. It's also a self-paced online platform and it focuses on career tracks as well in data science, data engineering, as well as data analyst. And similar, it's quite similar as well in the data camp that it says that, oh, like if you go, you can start off with like absolutely nothing. And if you complete all of this, then you're going to be able to be at the level of an entry level data scientist, really data analyst, with data engineer. That's kind of the claim that they have as well. So let me actually show you guys the platform now. Okay, let's see. So this is what data class looks like here. I have an open to the career track, which is like data scientist in Python. And this is like, this is the thing, like in the beginning when you sign up for data class,

57:21they actually go like, oh, like what are you interested in? Like what are you interested in? Or data engineer or data analyst or data scientist or you're interested in getting a job and whatnot? And then they kind of tailor it to path. So as opposed to data camp, that was more like, oh, like, you know, here's like kind of the path that you can choose, but there's also a bunch of these skills. Data class is very, very career focused. So I'm on the current path of data scientists in Python. And let me now just kind of in terms of time dedication, I wrote this down as well. So it's going to be slightly less time in terms of estimated amount of time compared to data camp. It's 68 hours, we're 240 hours to 68 months. And 240 hours to complete for a data science track and for data analyst track. The estimated 160 hours were like, four to six months to complete. So I would say the little bit, it's a little bit less than on the data campsite. So yeah, I was saying it's very like career focused and it doesn't really have any courses that are like specific to each skill set as opposed to data camp. Okay, let's just go through it quickly

58:22as an overview as well, just what it looks like and what it covers. So okay, we are on the current path and step one. So this is Python for data science fundamentals. So it kind of goes through all the fundamentals for Python and then you also go through the intermediate stuff. And then you have step two pandas and Numpy fundamentals. So this is Numpy, this pandas also make an out all note that after each of these courses, you get a guided project specifically about course. And the data visualization fundamentals, a story telling, I love this episode of story telling like I love the fact that they have this section because it's such an overlook section when you're actually at the job. So I love that they have that data cleaning analysis, data cleaning and Python advanced. And then a project walk through love that as well. And step three is elements of the command line. So they actually unlike data camp, they do through the command line. So here's the command line text processing using it, which I think is quite nice that they introduce that to you.

59:24And then the SQL is embedded within it. So in data, data camp, they have SQL courses as well. It's not embedded into like the career track. So you can always take that as well. But for data past, the SQL is actually embedded within this data scientist and Python course. So they give you these SQL fundamentals and kind of go through like just like fundamentals of what it is. They don't spend like too much time on it, I would say. They do like kind of intermediate SQL, but they don't spend like a significant amount of time on it. But I think it's really like enough. Yeah, I like kind of click through them and I really do think that it's enough. And then this is what I really like. But this is a lot of APIs and web scraping. So working with APIs, intermediate APIs, they have an actual challenge where you're working with a red API and scraping. So that's really nice because a lot of your work is going to be using APIs. And then they have a data analysis and business so like different metrics and stuff. Again, emphasis on those soft skills. And then after that, we have probability and statistics. So go through statistics fundamentals over here,

1:00:26statistics intermediate, and probability fundamentals, and conditional probability hypothesis testing. I think in terms of stats, this is a good basis. I think there are like some things that they don't really copy here. And I kind of wish that they covered a few more things in more detail that would be useful. But to their credit, I think they covered everything that is kind of the very fundamentals of statistics here. They have a probability, they have a condition of probability base, and they also have projects as well. So really like that emphasis. Because I think a lot of times on a statistic side of things, it's really difficult to make it as interactive. But I really like how they actually made the statistics come to life by showing you how it's been used in a project. It's really appreciated that. And then for step six, machine learning intro. So first, we're going to go with canyons neighbors. And then as you go on, it'll tell you about all the different cross-validation and how to add that to the hyper parameters.

1:01:27It goes through some of the calculus. So they do try to cover some of the math behind machine learning, not just tell you exactly, no, this is how you code it and stuff. So I appreciate that. Is it enough really for you to understand what's happening? Probably not. I want to say, but I think it gets you to a point where at least you know what you don't know. So you can easily look up what you don't know and look into that yourself. If that made any sense. Linear regression for machine learning, machine learning and Python. And it starts like predicting the stock market. So you have projects that are based upon these as well. And then finally, with decision trees. And then step seven, which is deep learning. So did a camp, didn't cover deep learning, I think. And but here they do have deep learning. So it kind of tells you like, oh, here's a neural network, how does it work? And you have some projects that are guided project authors. And they also introduced you to Kaggle, which is quite nice. Because then you're like, after you do these projects, they also tell us you like, here's so many more projects that you can do in Kaggle. So I think that's quite nice.

1:02:29And then finally step eight, which is advanced topics. So it's best practices, context managers, production decorators, as well as command line intermediate. So this is kind of interesting. I don't know how many people actually use the command line that much these days. I definitely, I did when I was back in like 2017, 2016. But I think appreciated. I do appreciate it, you're doing like pipelines and stuff. That I think like working with the command line is it's something that will come up. Like I think it's something that you wouldn't think will come up like while you're just practicing, but at the job, it will come up. If that makes sense. And also introduced the introduction to get inversion control. So as data scientists, we're like notoriously, we're like generally seen as notoriously bad at virgin control and just like writing a decent code. So that's really changing along in industry. Like the standard of coding for data scientists is definitely increasing over time.

1:03:30So as I introduced and get and all these different standards and stuff, like it's becoming mainstream now. So they do include that as well as map, spark and map produce. You get the big data stuff as well. Definitely not comprehensive on big data, but I really like how they give you like a sample of that. So you're aware and you can go look into that further if that's what you're interested in. All right, I'm going to stop here and see if you guys have any comments so far. Let me switch back to my face. All right. Any comments, questions, concerns? Oh, I also wanted to, like, well, I'll double click into like what it actually feels like. After addressing any comments for concerns that you guys might have now, so we can actually see what it feels like. But I just want to lay safe first off. I think you can tell that it's a very project focused, like everything is based upon projects. It's all about implementation. So I think that actually makes learning experience very different from data cap.

1:04:31All right. Let's see, let's see, let's see. What are you flexing about? What are you flexing about? I'm going to go with what I did. Okay. I actually can't find a comment. That what you're flexing about. Okay. Let's see, Clement, will you, will you have another session on Udemy Data Science course 20 from Uncomplete Data Science Food? That is the third thing that we're going to be covering today. After we finish data quest, so we did a data camp and we're doing data quest. And the last one I'll be covering is the 365 data science. Data science course 2021, complete data science bootcamp. So stay tuned. Let's see, the key to they say data quest is a more difficult version of code academy, which is good. I think, I think, yeah, once we like look at the projects and stuff, I think you'll start to notice like their style is,

1:05:34it's more problem solving based. Let's just say that there's like less hand holding in it. All right. Pecaca, what do you think of part time DS master's programs geared towards working professionals? I'm going to try to focus my comments mostly on the three, like kind of the courses that we're talking about now, but feel free to ask questions about other stuff like after I go through them, after I cover through the material. Mr X, where is it possible to learn more about useful statistics? I think so I, so as I have a question, so I know some people prefer like looking at books and stuff, me personally not so much because I have a very short attention span. So I like video courses. I also have used brilliant to prepare for my own interview, just to brush up on statistics and learn. I've also used it to learn a little bit more about statistics over time. Then not sponsored by brilliant. I think I'm not even big enough for cool enough to further me to sponsor me in any way. This is all like, but I have actually used brilliant. I did pay for myself previously.

1:06:36I think it's quite good at teaching if you're into that kind of learning style. Let's see. All right. Cool. Let's go on and I'll actually show you guys what it looks like then. Okay. So let's go here. Let's be able to compare. So programming and Python. Let's let's like go with conditional statements. Let's go listen for lips. So it's like loading the missions. You're like, you're on a quest. I thought I was nice. Okay. So it's not, I like data camp. It's not very like video focus at all. In fact, it's not video focus at all. It's very text heavy. So it's like text and implementation. Text implementation and projects. So when you're learning through it, it's like towards the end of the previous mission, we work with this table like, oh, this table over here. And it's like a real table that we actually found as opposed to one that we made up. I you know, it tells you about it. And then it's like telling you the text Facebook is stored as a string. The price 0.0 is like a float. And then you know, it tells you a bunch of stuff.

1:07:36And then this is how you learn. It tells you like, do you create a list above? We typed this out and surrounded a sequence of brackets. Like this is what a, this is what a list is. You know, like as an example, it's like a very data specific example, who's normally when we have lists, they just go like, here's a list, like here's an array, but not from a data perspective. So all in data class, it's very much like very, very data focused. Like this is a list in the context of data science. And then you have the instructions here, like stored the second row, Instagram as a list and variable name row two. So I already did this previously just to kind of check it out. So you like row two is equal to this row three is equal to this. I, oh, I made a list and then you can be like run code. And then you can see like, oh, row three row two, these are what the variables are. Like, okay. And then you can submit your answer and it's like, nice work. And then you go to next screen. So you get that instant feedback, which is, which is really nice. And you know, now we're going on and really, and then it tells you about like a list, contain both mixed and identical data types. And then what's the task work here?

1:08:39Yeah. So you see like they kind of go into a lot of detail, like explaining what a list is and how it all works, but in the context of doing as opposed to just like tell you straight out. Um, see what I've asked is, so you can already see the list of the first three rows. The fourth element, each list describes a number of ratings and app has received, retrieve this fourth element from each list and then find average value to retrieve numbers. Cool. So now we're really like learning about indexing, right? And then after indexing, I'm sure then we're going to learn about how to actually like iterate through it. Um, so I think I'm not going to work through it so much now, because I think you guys like get an idea of what it feels like already. Um, so all of it is pretty much presented in this fashion is very like here are some text and they explain things through and they implement it directly. Let's go back to the dashboard. Um, I also want to show you guys the projects. So let's look at this project. So this is called a guided project. They call it guided, but this is the guided project here is equivalent to the first to the data camps unguided project.

1:09:40That's that's what I would equate it as. And it tells you like, oh, like we learn about all these things and we're actually going to do something. Yay. Um, so follow the instructions. So this is what it goes like out of title, right? A short introduction where you explain no more than two per guess what the project is, where your goal is and the title and introduction are tentative at this point. And then you like go on to next and they kind of walks you through what it is that you're supposed to do and kind of teaches you functions along the way as well. Um, so this is kind of more equivalent to what it would feel like. Oh, shoot, right? No, oh my god. I didn't show you guys any of my screen. Oh crap. I'm so sorry. Ugh. Okay. I like didn't even look. Oh my god. Okay, I'm sorry. Let me, let me. That's a blooper there. I'm sorry. Okay. Let me quickly hopefully you like listen to what I'm saying. Oh my god. How long did I was I talking about? I was talking for like 10 minutes or something. Right? Oh my god. Okay. No. Okay, you should be able to see my screen. You should be able to see my screen.

1:10:41Okay. I'm very sorry about that. Let me like go back and actually show you guys like what it feels like again. That's really awkward. Okay. So let's know. Loose and for loops. Sorry. Okay. Listen to for a little. Um, okay. What I was trying to talk about earlier is that this is what it looks like. It kind of a walks through like everything over here. It's like this is Facebook. You know, it tells you. You have the office table and data set and then it explains to you what things are in the context of data. Um, and then this is how they introduced like here's stored a second row as a list and variable name row two. So you go like, row two is equal to this list and you already wrote this out here. But so you like write that out and then you like, you know, you run your code and it tells you like, Oh, like this variable was clashed plans. You know, I need to do that and then you verify your answer by clicking submit answer and it's a nice work. Uh, and then you go on to the next screen. And I pretty much just goes through that like in this section, it then explains to you like, Oh, like you can in a race, you can actually index them. We're in lists. Sorry, we're talking Python out like in list, you can actually index each one zero, one, two, three, four.

1:11:46Um, and then it tells you like what it means to be indexed and then you go on to try it on yourself, like indexing, um, accessing the fourth element from the list and then also dividing some. And then moving on, they're going to then teach you more about like loops like how to actually loop through them. Um, and looking at the positions to it. So that's kind of how it works. Um, in in terms of, sorry, I'm like, it's distracted. I'm so sorry about what happened previously. Uh, okay. Um, someone can make me into me at some point. Okay. So hopefully that made sense. I hope that was like now that I've showed you guys actually showed you guys what I'm talking about. And with that made sense. So it's like everything is very, if it's text space, um, and it kind of explains things to you in the very data specific context, um, because normally when you would do lists, they would just tell you that this is a list, they wouldn't actually like. Tell you that, oh, like, you know, you can have a list and you can actually put data into listening specific way. So I really appreciate that.

1:12:46It's very from a data specific, uh, fashion. And then also you like get that instant gratification of like being able to solve a question yourself, um, and doing that. So yes, that is, that is what it looks like in terms of like, uh, each of these classes and everything is presented in a similar fashion. So it's very, it's all like a combination of text, um, and implementation directly. So I was trying to show you guys the guided project that they had over here. So the guided project, I'm like paranoid now, just making sure you guys can see, um, what I'm talking about. Is that okay? You guys can see. So that's the guided project. So I was saying earlier, the guided project here is very similar to DataCams on guided project. It doesn't tell you, you don't like throwing the blanks at all here. They, they do tell you what it is that you're supposed to do. So it is guided in that sense. You're supposed to like add a title. You're supposed to like write a short introduction where you explain in no more than two paragraphs, what the project is, what the goal is in this project. They probably blah, blah, blah like that. Um, and then if you move forward, they then tell you like, oh, like you can, there's, you know, this is why this.

1:13:50They explain like why it is that you're doing certain things as well. So they're like, this is a data set and then now we need to explore the data, right? So how do we explore the data? We're going to create a function called explore data. And then they kind of tell you what the function is and you can like copy paste that into it. And then you're going to be like changing that over time. Um, yeah. So you're going to be like actually writing the function yourself. It's still very, very guided. Like it tells you each step that you're supposed to be doing, which I think as a beginner is appreciated. Because you can get stuck pretty easily, I think as a beginner. So it is very still guided, but it's not full of blank. So in my opinion, if you're doing it this way, it's not exactly going to be like if you're doing a product from scratch by yourself. But it's a lot more similar because you're actually going through the logic and implementing it yourself here. I need comments or questions. Is there anything I should have I can clarify since I messed up and didn't actually trigger my script for so long. Um, Lloyd, it's 1 a.m. Oh, it's okay.

1:14:54Bye. Um, have to maybe catch up in the morning. Yeah, next time I'll try to be better on the time. Also, I'll send out a pool next time. Instead. Uh, jam on ham. I love your username. You guys have great user names. Do you know our yes, I know our R is actually the first language that I started off with. So I didn't start off with Python. Actually, that's not true. I did start off with Python, but not in the context of data science and data science is sort of. Crystal, I think it's quite mobile friendly as well. Oh, that's really interesting. I didn't know that at all. Um, I haven't tried looking at it. You're mobile myself. That's that's quite nice. Um, data class or class of plants. Is it similar to class of plants? I'm like. Not I don't really play games that much. There's like a very specific game that I'm into that are probably not super cool. But I don't know. Classics.

1:15:55Max one. What's the free lifetime access? Oh, yeah, I'll tell you guys. Um, so remember you see the referral code that I have down there. Apparently, like if I, if you use my referral code and there's four of them, then I get free lifetime access. That's it. They don't give me like, so the other refer opposed like data camp, they give me. If you go through it, you get $20 off and I think I get $9 back for it. But for data quest, I don't give you money for it. It's more like you get $15 off and I get lifetime access for like four people. Yeah. That's what it is. Um, yes. So after I go live, it will stay on still. So do not worry. I'll also do what I did last time for the data analysts that Google data analysts. Can't talk. Google data is certificate. I'll like condense it. Remove my blooper in which I didn't show my speed and I'll post the contest version as well. Probably like not immediately like a few days after. But this life from itself will also stay on. It will not be removed. Um, Palette three.

1:16:55I'm here and staying up to attend the live. All right. I'm going to move forward then. I don't want to take, I don't want to permit you from sleeping more. All right. All right. Okay. So if we don't have any more questions or comments, let us move on. So what else I want to talk about? I showed you guys. I showed you guys the platform. Um, I also showed you guys like the project century, some of the projects, which are all in that style. Um, Yeah, like learning style was already touched on that as well. So it's a combination of text and project based learning very, very project focused. There's also more projects than in data cap, I believe. Um, so next up, let us talk about, let's see, support system. Yes, because support system is very important. Once you start coding and doing these things, you're going to start running into errors and you don't know how to solve your problems and you give up. That is very sad. Um, okay. So support system, they used to have a slack. Uh, I have an open here. Good thinking, previous Tina. Um, no, I lied.

1:17:56Let me go to the dashboard. Uh, support system support. Oh, yeah, they have a community tab. I think it is actually, I did have an open. Oh, no, I did. Just kidding. Um, okay. Support system. So they used to have a slack channel similar data camp, but they closed that. I think back in 2019, I think. Um, so now they have this entire like community tab kind of thing in which you ask questions. Very, very responsive. I did check them out. Um, so Q&A can post technical questions and get solutions really quickly. So they monitor this very well. And you can, they also have like, you can ask questions on here. You can also share projects and it's also kind of serves as a, as like a way for you to write about. It's kind of a medium articles like similar to that, like right about the stuff that you're doing in a data science community. So it's kind of a close knit community kind of where you get to communicate each other and just ask questions. Um, also they do have a career, the career board as well. Um, like I'm not, I think I am a premium subscriber. I don't know, maybe I'm not a premium subscriber. I think I actually am.

1:18:56Um, but yeah, they do have a career service as well. But that's on the support side. So might as well. Let me talk about career services now. Yeah. So, um, I do have this open here. So this is the career service that's available. Compared to data camp, data class has a much more substantial career service. Like, data camp, the just recently launched theirs is still in beta version. But for data classes, kind of like very big thing from very beginning. And that's probably because data class and very career progress, like data camp data class is specifically for people who are trying to become a data scientist or a data engineer or a data analyst. Of some sort. So it's very like career focus and they've always had like, um, weekies and just like resumes and guidance stuff. And it just like they just keep on adding on to that. So they have like, really comprehensive like, weekies and things like that. Um, and in addition to that, they also, you can like, they have a job board that people post in and you people, you know, supposedly get jobs directly does. Well, although again, I just want to say I even though data quest is more like career focus and it's more project based.

1:20:02I still feel like I don't think you can just be like, I'm going to do like all the data class and then I can get a job immediately after I still need kind of have to do a little bit more yourself. Um, like, you know, probably still put on more portfolios and just do some more projects like end-to-end projects because the guided projects, they're not less guided, but they still like are guided. Um, and you really need to be able to start doing things by yourself, right? Like take that step by yourself before you can start applying. But they do have a job board and I think it will be helpful to you after you do data, you do data quest and you kind of do like your portfolio more and try more things out for yourself. Alright, comments, comments, comments. You were doing good thinking previous Tina sad violin sounds. I know right? I thought I was smart. Um, so as a crystal I was just trying to offer my phone data class. I thought it worked quite well. Data cam is also made to work well mobile, but even on my iPad, the videos are often too small to see.

1:21:03Okay, that's that's nice. So if you're into like mobile, that's really good to know. I haven't personally checked them out on mobile yet myself. Um, let's see. On 75, I'm afraid I can't really answer that question. Each time we'll come and get me for sure if I asked if I answered that question. I'm afraid. Um, let's see. Alright, so if you don't need questions, we are going to, I'm going to move on now. So we talked about career. Um, now next up, I am going to talk about pricing. So I did open this up, I believe. Yeah. So in terms of the pricing, they have a spring sale right now. I get say 50% off where you can use my link and I get lifetime access. I would appreciate that. But um, I think you still get the 50% off of you, you're using up to you. I'm okay. I don't rate those spring sale gets 50% off with the only have two tiers here. So data camp has three tiers here.

1:22:04You have two tiers. The person is free. Um, again, like the, so the free one here, it lets you get access to more than data camp does that you get to go and like try stuff out a little bit more, but you don't get access to projects, which really is the key part. So that's behind closed gates. So if you're going to use data, press, uh, even have to pay for it. I think that's a whole point right? If they released everything and you wouldn't pay for it, how are they going to make money? Um, so yeah, like you get access to, you think try things out a little bit more, but you do not get access to products. And also, uh, the community, like a large part of data, press is about the community. It's about like asking questions, answering questions, job words, and say that you don't get access to that. So I think you're, if you're going to go over data, press, you're going to have to pay for it. Um, so yeah, the other premium version, and they just have one that's built annually, and they have one that's built monthly. I have, oh, actually remember this. I actually paid for the one I was built monthly because there was some like fine printing. If you get the one that's built annually, you have to pay the annual fee.

1:23:06You can't cancel it. And I was like, that was a bit, that was a bit sneaky of you. So I paid like $49, $50, $52 fast tax for it. Because again, like, I think this is really good, but it kind of still feels in more like for the beginner entry level. Kind of thing for me personally, I'm not, I'm ready data scientists, so it's not like super helpful to me. So I don't want to pay more than $50 for it. So yeah, I'm going to pop it. Getting comments on pricing. Any comments for pricing? No, no, no comments about pricing. Rubik Gupta, I'm confused for what, for what to start with for a data analysis project. I mean, these platforms, they kind of guide you through it. They will tell you what to do for a data analysis project, my kind of how to approach it. There's a lot of fewer resources available as well. So you can always check those out too. Jesus, Jesus, Jesus. Tina has come in issues. They didn't want to commit to the whole year.

1:24:07I mean, it's kind of like, okay, truthfully you guys think it's kind of like, I pay for it myself, even though I knew that I wouldn't be really using it. Just because like, the stuff that I'm working, like, if this is really designed for people who are pretty entry level, or they're trying to change the career and stuff, and for me, it's like not, like, it's not super useful for me. But I did it for you guys. You guys appreciate it. I did it for you guys. I paid for it. So I can give you guys a more thorough review. All right. Farhan, many thanks for using your one of my true videos. Hot was your welcome. You're welcome. You're welcome. I agree with you. We owe Tina 50 bucks. Maybe some bull. But sometimes. All right. Okay. So moving on, moving forward over here, pricing and talking about pricing over here, what else I want to talk about. Okay. Probably like lots. All right. Let's go back to the actual page. I'm too scared to put my actual face on now because I'm like worried that I'm going to, like, not turn on my screen. So play with me, even though I'm talking, you're going to see my screen here.

1:25:11So in terms of pros and cons, I really, really like that project centric. You know, that's just my jam. I just love it. It's very project centric. It's very realistic from the very beginning. So I think that you can just do data. I just don't just be like, yay, I'm a fill data scientist now. Again, no, I don't think you can. But it's much more realistic, better learning. And I think if you go through a data quest, you would not feel like you forgot everything as much. You might still forget some things, but you would feel like you would have more to build off on. And the leap towards doing your own projects is going to be less. Like it wouldn't be as big of a leap. So I would say it's also a lot more. So I'm going to compare this to data camp just because like they're pretty, they're kind of similar, at least in the way that they market themselves is quite similar. But compared to data camp, it's less bloaty. Like data camp was 10 months. This was like what how much I was 68 months, I think it was. And the reason for that is I did a camp has more bloat. Like more information. That's not exactly necessary for it.

1:26:13Well, for data plants, it's like straight up like we're going to learn all the things that we need to do and then just go do projects on that. I really, I quite like that. Also, grace of our system, they have like the support system is like very built out and very fast response rate. Data camp as well, very fast response rate. So that's just a pro for both of them. And for data class, a lot of career resources and job board. So this one is a good, is it big emphasis on data on am I showing you data camp now? I am data class. Okay. Yeah. So another thing about data class is that they were focused on career from the very, very beginning. It's all about like changing your career and then like helping a friend job and all these different things afterwards. So with the very built on like over time, it's just there's a lot of wikis, there's a lot of guides and there's a lot of people posting on the job board and stuff. So it's very built out. I think you, if you're going with data class, like definitely utilize that resource and it will be helpful to you for even if you don't get a job as from the job post, just being able to structure the resume and they'll, they'll help you like figure out how

1:27:15to get a job essentially. You know, I tell you about some of my tips that you can use and how to present yourself in a way that people would want to interview, which is usually the biggest barrier for people who don't have a bachelor's degree or a master's degree. Or like some sort of official school documentation in, in some being computer science or data science, it's hard for you to get that interview because people are like, oh, you just like self-learning yourself. So employers are not sure that you actually know how to do things, right? Because there's no guarantee kind of, so yeah, appreciate that. I also really like how they focused on storytelling and they had used API. So that focus on APIs as well. Really, really love that because just a really big part of the job, you know, really big part, web scraping as well and SQL is baked in. Definitely not skip the SQL part. I feel like SQL is one of those things where it's harder to do projects for. So the fact that they have a SQL portion and you can do projects on SQL is a big plus in my mind. Also, I like how they went more in depth with the ML. Like, do I think it's everything? No, I don't think so.

1:28:16I don't think you can just be like, I know ML now from doing, from doing a data class, but it does give you a really good, it gives you more of a basis and it gives you really good sampling of like different ML algos, goes into deep learning a little bit more as well. So it kind of tells you what you can focus on in the future. So I appreciate that. Some clients, right? So this is less handholdy. It's more problem solving based. So as I was going through some of the lectures and stuff, I don't even call them lectures. So some of the like assignments, I guess I would say, like, I obviously, no Python, I obviously, no C point, no these things are ready. But I was working on it. Like, it's like, you know, I was like, forgot this syntax, maybe or something. And I think like go look it up. And I think there is like just more of a problem solving aspect to it. So if you were like, if you like that aspect and you're okay with like solving problems by yourself and not just how many answers, like directly available to you, I think data quest is really, like, didn't want to be good for you.

1:29:16But if you're like more of a nervous person, you haven't really coded it all before. And you know, you're not very like comfortable problem solving. Then it could be a little bit discouraging my opinion, but they do have a very, very, very support system. So you can always just go and ask questions about that. Also, another con is that it's very career focused. Like it's like, if you're doing this and you're on a data scientist in Python track, it's like, you're trying to become data scientist in Python. It doesn't have a selection of skills. And it's, it's just the breadth of it is just not very large. What they do cover is great. And it really, they cover the things that are very useful in the job, but they don't really cover like some extra information that data can, for example, covers. What else? What else? Another con is that it's similar. Another con is that it's not super in depth. So with data, plus they kind of do a good job in sampling, as I said before, but they don't really go very much in depth for most of the things that they cover. They kind of gloss over like stats, for example, that might be quite useful to you.

1:30:23Yeah, so you will definitely have to play around with it and go deeper yourself. So there's like a concept that you don't exactly understand, because they didn't really explain it that much. You're going to have to go and Google it and try to eliminate yourself. All right, let me let me know as well. You guys, what are your pros and cons that you think about data? All you do that, I'm going to run to the bathroom.

1:31:43All right. Pros and cons. No pros and cons. Okay. Daniel, what program teaches more about SQL and database design? This is the question. I want to say data camp does a little bit more because they have more focus on like specific topics. With that being said, I haven't looked into the data engineering track as much. So definitely don't take my word on that. Do check it out yourself because I've been focusing more on the data science and data analyst. I'm purchase. That would be my yes though. It's same. Simee, you're in industrial engineering senior student Istanbul,

1:32:47so I should Turkey. I'll focus data science and operational research and masters. What are you thinking about industrial engineering and data science? Honestly, I don't think I can really help you that much on that. I don't know much about industrial engineering, but generally most things translate pretty well to data science just because data science like data is literally everywhere. Right? All right. For us, how much time to get five lock per month salary has a data scientist? Can someone tell me what lock is? I've seen that word before, but I'm not familiar with it. Is it like 100,000? Is that what lock is? Max, you prefer data class because I won't forget it's easily. Yeah. Polence, I personally prefer data classes.

1:33:48I'm more of a text-based learner type of person. Makes sense. Yeah, it is like a more text-based as opposed to video-based approach. All right. I will go and talk about just kind of finish off on data class here, and then we can move on to our kind of one. So I think in terms of who it best suited for, I think it's best suited for career changes as opposed to people who want to brush up on specific skillset. And let's see. It's also more compatible with people who are okay with problem solving a little bit more. If you run into issues, because for data class, there might be some problems that you have to. You have, you have, and you can't figure it out yourself. You have to be more comfortable with solving the problem yourself. We're like asking people on the community and things like that. Beep beep. You're interrupting me. Beep beep. Interrupting. Here, go, go beep over here. Okay. Yes. So that would be like who I think it's better for.

1:34:50And of course if you're like more, if you like the text-based approach a little bit better, then you're going to like data class more. And it's like more implementation focus. What do you guys think? So I'm going to go to a data class, but like so far, what do you think? Data class or data gap? I will not say anything to the very end. Okay. So I also wrote down some of the tips that I have if you're going to choose to using this resource. The first one is like because it's more problem solving based, you have to be more patient with yourself. Like, if you're coming from a background, this would be like memorization heavy. Like, for example, pharmacology like me, you might be like, oh man, I can't figure this out. God damn it. I give up. You have to be more patient and realize that data science itself is going to be a very problem solving based. So that's not like a, it's not really that you can just, what is it called? Like, you can't expect to just know everything, like straight up from it. I just changed my face back, but I'm going to remember this time to actually change it.

1:35:50So what else? In terms of, yeah, so you have to be more patient. And another, some other tips is that you really should apply your skills to building portfolio. The projects that are being presented are quite nice. And you know, there are, there's a lot of different variations and they really help you figure out. Like, you know, all these different concepts, but you actually problem solve through them. But in the end, they are still guided projects. So you need to be able to take these skills that you learn and apply them to different data sets. And also, you can see about APIs and web scripts, right? Go do that yourself as well. And do an end to end project. So it's really important to like take all these different skillsets and actually apply them to things without guidance, because that's what's going to, what it's going to be like on a job and also build all that portfolio. So whatever you're making a project like this, build out a front end to it. Just because it's just nice. If you have a visualization or something like that, plug it into stream, like something like that, have a dashboard where people can actually see it. And that makes the portfolio visually more appealing and more understandable to people.

1:36:53And as part of a portfolio, when recruiters are looking at your portfolio to be like, oh, I look at all these parts, we can easily see the things that you're doing. And it just makes it look more professional. Another tip I have is that, yes, so this is, this is like a kind of a general tip. Make effort while you're doing the career stuff. So, I can't admit it, that made no sense. Make some effort while you're working through data quests to start looking at different jobs and stuff, because it's similar to other platforms that are like it. It's very difficult for you to just be like, I did data quests. Now I can go and become a data scientist. Like, yes, that's kind of what they say. It will happen, maybe that does happen to some people, but truthfully, it probably doesn't happen to most people. And even if you do just get a job directly, you still would be kind of missing a crucial step of being able to understand things, like being able to solve problems by yourself and trying to like, a self-directed project. So, am I trying to get this right?

1:37:54Yes, so okay, make it effort. Like, while you're like going through data quests, like, look at career stuff, like the different jobs are there. And don't wait until you finish everything, just start looking for your job. Try to do that earlier on. It kind of feels scary, because I don't even know what these words mean. But it's really important for you to kind of prime yourself or the things that you need to know. And that will help direct your, while you're learning, or help direct you just like, almost subconsciously, to focus on specific aspects that will be more helpful to you when you're trying to get that job. All right. Well, that is all I have for a data quest. Any comments? Let me switch back to my screen so that I don't forget. Okay, data quest for you, King of Zamuda, Nelson G, data quest. Sammy data quest seems to be better, but subject to being the end, completely agreed. So I'm trying to present my, that's why I'm like,

1:38:54I'm not like, this is better than the other. I try to like, I hope I should. I'm doing an okay job. I'm trying to present to you like, just more like, factual information, actual information, where like things that I noticed that I think are good for learning. And I tried to also like explain why one thing could be good, could be better than another, as opposed to being like, you should always choose this one. Because I also don't think like one is really better than other, because even though they're quite similar, they are serving different populations, right? For data, for data quest specifically, they're very career focused. It's not like career chat, but for data camp, you can also be like a working data scientist, working data analyst who wants to, you know, just elevate their skill cell. A little more. I especially consider data analyst and you really want to become, you know, more proficient in coding, for example, an ML, for example, then it would make a lot of sense for you to go to data camp and just do those courses as opposed to go to data quest and just have to like go through everything, you know?

1:39:56Let's see, let's see. Byros Byro, hey, how's it going? I remember your username. I remember your username. It's such a unique username. So hey, hey, it's Mola. Hello. The third, the third, data class mostly video based, whereas data class seems to be a lot subjective, but the latter has more topics to dive in, who that's just my take on it. Oh, hi, that's fair. That's fair. Max, do more portfolio videos. I actually do have something in store. Oh, it's more like I'm going to be building my website soon. So watch out for that. Which is kind of simple. So, hey, why are you doing live at this night? It's very difficult to watch. Is it because of the time? It's like not night for me. Just that's why I'm afraid. But do feel free to drop and just catch up in the morning. I won't be offended. On 75, I just had a look and read it and someone wrote this. I just had a look.

1:40:56What I do. User claims he's a data scientist that makes 250k per year for just writing a few SQL scripts a day. Is this false? Tina, what is your thoughts? I will. Hmm. I'm not sure about that. I'll have to have more content. Because just writing a few SQL scripts can mean a lot of things, right? There might be a lot that actually goes into it and just the implementation of few SQL scripts. All right, moving on. We have no more comments about data. But shall we move on to our final one? 365 data science. So, let me show you guys 365 data science. Okay. So, I want to make a note here. This is the 365 data science platform, right? And they have a course libraries and modules and a bunch of different courses as well as a bunch of different tracks. However, what you guys asked me to review was not the 365 data science actual platform itself,

1:41:57which is, you know, it has all the courses that are available to it and is completely separate. What you wanted to review is the Udemy course, which is this one. What is it called? Ah, here it is. It's the data science course 2021 complete data science bootcamp. So, this is like one of the courses, I believe. I don't know if it's like different on the 365 data science platform itself. But this is like the specific course that's the bootcamp. So, I'm going to be reviewing this as opposed to the platform. I just wanted to make a note of that starting off. All right. So, 365 data science. Let's see if just see some of the notes that I do have. Okay. All right. So, this, they have a good nice description over here. So, I'll be like referencing some of that material as well. Just being paranoid double checking that you guys can see my screen. Yes, you can see my screen. Okay. So, 365 data science, this bootcamp is 28.5 hours long.

1:42:59And it's comprised mostly of video lectures, although it does have articles and it has a lot of downloadable downloadable material. Because if you're not supposed to just like watch the lectures yourself and just be like, oh, I'm absorbing information, actually what you're supposed to do is download all the material and follow along. So, there'll be like implementing things that you're supposed to implement along with them. I'm going to like play around with it yourself. So, it can be deceptive. It's like 28.5 hours of videos. It's like, oh, like I can finish this super quick. They don't have like a estimated amount of timeline for how long will it take to complete this. I would say honestly, it would probably take you like at least six months. I want to say like if you're going to, here's the thing. If you really want to just cover everything, then you can probably breeze through it. But you actually go through it and implement it yourself. Also, my recommended approach is to not just implement things that the video is talking about. But actually, if you project really to those concepts and just like expand upon the projects that they present yourself, I think it's going to take you like right a few months to get that done. Let me know in the comments what you guys think. Where anybody's success taking a Udemy course,

1:44:01let me know what you think. But yeah, that's kind of like my estimate. I couldn't like find any references for this. So, and I'll talk a little bit more about why I actually think it will take you that substantial amount of time. It is because this course, you know, like this course, it is called the Completed Science Book. And they mean that. They really mean that. It really is the Completed Science Book Camp. It's like, you know, like what we talk about previously. It's like, oh, like we're skipping on some things. We're like, you know, not really going in depth about some other things. In terms of like breath, we're deaf. But I want to say like we, we, we got it covered here in this book camp. Let me show you guys what I mean. So, it is briefly in your descriptions. You can see really clearly understanding. So they give like a lot of background about what data science is, like how it works, career stuff as well. And then dive into mathematics statistics, Python, and advances. Applying advanced statistical techniques in Python, the individualization, machine learning, and deep learning. And they're not just glossing through it.

1:45:01Let me see it. There's like, if you actually scroll through it, right here, they have 62 seconds. Well, 63 in clean the bonus lecture. So it's really, really substantial over here. And let me just kind of click through them a little bit as an overview first. And then we'll dive deeper into into the videos and I'll show you what the learning style feels like as well. So yeah, like, so in the beginning, they have a lot of like, what is data science? Why is it important? Why is it popular? And what does it actually mean? Which is, if you already know what it means, you know, I feel like you can definitely skip this part. If you're just very like intro and you have no idea what this is, then definitely go through it. And talk about what machine learning is as well as some of the career stuff. And then they go onto probability and they really cover probability quite well. So they covered all these different things and then commonatorics. You know, they covered underline math behind it. Base inference, like that's substantial as well over here. Probability distributions. And then after covering that, they go into like statistics as well.

1:46:05It's like, you know, descriptive statistics. And this is like, of course, in itself, if you're taking like a university course, each of these sections are essentially a course. Sorry, not sections. What is it called? Each of these like parts, yeah, I don't call it parts. Each of these parts is like an entire course, essentially. So yeah, they go through all of this. The descriptive statistics, inferential statistics, confidence intervals. And it's like not brushed at all. We're like literally learning it very specifically. This is T-distribution, how to actually use it. What's a T-test in reality? What does it mean for a confidence in the post-the overlap? What does it not mean for it to overlap? Cohen's D, like all those different things. They cover that. Hypothesis testing. And then for like Python, again, like each part is, it's all in course, seriously. Very in-depth, like kind of going from ground up. And again, like when we were going through them, they have these resources on the side. So I would actually recommend you download all the resources in the beginning as it goes down on each one of them, because it's actually quite hands-on. It's not really just you sitting there watching videos.

1:47:05It's quite hands-on. In the math section, it's like, I actually want to say that the math section is relatively hands-on as well. Yeah, I think that's fair. Because it's not fair to be like, it's not as hands-on as like the Python section. Well, yes, because that makes sense. But it does, it is still quite hands-on. And in doing that, especially in the programming parts, like you're going to be falling along and doing all these things. And it goes all the way up in terms of like Python tools, all the way to modules and explaining to you what modules are. And it does a very good basis, right? It's not just like, here's numpy, here's pandas. Unless just do numpy pandas. If you explain to me, here's a module. This is what it works. How do you import it? Like, what does it mean? Here's some other modules that you might want to look at. Yeah. And then more statistics, like actually implementation of statistics within Python. Very, very thorough. I really must say myself. Like, hopefully crap. This is like thorough. Multi-clinarity, like none of the papers that we talked about earlier that cover this at all.

1:48:06And it's like, we were really implementing it. We all the things that you've learned, right? So really appreciate that. And then for the ML stuff, like, came in clusterings like that, they also really like show you the math behind it as well. Like, it's not just telling you how to use stuff. It really goes very, very in-depth, like, how it works exactly. And then you obviously, like, go and actually use it. It tells you what the pros and cons are, what it can or cannot do in terms of implementation. I mean, you have a whole section dedicated to linear algebra. What's matrix? Linear algebra, linear algebra, geometry. And it goes on to, like, what's a tensor? What's that relationship with linear algebra? So it covers the math very, very deeply before we guide on into deep learning, which makes sense. Also an entire section dedicated to learning. I want to say, like, the platforms that we talked about earlier, so data time didn't cover deep learning. And data quests kind of, like, they did cover deep learning, but it wasn't, like, very thorough. It's more like this is how you use it. And it kind of explains, it does explain what it is, but, like, not at this level, definitely not at this level.

1:49:08All of that dedicated to it. And then you do implementation of that using tensor flow, which is very, very nice. Like, they actually go through, you know, show you how to implement all these different things and explain to you how each of these sections work as well. Absolutely, end up. Like, I just, that's it. I was actually really very surprised by how end up all this was. And then one more notable thing before moving on. So, like, I'm kind of just, like, covering through them really quickly. So, but I do want to make note of one. They do have a case study. What is case study? Sorry. They do have a case study in which it's kind of similar to a project, but it's much more of a comprehensive open-ended project. So, it's not like, here's a scope down project for you to work on. This is like literally from zero to actual presentation. Like, this is a very real life. And it's similar to what you would actually do. If you are studying the project, which is called absenteeism,

1:50:10at the workplace. So, they go through, like, you know, this is the plan. It actually had a plan, your project, which is very important because if you don't plan your projects, you're going to have a bad time. I learned that from experience. But they tell you that. So, you don't have to do it like that. And then it's like, here's how you do an approach, and then, you know, prepossessing, you go through it. So, they demonstrate how to, like, go through each of these and EDA. I'm not going to go into, like, too much detail about this. But, like, you can also, like, look at it on the course website itself. But it really just goes from zero all the way up. And they're selecting how to do these ML models for it. And then, in the end, they also, like, show you the predicted outputs. And then also, in terms of, like, tablo, they will show you how to present that information and the visualizations as well. So, the case study, yes, it's like one case study. It's not, like, a bunch of different ones. But it's very, very, very, very, very, very, very, very, very realistic. I would just call that realistic, very, very realistic. Yep. And then they also give you additional tools as well. So, there's just, like, so much information.

1:51:10That's what I can say. Like, this is, when they say complete data science book camp, really, really, really complete data science book camp. All right. So, checking back on the comments, any comments? Pull-ins, all the instructions in data science 365 come from a good academic background. I haven't looked into these instructors specifically, but I would not be surprised by just the quality of the work that's being done. Like, if you don't know what you're talking about, it'll be very hard to be in such depth that you're talking about. Any comments, questions? No comment? No question? Okay. Cool. You can't ask no questions. That is fine. Oh, the link is too big for the chat. For this chat. Maybe you can put it in this court. Okay. Cool. All right. So, if there's no, like, questions or concerns, let's dive deeper into what you guys would actually look like. So, in terms of the learning style,

1:52:10it's a very video focused and very animation driven. Like, I'm just thinking about how much effort there was to do. This is what happens when you do YouTube. You're like, well, how the most have been so much effort in your editing. But it's very, very, very, like, the animation style, very well visualized. So, let me show you, like, for compliments, for example. Since I have it here already, yeah, you guys can see it. I can't get the audio to play because then I just can't figure it out. I can't watch it. But this is kind of, they talk through it. It's like very clearly animated and very clearly spoken as well. Like, this is the coin toss and this is how it works. So, all of it is, like, in this style. We can check out, like, one of the Python ones as well. That might give you a good idea of what, like, demonstrations look like. What the demos look like. Okay. Why don't we look at numbers and Boolean values and Python? So, yeah. Here's Python and it's like, see how it's, like, literally animated with boxes? It's not just, like, some, some, the instructors just say,

1:53:12stuff, like, they actually animate each stuff, which I think is, it's very nice. It's, like, so clear cut, whatever it is. As they go in through these demos. Yeah. Hopefully, like, it gives you a good idea of, kind of, what the lectures look like. Let me know if you want to see, like, a few more samples later on. But, yeah, that's, that's kind of the style. So, it's very video heavy, very polished, kind of style. And I do want to make a note that in itself, it's not project-based. It's not like, you don't really do, like, a lot of problem solving. I mean, you can, you can do it, but you don't have to do it, right? Because you're following along and trying to, and you're, like, manipulating on variables, you're, like, doing all those projects that the instructor is showing you by yourself. And you should be doing that. But you could technically, like, not do that. So, you kind of need to have discipline there. But you can, like, technically, not do that, right? So, it is, like, a lot of video watching. And you're just going to depend a lot more on your own discipline. It actually works through it and not just, like, watch the videos and just assume that you know how to do it, which is a great trap.

1:54:13Watching the videos and thinking that you know how to do it. That is, like, that is a very big trap that a lot of beginners do face. And, yeah, so, it, and the actual case study itself is, like, an extended project, essentially. So, there is that project. If you're going to use this, like, highly recommend for you to actually follow through on each of the steps and just play around with it more. I'll go into more details about my, kind of, tips for how to use the resource. But yeah, that's kind of, I hope that gives you good idea with the learning style. Feels like. Let's see. What's pretty much? Comments, questions. I'm watching the chat right now. Okay, I'm going to move on. And then we can discuss things a little bit longer. Oh, moon, you have a question. How long is this statistics going? It is significant. I feel like if I move this forward, I move this. It's, it's very significant. Like, I want to say, at least the third of the class, I want to say, is, is, is stats, maybe like a fourth?

1:55:14Something like that is very significant. Like, it covers the basis of statistics. And it also covers, like, applied statistics. And then it also covers, like, machinery stuff. And it's not stats, but like, the math behind machinery as well. So yeah, it is very significant. All right. So let's talk about support. So I think support, it's, because it's a course, right? So it's not like, there's like some form dedicated to it. It's, I thought, you to me, it's like the same as you do me's other support channels. I'm just opening that up right now for you. It's the Q&A. So you can just like make, you can ask questions and stuff. And it's just like a lot we will have taken this course. It's a really, really popular course for very good reason. In my opinion, so most of the questions that you're going to ask is probably really there. And the people, I did notice that they are very fast to get back to as well. So if you have any like specific questions about each of these courses, people do get back to you very, very quickly. So I think the support system is very solid.

1:56:15So I would usually evaluate career prospects, but I don't think this is fair, because this is like a course, right? It's not supposed to, like, they do kind of cover resumes and stuff. It kind of occurs that you can expect. But there isn't really like direct career support, because it's not meant to do that. Talk about cost. You see, I thought it opened up the cost, but past Hina was not intelligent enough, or like did not have enough foresight to remember the cost. I'm just going to tell you, you don't have to see it. Just, will you believe me if I tell you to look at the costus? You and even if I don't show it to you. So, if you're just buying it directly, it's like $99.99, but it's you to me. We all know you to me. It's like, there's a way for a huge ass sale. And right now there's a sale, and it's $14.99. It's so cheap, and you get a lifetime access. Everything, all the resources, all the cool stuff. This is just such great bang for your buck here.

1:57:15I'm like, this is insane, right? And it's not subscription based. You don't have to pay for it monthly or anything. Buy one time, and it's yours forever. Incredible. All right, so how about pros and cons? Well, let's see if there's any questions first. Pullets, the guy who teaches the Python course of docs for PhD. I'm not surprised. I'm not surprised at all. Looking at the quality of it. Daniel, wow, that's impressive. Yes, I am very, very impressed by it by this, by this, just like the breadth and depth of the things are off. The lectures, just people just clearly know what they're talking about. These are experts in your field. It's a hush. I feel like there's no defined data science rules. Most of the companies are still pretty much her field. I'm not going to comment on this because I just want to focus on get through with this part first. Let's just feel free to comment afterwards. Okay, we can talk a lot of stuff afterwards. So pros and cons.

1:58:17Okay, so in terms of pros, I just think it has amazing breadth and depth. It's just really pretty incredible by how much material it is in the quality of education. Also, just the animation style and things are clearly labeled. There's a lot of effort and love that's been put into lectures. You actually perfect over time. Like, this is not someone who's doing a rush job or anything like that. Like not not saying like, you know, data camp or data class, they were doing a rush job or anything. They definitely weren't either. But this is just very, very well-productionized. And the information that's being covered is taught by people. I really know what they're taught from. What else do I have as a pro? Yeah, so, I can't read my own hand right now. Yeah, so kind of related to that, it does cover deep learning. And it covers the math behind both the learning and the statistics and for machine learning as well. So you're really learning stuff from the ground up as opposed to just like learning how to use it first. So I think that's pretty great pro.

1:59:18And as I was saying earlier, great bang for your buck. At least for me, it was on sale for $14.99. Even it was $99.99. This is like self-grading for your buck here. And it's not even a subscription base. So I don't know if you get anyone who played about the price here. Okay, and then another comment that I had as a pro is that it's very hands-on. And it's very applied, even though it is video lectures, but because you're supposed to be following along, it might be deceptive and do you think you're just going to sit there and watch videos, but it's not supposed to be. You're supposed to be following along a lot while you're actually going through it. So it's very, very hands-on. And the case study is very in depth and very, very realistic. So if you go through that case study and you follow along, if you come across like another project that you want to do, it would not leap between doing your own project is going to be less. Because you already did something that's quite comprehensive, you know? And with that being said, though, because it's not project focused necessarily, let's moving on to the cons, because it's not project focused necessarily. You don't really get as much like, how do I say?

2:00:21You don't get to experience as many projects, whereas many small little pieces to work on. And it also requires more discipline. Because you can totally just like not actually implement any of these things yourself. And, you know, just think that you know stuff and just move on. And I really think that a lot of people probably have fallen into this trap in this particular course, just because it's a really common trap to fall into. I actually just realized, you guys probably can't even see the animation style, because you, to me, blocks the animations, do they? Well, that's awkward. Well, and nobody even, maybe somebody commented, but I didn't even notice. Okay, I don't know if you can actually see it, but the animation style is absolutely brilliant. Anyways, moving on to more of the other cons. Yeah, so if you don't have the discipline to actually follow through with it, that could be an issue. And another thing that I want to talk about is that because it's actually covering so many of this fundamentals first, that can be difficult for some people. Like I said earlier, there's like this balance between like,

2:01:22implementing stuff and keeping your interests and getting that gratification and then learning more deeply about it as opposed to just like, they're all the fundamentals, you know, and then they're diving into that, because it's going to take like sitting through a bunch of math lectures that are super theoretical, even though you're like applying it, but you're not applying it to like actual data science or data sets, but you're interested in it. That could, that could be like not super motivated for some people, right? So I think if you are going to go with this course, like be aware of that, that you would be sitting through like material and gives you a great basis of information, but you're going to have to be patient. You're going to be patient in the sense that you will get to use it at some point, but you don't get to use it immediately right now, like some of the platforms that we covered previously. Um, I'd say, uh, one of like, yeah, so another thing, another con that I do feel like it's a little bit bloated in the very beginning of the course, like in the course itself, it's like in the beginning, they cover a lot of sections, but let me show you what I mean. They cover a lot of sections that are like, what's data science like, all these different

2:02:25information like what the field is, what the careers are and debunking myths and stuff. And that's like the significant part of the course. This is more of like a, if you know stuff about data science, at all, you probably don't really need to go through that part very much, if they can probably condense this, you just do a few lectures, honestly, like not necessarily going to so much detail about it. Um, so I just think it a little bit bloated, but I don't think it's that big of a deal. You can skip it if you don't want to. All right, so that's being what you all have for the cons. So I want to talk about who is best suited for moving on next. Oh yeah, let me know in comments what you think the pros and cons are as well. Very interesting and seen. Um, so who are you think is best suited for? So I think this is really good for people who love understanding things from a ground up perspective. Like you don't want to just like implement and get that instant gratification immediately. Like this is good for people like I can wonder if you want to like understand these at a deeper level. Um, I also think that this is different people who are motivated and have to actually follow through

2:03:25and patient enough to like go through all the lectures and actually implement everything yourself as opposed to just watching lectures by yourself. Um, and let's see. Also, it's for people who because it's not like it's not as problem solving focused, it's important for you to be comfortable playing around with it. So what I mean by that is say you're implementing this project, right? Then you're following along with it. You have to be comfortable saying like, hey, like what happens if I make this small change instead? Like what if I did this instead? Um, as opposed to just following exactly what it is that that person's saying. So you have to be like kind of more exploratory in that mindset because you don't necessarily have to be and they don't encourage you to do that like very specifically. Like you don't have to do it. But it would be in your best interest to do that exploration so that your learning is not as directed by someone else, right? It's more like self-directed in that sense. So I think it's quite, it's like, I don't know if this point comes across like very well, but I'm explaining very well. But I think it's actually a very, very important point. You have to be someone that's be like, okay, pushing yourself to be hands on as opposed to

2:04:30to platform itself pushing you to be hands on. It's more. Um, what else do I have here? Yeah, and all the career stuff, like it's a course, right? So I don't think it's fair to be evaluated on like the courses itself. So it's just going to, if you're going to buy this course, it's going to be for learning. Like if you're not, you shouldn't be like, I'm going to do this and then just get the job using this. Although on an actual 365 data sign platform, they do have courses that cover interview process and things like that. And I have a link for 57% off for the like full data science platform, which is like subscription based itself and offers different modules. But for this course specifically, I don't think we should talk for career stuff. So that's not really really what it's meant for. All right, let me see your pros and cons. Sevi, why are you still awake? Oh my god. Go to sleep, Sevi. All right.

2:05:32I'm kidding. You don't have to go to sleep if you don't want to. I'm saying I really appreciate it that you're actually still sticking around. Let me see. Arturo, should you binge the course and do projects where go along with the walkthrough? I think you should go along with the walkthrough. Do not binge the course. You're not going to remember all that information anyway if you don't want to start implementing them. So definitely don't just binge watch everything. Let's see. Daniel, how did you get to work as a data scientist without experience? I'm going to explain that later. Just tell DR I did have experience. Let's see. Swai Boomer, Doomer, Loa, QB, No Flu, Empire, so you're pretty much our master. I guess you're a master of Python at least. Sevi, you guys did see the animation. That was good. I guess like sometimes you get to see it.

2:06:33Sometimes you don't. Cunab, 365 would be way too crazy in death for me. Yeah, I can definitely understand that. So that's why I was like emphasizing the point. It has to be someone who really wants to know no stuff. I also want to say if you're going to be taking this 365, I think it's a bad idea just to use it as more of a resource or a guide as opposed to go through every single thing and then do that. I don't think it's a bad idea just to use it as more of a supplementary information as you learn more about data science. I start implementing your own projects and stuff and then use it more as supplementary and maybe introducing you to like deep learning or like topics like that in the future. As I could begin our course, I can see it as being a bit overwhelming for sure. Byer's prior, which has got you to me course. Can I start in two days? Awesome. Let me know how it goes. This next one, this course looks fantastic. Yeah, I mean, it's pretty, it's awesome. It's pretty in death. Polens, I love knowing my stuff.

2:07:34So I prefer 365 data science and I have an active subscription with them. Cons, I find them more jippo to actually follow. Yep, I definitely can see that happening. I think in general it's just like because it's not that incident gratification, like applied learning kind of thing, it's harder to stay focused and be disciplined enough to implement stuff. Definitely understand. Okay, cool. If we don't have any more comments, let's see. What do I have to do? Okay, last, the last thing I want to say is like for tips for doors that are choosing this specific resource. My first one is that I think I've kind of like just rambled on with so many times. I'm just going to say one more time. I apologize for being rambling. Implement things yourself. You really need to implement things yourself. Don't just follow along. Like that's going to be your biggest trap or 365 data science. Like this U2M course at least. It's going to be like you're just going to sit there and binge watch and get too lazy and not actually do anything. I think this is going to be really, really, really big traps. Please, please, please don't do that. Download everything yourself and actually go through it.

2:08:34And in terms of like doing the case, do the case study and then you also want to apply everything that you learned into projects. This is important for any resource, but it's especially important for this resource. Because it's covering things in so much depth and the video style not sure if I'll long is just more passive than you can implement things directly by yourself. So you really, really, really need to take that knowledge and apply it to your own projects. Especially the more like in-depth knowledge. It's hard to conceptualize these things if you're not implementing them. And then you'll probably just like forget very easily or just like not understand it at all to begin with. And finally, my other tip is that don't be so afraid to skip over some of the concepts and the beginning parts. Like this is a very comprehensive course and it's there for people who want to go through it, but it doesn't mean that you need to go through every single little detail, right? Like it's not, I'm just going to switch my face again. It's not something that is like you have to go through every single step to master data science.

2:09:38And my hunch is that this is a kind of course where you're going to be going through ones and implementing stuff hopefully. And you're going to be coming back and referencing it over time. Because these concepts are being presented. There's so in depth, there's so much information as being involved. I really doubt that you're able to learn and understand everything perfectly the first time around. You're going to come back and re-reference them over and over again. So it's just like don't see this as like a let me do this one time and then forever be okay kind of course. And also don't be afraid to like skip over some concepts in the beginning if you're too difficult where you don't really understand what they're talking about. And that's okay, right? The implement is something you can implement. And then once you start learning more, you will realize that these concepts that seem very conceptual in the beginning, they start making more sense in the future. Yep, so that's all I have for my tips and resource. For tips on using this resource features. I'm done with my notebook. All right, well we covered through everything.

2:10:38Oh, I guess tired. I think I'm like I'm not tired. I'm like it's like shy of um we did it. We went through it. I'm pretty pumped. I really loved doing these lines and that I loved chatting with you guys as well. All right. I'm going to stick around um or I want to say like 15 minutes maybe. I'm going to say like half around that 15 minutes or so. So I can go grab dinner but I'm happy to answer any questions to my best of my ability. Sabi Tina reading her nose like she's reading scripture. You think I want to see how ugly my notes are though? I'll show you. Like I can't even remember those. You see it? It's like if you see me like going like this is because I can't I can't read my book.

2:11:39Let's see Maxwell if you're looking to train a group of data scientists for your company which data camp would you recommend them to them? Is that is that I don't think you meant to be like you should choose data camp but I'm sorry. Which data camp would you recommend them to learn and what additional assignments would be asked and to complete? Yo that's hard. It's hard. I think it really depends on what kind of company you are. And okay here's here's here's the clue in your question though. If I'm trying to train a group of data scientists for your company so they're already data scientists right? Oh are you asking me like which data camp course I would recommend because I would recommend data camp because you're already a data scientist. You're not trying to change your career or anything. So I would recommend like data camp for that and then it would be like whatever extra resource that you need for your data scientists to learn right? Like maybe maybe something that for data scientists is like tap low or something because most data scientists I'd be comfortable like CS background like I do it's like you don't really touch that kind of stuff so you kind of got a figure out yourself. In fact I didn't even know how to use Excel.

2:12:40I don't know how to use Excel. I also think that the 365 data science courses are really great reference material to come back to over time and get something that you should be going back to because it's easy to forget a lot of these concepts and we're like the application of these concepts so it's always a good to go back and review the basics. Um yeah sure I still can't choose whether I should go down the path of data science where stick to software engineer since I haven't developed an interest in a job is the goal at present. Ah now I have a video on that so why don't you check that out and see how you feel about data science versus software engineering. I see it's 5 a.m. in Pakistan. I love them Pakistan. Oh my god I appreciate you. Thank you for being here. Oh my gosh. Um Abraham your med degree of paid off the notes. How is how it got awful? I suppose that is true. It's the only thing that I have learned. Um let's see.

2:13:42Abraham what am I having for dinner? It's a good question. I'm always thinking but um like that's why I'm capping a 7 15 because I'm kind of hungry. I don't know what I'm having for dinner. I'm thinking like barbecue chicken maybe like I barbecue my own chicken but really like pan fry barbecue chicken with barbecue sauce probably. I'm also what I have. Yeah it's probably going to be chicken today. Chicken and rice. I love rice. I just like can't not eat rice every day. I like every single meal. Um can you please take my name please. Haseeb. I spoke your name. Is that what you wanted me to say? Um Omar Hattina I wasn't able to listen to your tips. Can you post them somewhere? Yes I will. I will. Um this live is still going to be available afterwards and I'm going to probably do a condensed version. I might like split them up into like the three separate courses of resources so it's less a huge chunk of information. Um but I will be posting that information. Um

2:14:45and 75 how do you learn from a course? Remember all the content as I'm not a data scientist yet and therefore cannot apply the content in the courses to my job any tips. Well my answer to that is I don't you don't remember all right you you remember frameworks you're like you remember processes of doing things and if you don't remember specific information you know where to look to look back like you know what you don't know so you kind of fill in that information and as over time um the things that you use very very often they're going to stick to your head and the things that you might not use very often but you learned about in the past when they come up you're like oh I remember that I had to like deal with this as like I remember learning about this at some point like I better go and look at how it works. That's a good example. Oh here's a good example like I can ever pronounce this word hetero this this acidity. You guys know what I was talking about hetero um and okay can someone pronounce it can someone type it? I don't even know how to type it hetero diaccessity.

2:15:46Oh Ben I hope you guys know what I'm talking about but it's like that concept right you can like probably forget what you're supposed to do in that case but then when you notice um in your data you're like oh crap you know and then you're like I better look up how to do with that situation again because I don't remember so you go and look that up so it's all you just sit there and remember everything. Someone type without word is can't pronounce it word type it hetero diaccessity then just hetero deacity um i apologize. I hope that made sense I hope that helped um I see I have major in medicine but I'm doing software engineering as well I do coding for like four hours I want to go into cyber security cool very cool. James what do I think about the platform which platform are we talking about? sorry I don't know which platform we're talking about. That's friendly I'm actually doing an intern in amazon as they don't know. Do you know anyone in amazon to share how they're feeling about the workplace and even as rewarding slash profitable.

2:16:48I will not be tricked into seeing where I work I'm kidding I know you're not trying to trick me but um I'm afraid that I cannot answer this question without revealing if I work or do not work. I do recommend that you reach out to people in in your workplace and generally if I'm that you reach out to people while you're workplace people are pretty nice and responsive to what you want to say and you know just kind of reach out and start those connections and start talking to people we're like talking to your manager or like people who are also in your role and just and you'll learn very quickly because everybody likes the gossip and talk about other companies as well. um um see ACTC you're going to school for data science but you're working as a sweet. I didn't see as masters so I'm working as a data scientist so I guess I'm the opposite of you

2:17:51pika ka bootcams versus DS masters. I think it depends on the person bootcamp if you're going for a bootcamp we have to be more self like driven I want to say and for bootcams they generally data science bootcams they have like certain requirements like some of them literally only accept math PhDs or like some sort of PhDs because they're just basically teaching them how to implement stuff um so if you have like zero background I think you should consider that carefully um if you're going for bootcamp. DS masters more conservative approach I would say yeah I'm not going to go into like too much detail because I think like make an entire video about that um but yeah just looking to definitely do your do your research on this and understand your software like for me I would I don't think I would have done a lot of bootcamp to be honest because I have a very short attention span um I just you know like I get bored easily and I didn't have a background in anything that's like

2:18:52technical previously so it would have been quite challenging for me and you have to be like very driven like in a sense you have to go do your networking do everything like very very you know like hands on approach and at least at that time maybe now maybe I would be able to but at that time I would not have had the confidence to be able to do all um see more questions more questions heterodeccessity yeah I'm sorry I literally can pronounce it um I on 75 what I'm trying to explain previously was just that there's going to be concepts that you remember um but you don't exactly remember it like you don't remember the details but because you've seen it in the past so when situations do occur um you would you don't memorize them right you just remember that at one point there was such a thing that you have seen in the past so you go and google it instead and then you fix your problem like it's it's not like you're supposed to memorize every single little bit that you're studying right like and remember it and know how to

2:19:53use it all the time exactly like that's that's that's never going to be the case and it's very very difficult to do so so don't get stressed out about that um ha seat you do freelancing as a web developer but it's two time consuming like working on a website i mean dollar jobs i mean i only data science is less time consuming to be honest uh i guess it depends on where you work specifically um let's see max one i would love to do a comparison between online courses and my data science master's anyone you in one year yeah why not you should do a video about it um um do i work at amazon actc who knows who knows who knows i don't know could be 20 20 percent chance oh duck need thank you thanks so much i really appreciate it

2:20:57i think it's pinned up as well thanks so much thanks for thanks for supporting me okay duck meat your question sorry i'm like kind of scrolling through i don't think you had a specific question right yeah okay i appreciate um sepi actc dependent company for example tech they got fired because you revealed you worked for facebook i if i'm not wrong so yeah i'm not going to call it any more about work that's just that's just not taught i'm scared um let me see him you um what are pros and cons of being a data analyst versus data scientist oh that's so hard is albert around i want albert to be able to try it um

2:22:01again it's going to be like a whole video on it well i'm talking to some i can tell you about some of the differences i feel like some of the differences is that data analysis more like specific to the business role so whatever it is that you're working on is going to be more like product driven or business aligned as opposed to data scientists that are usually less so um it would be more like here's an issue that we have to solve and then you kind of talk to people um to try to figure out like what the issue is and then you go and solve it using like more technical tools as opposed to data analyst it might just be more like fast amount of seats like maybe maybe your boss is like we're losing revenue it's like kind of consulting type questions for example like we're losing revenue why are we losing revenue and as a data analyst you're just going to be like oh crap and then you do like a segmentation and you figure out where you're losing money and then you present it like with some sort of some sort of visualization for example so that's something that a typical data analyst will do a lot well the data scientist you kind of do that once in a while as well but it could be more like we have this like issue that we're trying

2:23:02to solve like how do we detect anomalies in this specific thing and then you're like as data science you might go and implement a model to be able to do the anomaly detection you're still solving a real life business problem but you might just be like spending more time doing it as opposed to just doing like segmentation which is quite quick I hope that makes sense um see oh no the max go bye max bye max thanks for joining um what are machine learning what what to do for ML engineers yeah it's a great question so most machine learning engineers that you know come from a software engineer background because machine learning these days is very CS right if you don't actually have to know how any of these things work to be able to use them very well um so ML engineers generally come from like a you're like a software engineer and then you start playing using like ML packages more and more often and you start playing around with them more

2:24:02and you're implementing it from a software engineering perspective so that's kind of the trend that I'm seeing as a data scientist you do like do models and stuff but it's you're not like using like if you're going to go and use like GPC3 you know like things like that um that's generally what and the engineers are going to be using a lot does that make sense so if you're going to put once be ML engineer I would say like learn to be a software engineer like the things that skill sets the software engineers do is going to be more relevant to know how to code properly know how to use yeah I know the major thing is I know how to code very important and also the software engineering life cycle and these things is going to be more relevant um shamiit what are the advantages of subscribing to the 365 data science program on their website compared to just doing a UDME course uh so if you're subscribing to 365 data science on their website you get access to all the courses on the UDME course you get access to one course um and on the platform you also get more perks that are related um I'm just like let me show you guys actually let me show you guys

2:25:16yeah there you go so this is what the platform looks like yeah this is what the platform looks like so in module one you know it's like it's a bunch of different courses right like it's not just one course um and you have a bunch of different modules and then you also get like support and resources like you have q&a hub um and certifications that's something that you get as well uh so you get like more stuff so I would if you want really want to compare directly like data class data camp and to sit for data science you would be comparing them based upon platform as opposed to reports itself because it's more like cohesive I hope that made sense um that we know platform is a wrong option more knowledge is better regardless of the platform yes that really is true if no platform is the wrong option you really is like whatever works for you um and yeah it's really I think I think it's more about like what works the best for you like what are the things that work really well um that's

2:26:18I think that's the thing that's all at that time we're refraining from saying like you should definitely choose this one because I don't think that's the case at all right like you should choose the one that you feel like resonated the most with you like the one that you kind of see yourself doing the most because that's what's going to matter and each of these are very good at what they do let's just say that like there's a reason why you guys even asked me to review them as opposed to like some other ones right it's because they are very popular and there's good reason too Sebi thank you so much thank you so much really appreciate it this is for keeping me entertained thank you I really thought I was entertaining to you and it's like oh my god it's like by 18th time you go to bed go to bed Sebi we still people to wake up tomorrow I'm very sorry that you have to stay up all the way until 5am to hang out with me I promise next time I'll try for all they'll do it both I think mornings will work better for people on Indian Indian time and call it they call it I see like India standard time I think it is I think it's in New South time right well it's 549 AM play crop

2:27:22okay please if you're from India standard time I asked that you go to bed okay I care about you guys but I feel really bad okay so I'm probably gonna go and bounce now heavy it's Sunday okay I'm gonna lose that me so you go to bed as well everybody else words and terrible like ungodly hour go to bed please don't feel obligated to hang out and stay thank you guys so much again for joining I'm sorry if I didn't manage to get your question like oh putting as well I'm so sorry I didn't I didn't even see that um thank you so much putting like sorry I like totally totally missed thank you I really really appreciate it um yeah like I just I really love doing like live stream to you guys and just I really hope that what I what I the live stream today was helpful for everyone um it's just yeah like I really hope it was helpful I'll

2:28:26be condensing these into like separate videos as well just to make it more digestible and the live stream will still be there as well so hopefully that was that was good and please if you have any like comments um if you have any like comments we're just like suggestions for live streams or anything else please do let me know I do read all of the comments even though I don't really have time to reply to every single one anymore but yes I the things that you tell me the things that you say I really take that to heart and I try to improve over time I hope you guys can see that okay thank you all so so much for coming um please go to bed for those of you that need to go to bed um and yeah I'll see you guys hopefully on Monday's live stream where where my next video see you bye

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