FAANG Data scientist reviews: Datacamp, Dataquest, 365 Data Science

hello friends how's it going sahil love from india yo isn't it isn't it like 3 30 in india that's what i think sebby told me um india time yo it's like 3 31 a.m oh my god thank you so much for for joining despite it being 3 30 a.m really appreciate it how's everybody doing raphael brazil in the house hey sami ooh you actually showed up oh my god summon me demonic music plays in background well no sleep no sleep for you then ibrahim how's it going thanks for showing up hello let's see sami was a sleeping and got summoned now i'm awake wow i really appreciate it i really appreciate it i will try to my best to make sure that what we talk about today is is worth the lack of sleep i'll try my best all right so uh 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 um let's see sebby how's life how's your saturday my saturday was good good i um kind of just chill one over so i actually like went over some of the notes that i made for this live stream so because we have a lot of stuff to cover um what else i do i took i took my dog out um for a walk and to the vet because he needed his vaccinations um yeah and that was pretty much it and then i took a nap right before so i'm all like refreshed now ibrahim 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 is it going yusef hello from morocco hey do we have at least two moroccans down um ibrahim's hair too luke hey just finished popping my popcorn i'm ready for this oh man not a pressure now luke yo you i have so much pressure i got i gotta be more entertaining iman hey there jasmine hello from singapore how's it going muhammad hello from morocco too hey we have a bunch of moroccans here today rajat how is datacam for learning data analysis that is what we're going to cover today uh we'll be focusing more on data science but i'll be going through the platform pretty in depth so i think you'll have a pretty good understanding of for data analysis as well i'm probably pronouncing your name wrong i'm sorry hello from indonesia hey brenda dean bernardani hello miss are you okay um i'm pretty good i am i'm quite okay thank you alfredo hello from colombia hey ellen went through intensive econometrics project for the past few weeks now binging 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 when you're just like procrastinating and watching anime then it's like not does not feel as good mr x start one more minute one more minute let's wait perceive anybody else trickles in sami my first red ice cream i'll if you fall asleep sabby let me know on monday hello from london hey dominican republic yo we have such a great variety of country representing here 1am in kenya too oh my gosh thank you guys so much for joining you know what um i still be recommended next time i'll put out a poll for a time instead because i think that mornings for me might actually work better for for most of you guys here as opposed to 5 p.m and then all you guys are like 3am max how's it going hey love your blazer thank you i'm trying to look up professional today i'm like do i do i look like more professional than than normal martin hello from northern california wow deutschland morocco yo this is awesome and new york 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 going to give like a brief intro what's what's up today um and kind of how this live stream is gonna work so we have a lot of material to cover um but i do want the still to be pretty interactive so please like leave comments talk to each other in the live stream 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 um what i want to do is like after each course i'm going to be covering them one by one but not course like each like program i guess each resource we're going to have like a slight q and a for maybe 10 minutes or so and in the end i'll stay behind and we can have more q and a's 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 live stream chat and just talk to each other answering each other's questions if if you're able to as well all right so we're going to cover today so i'm going to go for an overview of each of these resources we're going to start off with data camp and then data quest and then we're going to end with last but definitely not least 365 data science udemies uh do something about data science udemy complete data science boot camp that's a mouthful um and for each of these i'm going to talk about the time dedication the material and content we're going to dive in i'm going to show you guys what it looks like the learning style whether it's project-based or not because those of you who know me very well i'm very like interested in things being project-based i think it's the best way to learn we're talking about the support system a help system that's available uh 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 um if you choose to use that resource like kind of how that fits into your whole learning journey um and how 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 cam a 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 uh so ned the ceo was kind enough to actually give me a link so i can look at the udemy uh 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 so just getting that out of the way all right so if you also you see me looking down is 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 like a couple affiliate links at the bottom that's like just referral codes and stuff but all my thoughts all right well it looks like nobody has extreme objections here um if you do have an extreme objection do let me know um but seeing as we all seem like it's all good let's get started all right so first one that we're looking at is data camp so kind of just i'll just give you guys like a very brief summary and then we'll jump into the actual resource so datacam 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 and it's supposed to be like they promise that if you use this resource that you're gonna go from like zero to hero essentially like you're gonna go from 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 that's their claim um so they all have let's see what else do i have right here blah blah blah yep that's pretty much it okay let's just get let's just jump into the resource uh let's see how do i show you guys my screen good all right so i think you guys can see my screen now uh let me just jump to here as well yeah so this is what it looks like um 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 you know i'm going through them sequentially and i don't have to like trying to find stuff um but yeah as you can see i already like started doing some of these um i'm working through some of these myself just kind of getting a feel for it i've been testing it out for around like the past couple weeks or so um so that when i'm doing this like i have my i have already gotten like a grasp of what it feels like all right so this is what it looks like so first it's very like you know it's very nice and clean very pretty looking i must say um and here we're gonna look at the career track so i'm most interested in data scientists with python here so um that's what i'm going to like start off by looking at um so they have a bunch of other ones like our programmer data science r data analyst with our python programmer a bunch of different stuff like there's like just a lot of tracks and there's also a lot of courses as well but let's start with the data scientists with python track all right let's continue to track because i already started all right um 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 i realistically i would estimate it would probably take you like i would say like 10 months i think 10 months is appropriate because you know 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 i don't think 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 while working um while working on data camp as well so i think honestly like to be able to go from zero to hero i would say like around 10 months is 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 um and then also like looking at some of the projects and stuff okay so first like you know they go from introduction to python um oh great i scored 113. see gamified it's also very gamify which i actually kind of like um so then intermediate python uh then we have a project over here and then data manipulation with pandas oh um 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 um more 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 functions and things like that object-oriented programming wise but in a data science perspective so we have part one and part two um and then we have data visualization with seaborne um and then importing data into python like how to actually get your data into python and how to clean it so intermediate importing data into python cleaning data working with dates and time this is something that's going to come up a lot if you're doing like time series stuff writing functions in python exploratory data analysis statistical thinking they have a couple courses on statistical thinking um 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 intervals and things like that that you need as a data scientist um 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 like 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 going to stop right there and check the stream see if anybody has any comments over here um i'll talk about the cost um i'll talk about the cost like in a bit like i'll yeah i'll talk about the cost structure it is a subscription-based model um yeah they do have sebi they do have separate tracks with r and python yeah and they also split between like data analysis as well as data engineering um and data science and so yeah 10 months seems reasonable yeah i think it would honestly take you around 10 months or so it's i max like data cam is going to take about 10 months for those who can't dedicate full time i feel like yeah if you're if you're like dedicated to it full time you can probably knock it out in like five or six months i want to say where 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 camp 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 um sebby you get a six month date account absolutely use student tools of github i think there is um that one as well i'm not a student anymore so i don't notice specifics about that all right so um shall we jump into now i wanted to talk a little bit more like kind of clicking and actually showing you guys what one of the classes feels like and then also looking at the projects which i am um 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 um 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 like you know these are things you can do with python um and it's like throughout the throughout the courses it's gonna be a combination of videos as well as like multiple choice questions um and then just like very hands-on based stuff as well so we're like okay we've got 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 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 you just do like whatever is over here and then you submit your answer and you run the code um and then you kind of just have a good 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 um throughout the entire thing like throughout the entire track you want to call that like all the courses it's all pretty much um based like that so how do i go back try to go back cannot how do i go back to my dashboard okay and i guess that's quite motivating as well you're like oh like you know i'm i'm so close i should probably go back so they have like these kind of inbuilt little motivation triggers and stuff which which i think is quite nice um like if that's if like playing games and having points and stuff is your thing i think it's quite nice uh let's go back to where we were which is career track yeah um and then i also wanted to show you guys like for example um after this like 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 tried 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 um i don't really like that it's very built into blank based 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 the practice here um so i guess in this case might as well just do it so define a function named hours two seconds okay so we're going to go like def here and then put hours right and then you just really like submit answer i guess oh 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 um in terms of the assessment checking back to see if you guys have any questions or any comments uh oh also thank you mods thanks thanks mods for being here please please keep doing your mod rolls i appreciate it very much um soy boomer that's your name soy boomer doomer low iq not a statistic i agree with that completely that's actually one of the cons i'll be talking about later i think there's not enough like mathematical emphasis um or at least like logical emphasis it's very much like a sampling of different things and it's very like coding based uh let's see all right so uh tsubima i'll be talking more about like you know after we go to each of these platforms then we'll be doing more of a comparison between them so please stay true all right so i do want to show you guys like you know now that we saw the lectures i want to show you guys what a project looks like as well because i think projects are the best way to learn yada yada they're amazing this is how you actually like you know get to solidify your your knowledge um 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 clicked through all the different projects they're all pretty similar in structure they just kind of get a little bit more complex um as as type goes on but it's pretty much like it's very very guided like they really really hold your hands here it's like you have your jupiter 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 one and then if you actually notice here everything 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 um 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 tax and you go on and eventually you complete the entire notebook so that's kind of what the guided one looks like i think if you are more like a really coated at all before you're kind of like nervous starting off i think it's quite nice that they they do it so guided like it's very very handheld let me also show you guys like what the unguided uh ones look like for all of the guided projects you can also do it unguided um my recommendation honestly is that you do it unguided if you can maybe doing like a couple guided first and then do it unguided um 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 still like first you need to create a matpot lip scatter plot and then you do like these things and you want to exactly make it red exactly make your orange and make it light green um 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 well in reality like most projects are going to be more like much more open-ended right you know here's some here's some data where you have to actually go find your own data and then you're trying to figure out some insights from it um but here it's still like very very guided hope that made sense all right see if we have any comments or questions um view noob data cam always felt too easy yeah i can definitely see that um i think they it's really tailored towards people who just like both have have no experience and are more like i want to say like more nervous and they want to do things from like ground up so that is that i can definitely see why you would think that um elon musk how's that on guys yeah i agree right i feel like it's still very very guided i think by unguided they pretty much just mean like things are not filled in for you already like it's not exactly fill in the game the fill in the blank um so i think it's still like pretty guidance if you're going to use data cam i think you might as well just do it unguided 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 still don't actually know like i gave you a data set even though you did the guided one or even unguided one you probably still have a lot of trouble like knowing how to approach it because you don't really get to like think uh what it is that you need to do by yourself um richard not including any tracking access tableau courses from skills tab yep for sure they also this is just the data science tab there's also like you know skills tab uh where there's actually like projects startup projects like courses on a bunch of other stuff that you want to do that's just that might not be covered until under a career track cool all right so next off what do i want to talk about i'm just making sure i cover everything here ah yes so i want to talk about now that you kind of have a feel of the platform and what it looks like so this is pretty much what it looks like you have your progress your groups um you know all these like things and you have your assessments and another one that i wanted to bring emphasis to is they the support system that they have so they do have a slot community over here let me see i'm pretty sure i already joined the slack community let me just like do it off screen just in case i reveal my password of some sport um but let me give you a second i just want to show you guys what it actually looks like it's telling me to select bicycles right now so i'm just okay i'm just selecting fire hydrants and whatnot okay whatever okay we can come back to that but i also 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 slack community it's really it's really quite active um you can ask questions about like oh like you know this doesn't work like someone will come back and help you so it's very very active there um there's also like jobs but they do have like a they actually recently added like a job a job hub i think they call career hub as well but they do have job postings as well that people list um where they hire directly from people from data camp which i think is quite nice um i don't think it's like as active though like i don't know like how many people actually managed to get jobs from this um but it's nice that it's there so yeah you can ask questions and people will get back to you cool um and then in terms of like careers and prospects i do want to like touch on that a bit um they have like a new career hub i think this was actually like really recently added they didn't used to have this but they do now um it's quite interesting because okay first you have to like make sure you actually like know everything so i didn't complete this because there is like um you actually have to like finish stuff first i believe um and then you know you have to do a lot of these things i didn't finish it myself but what you have to do is i do each of these assessments and just to make sure that you know how to do stuff yeah they're making sure that you actually you know are at a point of entry-level data scientists or data analysts or data engineer um and then afterwards what they say is that you get interview ready with resume reviews and one-to-one sessions with our experts so that sounds that sounds pretty good right so they actually give you one-to-one sessions and they'll review your resume for you and help you on the career track so this is still in beta um 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 i'm not gonna be like i'm gonna do data camp and then i'm just gonna get a job afterwards after that like i wouldn't rely on that myself um in fact like in my opinion data camp is more like a starting point like where you start to learn about just 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 um and it doesn't really it claims that it gets you to the point of actual data scientists in my opinion i'm not so sure about that i think you do need to do more things yourself you got to do more projects yourself and build your portfolio up more for people to actually like look at you more seriously and want to hire you as opposed to just doing it so that's that's kind of my opinion on that um all right going on to cost um so i have it open here all right so this is the um find your plan your personal plan page and um they have like your personal time you have free you have standard you have premium and they have the business ones i'm assuming most of you guys are not interested in business ones so the free one it gives you data camp a try for free but honestly it doesn't like if you get it for free you pretty much only get the first chapter of each course so you don't really get to like learn anything um 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 the theoretical stuff like that they teach you and really like really all the things are coming together so like that's really just a it's really like a what is it called 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 um uh which is like 25 and then they also have one that's like 33 dollars per month which is the premium for learners who want access to all projects i wouldn't recommend it um just because the way that the 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 gonna like it's not particularly unique or interesting so i would honestly just go with the standard one and then after going through the projects and going through data cam i would just go and go and do my own projects instead like applying all the stuff that you learn and from the projects that you do have all you've done at datacam which is applying that to other data sets um and things like that that's kind of how i would go about it would not recommend the premium all right see checking back on the live stream see if you have any comments or questions uh maxwell build annually yeah it is right so i honestly i'm probably gonna cancel um personally i'm probably gonna 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 like i kind of looked at the courses and stuff and i either know most of it or i'm not very interested in learning about stuff like yeah that's but it is um yeah i mean it's subscription based if you do it for 10 months you're looking at like 250 dollars right which 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 um but it is like it is subscription based um let's see uh sorry i'm kind of just scrolling through because i feel like i missed off a little bit um let's see i'm scrolling through to make sure that i'm getting people's questions like me i found data cam had an issue of learning than forgetting it feels like you learn a lot however it's easy to forget what you just learned because of its generous hand holding i agree with that um i can definitely see that happening with with with data camp it's it's very much like uh it's like kind of fill in the blank stuff and even the projects because they're so guided it's like oh like you know i know how to do this thing 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 from doing data i don't think they're there yet yeah um sammy thanks tina for letting us know you're not a robot where 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 i clicked the fire hydrants and i couldn't find the fire hydrants so i guess i'm a robot damn it duck me if you're using data cam please do a lot of projects in your own time to help remember the things you've learned very much upgrade i'll emphasize that as well when i talk about what my tips are for using this resource all right let's see if you have any other questions and things um let's see should she hop do you so you think we should go for dataquest we will see once we get to dataquest i'm being mysterious here um sabi why would they show per month if it's built annually where's the logic uh you know it's it's a marketing thing you know like i don't think they're being deceptive by doing that i think most places do that it's just it's kind of like a marketing thing right it's like if they tell you a huge number then you're like but if it's like you know oh it's only 25 dollars per month you know it's like y'all like that feels more reasonable like it's not that it's not 25 a month it is 25 dollars per month you just build annually for it um yeah uh carol you uh a lot of typos i have it for data guided such and guided projects in the slides the ml courses are decent yeah i think the ml courses they 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 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 alright um i like wrote down some stuff here as well just to make sure i don't forget uh so do bear with me here as i'm looking through my notes which is my terrible handwriting that i can barely read okay yeah so okay let's talk about the pros i think it's nice it's like gamified system um you know the platform is really nice i'll show you guys platform again just so you can see like it's really nice and streamlined um and it has like a very like modern feel to it which i appreciate um and it's also instant feedback and no setup i think this is this is the reason why these like paste like these self-paced like online platforms are really popular and a good reason for that is 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 yourself and you run into errors it's really easy to give up but because it's not there it's like already done for you so i think that's quite nice as a beginner um it's also very handheld that could be a pro or a con depending on the kind of person that you are if you're like more of a nervous person and you like you like that then i think this is this is a great option um and 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 you know like make the projects kind of realistic like their projects themselves are realistic like sometimes the stuff that they ask in their projects i feel like they're not as realistic um but like you know the questions they ask is pretty pretty good i would say um 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 it not 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 and also they do have like seminars um and they have that career tab that's available now so you get the individualized um career counseling and at 25 a month although billed annually it is quite nice i think um so i think you're getting pretty good you're getting you're getting some pretty good stuff for for that price point so talk about the cons all right um also let me guys know uh you guys let me know what you guys think about what the pros and the 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 what everybody else's opinions are as well um so in terms of cons so this is i think duck me was the one that mentioned it where was it soy boomer doomer low iq still love your username um it was like not there's in terms of the ml algorithms and just in general the stats and math reason really wasn't that great it's if you don't already know stats and stats and you're not just using it as a refresher i think you're gonna you don't really cover it as much a guy would supplement that with something else um especially with the algorithms is very much like this is how you cycle learn this is how you import model it doesn't really go into too much detail about how things actually work um from like a conceptual point of view not even like mathematically from a conceptual point of view they don't really go into too much detail about that um also just in general fill in the blank i don't think it's very realistic it's somebody else that mean i think mentioned like you know you like learn a lot of things and you just forget about it because it's just you know when you're actually coding you're not like filling in a blank at all like it has to all come from here right and they kind of cut that part out in their projects and in their assessments um also some other cons that i found um it's more like they don't really cover apis we're web scraping um web scripting 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 i think that's really important um because like a lot of data science uh just like coding in general it's gonna involve you using a lot of apis and if they don't really cover that they do cover like a little bit in terms of integrated inside but they don't really have a second data to get it dedicated to it and i think they really really should like um it's such a big part of what you do as a data scientist um that i think they really should have like actually an entire course dedicated to apis in my opinion uh what else what else oh 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 this teaching style and these things like they don't really like mesh together very well sometimes um and because there is like that video emphasis to it i definitely noticed that when i was going through the different courses let me know in the comments what you guys think as well oh goodnight rajat thanks for joining in i know it's like freaking 4 00 am really appreciate it maybe you can check back in um on in the morning as well and see if there's anything else um i mean there will be more stuff but check back in the morning i 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 me agreed definitely agreed um see so if i'm missing your comments and stuff i do apologize because there's just like a lot of comments i'm trying to scroll through um let's see what am i repeating let's see all right uh guy guy named mad joe ftw fill in the blank would be better an example intro to cs course at a university um i feel like just filling the bank in general it's just not very it just i just don't really like that very much i just don't think it's realistic at all um luke burroughs i like to diverse selection enforces the niche topics from data cap agreed okay so there are like specific topics like they have a tableau one um and there are ones are supposed to be really really good as well i kind of like looked into them a little bit um but i didn't go through them as as thoroughly as this track but definitely agreed on that um herman most online courses don't provide enough math knowledge when it comes to data science is not bad i think it's a um because the thing is that there has to be a balance right like there's the balance between like feeling like you're you're in that instant gratification is really nice that you're coding stuff and you're really seeing results that's really motivating as a beginner but at the same time like that math knowledge like riley's like logical conceptual knowledge it does need to come so i think there has to be a balance between that um in this case i feel like it's a little bit short and you'll have to go and do a lot of work um yourself to to kind of like pad that on um but i under i kind of like yeah i would i wish that there was a little bit more here or at least like tell people here are 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 um lonely ceremonies all core surprises are the same online yeah pretty much they're it's a very competitive marketplace and i gotta say like all the things that we're covering today are are very like they're all very well done right for what they're trying to do um tina how can you be my mentor i mean i would i would i would love to um i just don't have that much time right now i'm afraid uh although i'm working with chronic holder people just to like you know to to like i'm like thinking about starting up a more official program for mentoring all right so shall we move on to uh okay so i some concluding words i'm also just keeping an eye on a clock um 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 um some tips if you do choose to use this resource okay so what do i have written 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 like i said previously a few times already like if you are more nervous and you're really starting from scratch i think you would actually appreciate that handholding and that gratification that comes from that instant feedback um and feeling the bike maybe not so great but i i appreciate the fact that they do help you like one step at a time um it's also good for people who are looking to ups to upskill as opposed to like you know just uh become a data scientist from scratch for example because they have that skills track like let me show you guys again uh what i mean by that they do have that skills track um you know it covers our programming there's python there's sql there's shell spreadsheets tableau and theory as well like there's a lot of um specific and there's finance fundamentals as well that you know if you're looking to upskill you're already a data analyst for example or data scientist and just one you're interested in like specific areas i think it can be quite nice like for example if i only know python and i want to learn about r you know i can just take a few of these um so i can get started on them um although me personally i think if you're going to learn a new 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 uh so it's nice for upskilling so it's not necessarily just dedicated to career switchers and find a learning style if you like talking head videos kind of thing like data camp is good you're going to like data camp a lot because that's kind of the style that they go by and we look at data quests 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 of the learning style uh so some tips if you're going to 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 you to it and all the skills and just giving you a really great basis to work off as well as you know career track and all these different things but i think it's 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 um even better if you start doing like more real life projects i have some i have some videos that are about that you can check out my channel um but like you really need to start applying these skills and actually making your own project doing your own portfolio that's gonna how that that's gonna be how you go from just like that entry just like starting off to actually like being employable really like being someone that companies would want to hire and really like also cementing your knowledge that you learn from data camp um so that's like my first tip um my second tip is that if you're gonna go through data camp don't be like i must take notes on every single little thing like you know like because i especially want to say this for data cam because it's so syntax focused like all the spreadsheets and stuff and all the uh courses they're like very fill in a blank 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 it and trying to remember them um and i think that's that's gives off a 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 don't need to remember the syntax for anything right like if i like literally don't remember the syntax for like really basic python things like for example when i'm like trying to do a filter i like literally forget how to do a filter every single time and i have to go look up the documentation for it that's kind of how it is like you're constantly going to be looking stuff up because the syntax doesn't matter it's more about the knowledge and how to apply it that matters so i highly encourage you to not like get so caught up in the details and the syntax of things um yes what else that's been that's about it i think that would be like kind of my my specific advice for datacamp as a resource all right let's check back in the comments we're going to moving on to data plus next um let me know if you have any feedback as well do you think the way that i'm approaching this like going on the data quest and then 365 data science like do you think this is a good am i covering all the things that you guys want to see um do let me know now before i get started on data quests also if you guys have any other like additional comments or questions on data camp um i think we can spare like maybe three or four like minutes or so to cover that um before moving on see you the only is here i think i would learn a lot of tina start some online course i'm doing a sql interview course that's that's how i'm in the works right now uh wait what is it that we want to know sorry i'm like missing people here oh and just by the way i just wanted to let you guys know so i do um have i'm sorry i forgot super chats and the custom emojis enabled um i got feedback last time when i was doing the data uh data analyst course like live stream that people really wanted me to have that open so that they're yes it does cost like you can send me like a few cents for something um you know uh but it's able to like you know prioritize your questions so people asked me to open that up last time i just wanted to let you guys know that is an option um but please no pressure at all i promise all right um let's see what is it that we want to know okay so manish why data science course doesn't have data structures algorithms which is most asked interview um 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 and 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 um if you're if you're into the lead code world um but data algorithms maybe 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 um like like hash tables like things like for dictionaries if you're if you're pythonic i really don't see that very much and the reason because it's because of data science like you don't really use those data structures um that much like you might use it like in terms of uh like under the hood or you know maybe you're iterating through something but most of the time like the stuff that you're using like you're not directly using data structures so they don't really ask you that much um in interviews from my experience at least let me know if somebody else disagrees um okay so far so good so far so good okay everybody seems quite pleased all right um you could try using slides hmm let me let me think about that i feel like if i'm using slides i don't think it's like better if i just showed you directly like all this stuff where you think like if i just like had a slide that might be better let me know um okay well everybody seems to to be happy with me right now very glad all right so answering a few more questions why why data center oh and i already answered that one okay pride are you not expecting to know these synth i think you mean syntax in a technical interview data science that's an excellent question i want to answer that um so in data science interviews you yes you do need to know the syntax like in a way because when you're actually like coding you you can't just be like oh like i don't know how to actually code let me just write stuff on a pseudo code like yes you do need to know the syntax but but it's so much more important like that syntax will come to you you know what if you actually practice doing projects and stuff like and you know how to do these things like this 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 if you forgot that they're not going to be like oh no we're going to fail you because you forgot like which arguments exactly go into this function you know like you it's they're not going to ask you stuff like that so as a beginner like that if you focus 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 and just try to remember them before your interview um and that that should be fine uh let's see no slides no sides okay then i'm sorry we got we got um i guess we'll go with no slides democracy here um let's see tina what's your opinion on germa tax course i don't know i haven't looked at drama tax course i'm afraid oh um yeah thanks everyone for answering mcflurry mcphee's question yeah don't want hr to come and get me that's why i don't reveal it not because i don't want to tell you guys um all right shall we get should we shall we move on if nobody else i don't see people having any more like questions and specific things so i think we should we should move on sounds good all right okay um let us move on to data quests 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 like where's my where is my okay okay here okay so i'm gonna give just a brief overview of data quests as well and then jump into the data quest platform so data quest on the surface seems very very similar to um data cam 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 data analysts um and similarly it's quite similar as well the data came in 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 gonna be able to be at the level of an entry-level data scientist or a data analyst or data engineer that's kind of the claim that they have as well so let me actually show you guys the platform now uh okay let's see so this is what dataquest looks like here um i have it open to the career track which is like data science and python um and this is like you this is the thing like in the beginning when you sign up for data quests they actually go like oh like what are you interested in like what are you uh you're interested in data engineering or date analysts or data scientists are you interested in getting a job and what not 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 quest is very very career focused so i'm on the current path of data scientist in python um and let me now just kind of in terms of time dedication um i wrote this down as well so it's going to be slightly less time um in terms of estimated amount of time compared to data camp it's 68 hours or 240 hours to 68 68 months and 240 hours to complete for a data science track and for data analyst track um they estimate 160 hours for like four to six months to complete so i would say it's a little bit it's a little bit less than on the data campsite um so yeah i was i was saying is very like career focused and uh they does it doesn't really have any courses that are like specific to each skill set as opposed to data camp um okay let's just go through it quickly as an overview as well just what it looks like and what it covers so okay we are on the current path um 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 um and then you have step two pandas and numpy fundamentals so this is numpy this is pandas um also make an i'll note that after each of these courses you get a guided project um specific to that course and the data visualization fundamentals storytelling i love this emphasis on storytelling like i love the fact that they have this section because it's such an overlooked section when you're actually at the job um so i love that they have that data cleaning analysis data cleaning and python advance and then a project walkthrough love that as well and step three is elements of the command line so they actually unlike data cam they go through the command line so here's the command line uh text processing using it um which i think is quite nice that they introduced that to you um and then the sql is embedded within within it so in data data camp they have sql like courses as well it's not embedded into like the career track so you can always take that as well um but for data quests this sql is actually embedded within this data scientist and python course so they give you the sql fundamentals um and kind of go through like just like the fundamentals of what it is they don't spend like too much time on it i would say um 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 um and then this is what i really like about this as well they have apis and web scraping so working with apis intermediate apis they have an actual challenge where you're working with the reddit api and scraping um so that's really nice because a lot of your work is going to be using apis and then they have a data analysis in business so like different metrics and stuff again emphasis on those soft skills and then after that we have probability in statistics so go through statistics fundamentals over here uh statistics intermediate and probability fundamentals and conditional probability hypothesis testing um i think in terms of stats this is a good basis i think they 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 um you know they have the probabilities their conditional probability based and they also have projects as well so really like that emphasis because i think a lot of times on the statistics side of things it's really difficult to make it as interactive but i really like how they actually made the like the statistics come to life by showing you how it's being used in a project so really appreciate that um and then for step six machine learning intro so first we're going to go with k nearest neighbors um and then it kind of as you go on it will tell you about all the different like cross validations and how to actually turn it up to the hyper parameters it goes through some of the calculus so they do like try to cover some of the math behind machine learning um not just like tell you exactly like no this is how you code it and stuff so i appreciate that is it enough really for you to like 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 um linear regression for machine learning machine learning and python and you know it starts like being it's like predicting the stock markets 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 data camp didn't cover deep learning um i think uh and but here they do have deep learning so it kind of tells you like oh like here's a neural network how does it work and you have like some projects that are guided project walkthroughs and they also introduce you to calgo which is quite nice because then you're like um after you do these projects you it also tells you like here's so many more projects that you can do in kaggle so i think that's quite nice um and then finally step eight which is advanced topics so it's best practices context managers introduction to decorators um as well as command line intermediate so this is kind of interesting um i don't know how many people actually use the command line that much these days uh i definitely i did when i was back in like 2017 2016 um but i think i appreciate it i i do appreciate if you're doing like pipelines and stuff um then i think like working with the command line is it's something that will come up like i don't 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 um yeah and it also uh introduced the introduction to get inversion control so as data scientists we're like notoriously we're like generally seen as notoriously bad at version control and just like writing decent code so that's really changing a lot in industry like the standard of coding for data scientists is definitely increasing over time so it was like introducing git and all these different standards and stuff like it's becoming mainstream now so they do include that as well as map spark and mapreduce you get the big data stuff as well definitely not comprehensive on big data but i i really like how they give you like a sample of that so you're aware of it 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 um any comments questions concerns oh i also wanted to like we'll i'll double click into like what it actually feels like um after if you after addressing any comments or concerns that you guys might have now so we can actually see what it feels like but i just want to like say first off i think you can tell that it's very project focused like everything is based upon projects it's all about implementation um so i think that actually makes the learning experience very different from data cap alright um let's see let's see let's see hmm [Music] what are we about what are you flexing about i will reflex but iit okay i actually can't find a comment is that what you're flexing about okay um a c comment will you will you have another session on youtube media data science course 21 complete data science account that is the third thing that we're going to be covering today after we finished data quest so we did a camp now we're doing data quests and the last one i'll be covering is the 365 data science data science course 2021 complete data science boot camp so stay tuned um let's see nikita 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 um i think you'll start to notice like their style is it's more problem-solving based let's just say that there's like less handholding in it um all right pikachu what do you think of part-time ds masters programs geared towards working professionals um i'm gonna 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 um [Music] mr x where is it possible to learn more about useful statistics uh i think so i per so that's another 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 um 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 again not sponsored by brilliant i think i'm not even big enough or cool enough for them to sponsor me in any way so this is all like but i have actually used brilliant i did pay for it myself previously and i think it's quite good at teaching if you're into that kind of learning style um let's see all right cool let's go on and i'll actually show you guys what it looks like then okay so um let's go here just be able to compare so programming in python let's let's like go with conditional statements let's go with this listener for loops so it's like loading the missions you're like you're on a quest i thought that was nice okay so it's not unlike data camp it's not very like video focus at all in fact it's not video focused at all it's very text heavy so it's like text and implementation text implementation and projects um so when you're learning through it's like towards the end of the previous mission we work with this table like oh this this table over here um and it's like a real table that we actually found as opposed to one that we made up um and 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 add a float um and then you know it tells you a bunch of stuff and then this is how you learn it tells you like to create a list above we typed this out and surrounded the sequence with brackets like this is what a uh this is what a list is um you know like as an example from a it's like a very data specific example because 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 in data class it's very much like it's very very data focused like this is a list in the context of data science um and then you have the instructions here like stored the second row instagram as a listen variable named row two so i already did this previously just to like kind of check it out um so you like row two is equal to this row three like this oh i made a list and then you can be like run code and then you can see like oh um 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 the next screen obviously you get that instant feedback which is which is really nice and you know now we're going on then we're like and then it tells you about like a list contain both mixed and identical data types um and then what's the task over here yeah 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 telling you straight out um let's see what our task is so you can already see the list of the first three rows the fourth element each list describes the number of ratings an 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 it's very like here's some text and it explains things through and then you 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 um they call it guided but this is the guided project here is equivalent to the data camps unguided project that's that's what i would equate it as and it tells you like oh like we learned about all these things now we're actually going to do something yay um so follow the instructions so this is what it goes like add a title write a short introduction where you explain no more than two paragraphs what the project is what your goal is and the title and introduction are tentative at this point um and then you like go on to next and then it 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 shoot oh my god i didn't show you guys any of my screen oh crap i'm so sorry oh 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 i was talking for like 10 minutes or something right oh my god okay let me show you guys what i was talking about now okay you should be able to see my screen you should be able to see my screen now okay i'm very sorry about that let me like go back and actually show you guys like what it feels like again it's really awkward okay so listen lists and for loops sorry okay listen to for loops um okay what i was trying to talk about earlier is that this is what it looks like it kind of it walks through like everything over here it's like this is facebook you know it tells you how you have this 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 introduce like here's store the second row as a list in the variable name row two so you like row two is equal to this list and you i already wrote this out here but um so you like write that out and then you like you know you run your code and it tells you like oh like this variables clash of clans you know and you do that and then you verify your answer by clicking submit answer and it's like nice work uh and then you go on to the next screen and it pretty much just goes through that like in this section it then explains to you like oh you can in arrays you can actually index them we're in this sorry we're talking python now like in list you can actually index each one zero one two three four um and then it tells you like what it means to be indexed and then you go on to try it out yourself like indexing um accessing the fourth element from the list and then also dividing the sum and then moving on they're gonna then teach you more about like loops like how to actually loop through them um and looking at the positions through it so that's kind of how how it works um in in terms of sorry i'm like getting distracted i'm so sorry about what happened previously uh okay um someone could make me into a meme at some point okay so well hopefully that made sense like i hope that was like now that i showed you guys actually showed you guys what i'm talking about i hope that made sense so it's like everything is very it's it's text based um and it kind of explains things to you in the very data specific context um because normally when you 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 in the list in a specific way so i really appreciate that it's very from a data specific uh fashion and then also you like get that instant gratification of like being able to solve the question yourself um and 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 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 have over here so the guided project i'm like paranoid now just making sure you guys can see um what i'm talking about so okay you guys can see so there's a guided project so i was saying earlier the guided project here is very similar to datacam's unguided project it doesn't tell you you don't really fill in 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 they're supposed to like add a title you're supposed to like write a short introduction where you explain and know more than two paragraphs like what the project is what the goal is in this project like 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 they 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 um so it is very still guided but it's not from the blank so in my opinion if you're doing it this way um 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 um yourself here any comments or questions is there anything i should have i can clarify since i messed up and didn't actually share my screen for so long um lloyd it's 1am oh it's okay bye um catch up maybe catch up in the morning yeah next time i'll try to be better on the time i'll always send out a poll next time instead uh jam on ham i love your username you guys have great usernames do you know r yes i know r r is actually the um first language that i started off with so i didn't start off with python actually that's not true i did start up with python but not in the context of data science and data science i started off with r chris schultz 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 through mobile myself that's that's quite nice um did a quest for clash of clans is this similar clash of clans i don't i'm like not i don't really play games that much there's like a very specific games that i'm into that are probably not super cool but i don't know what clash of clans max1 what's the free lifetime access about 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 referral posts like data camp they give me if you go through it you get twenty dollars off and i think i get nine dollars back for it but for data quests i don't give you money for it it's more like you get fifteen dollars off and i get lifetime access forever 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 analyst that google data analysts can't talk to google data certificate i'll like condense it remove my blooper in which i didn't show my screen and i'll i'll post the condensed version as well probably like not immediately like a few days after but this live stream itself will also stay on it will not be removed um palence 3am here is thing of the attended life all right i'm going to move forward then i don't want to take i don't want to prevent you from sleeping more all right all right okay so if you don't have any more questions or comments let us move on so what else do i want to talk about so i showed you guys but i showed you guys the platform um i also showed you guys like the project center 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 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 a support system they used to have a slack uh i have it open here good thinking previous tina um no i lied actually let me go to the dashboard uh support system oh yeah they have a community tab i think it is that actually i did have it open oh no i didn't just kidding oh okay support system so they used to have a slap channel similar data cam 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 can ask questions very very responsive i did check them out um so q and a can post technical questions and get solutions really quickly so they monitor this very well and you could they also have like so you can ask questions on here you can also share projects and it also kind of serves as a as like a way for you to write about it's kind of like medium articles like similar to that like write about 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 like a career the a career board as well um i guess 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 um but yeah they do have a career service as well but that's on the support side so i 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 cam data quest has a much more substantial career service like data camp they just recently launched theirs is still in beta version but for data requests it's kind of like very baked in the very beginning and that's probably because data quest is very career focused like data data plus 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 focused and they've always had like um wikis and just like resumes and guides and stuff and it's just like they just keep on adding on to that so they have like really comprehensive like wikis 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 as well although again i just want to say i even though data quest is more like career focused and it's more project based i still feel like i don't think you can just be like i'm gonna do like all the data quests and then i can get a job immediately after i still think you kind of have to do a little bit more yourself um like you'll probably still build up more portfolios and just do some more projects like end-to-end projects because the guided projects they're a lot 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 um and i think it will be helpful to you after you do you do data quests and you kind of build out your portfolio more and try more things out yourself all right comments comments comments you bring him good thinking previous tina sad violin sounds i know right i thought i was smart um so it's crystals i was first trying it off on my phone data quest i thought it worked quite well data cam is also made to work well on mobile but even on my ipad the videos are often too small to see okay 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 [Music] let's see on 75 i'm afraid i can't really answer that question um hr will come and get me for sure if i ask if i answer that question i'm afraid um let's see all right so if you don't have any questions we are gonna i'm gonna 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 guess say 50 off where you can use my link and i get lifetime access i would appreciate that um but um i think you still get the 50 off if you use the link but up to you i'm okay either way so spring sale you get 50 offers they only have they have two tiers here so data cam has three tiers here you have two tiers the first one is free um again like so the free one here it lets you get access to more than data cam does like 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 quests um i think you're not going to pay for it i think that's whole point right if they released everything then you wouldn't pay for it how are they going to make money um so yeah like you get access to you can try things out a little bit more but you do not get access to projects and also uh the community like a large part of dataquest is about the community it's about like asking questions answering questions job boards things like that you don't get access to that so i think you're if you're going to go for data quests you're going to have to pay for it um so yeah they have a premium version and they just have one that's billed annually and they have one that's built monthly i have oh actually remember this i actually paid for the one that was built monthly because there were some like fine printing if you get the one that's billed annually you have to pay the annual fee you can't cancel it and i was like that was a bit that was a bit sneaky of you so i i paid like 49 i paid like 50 bucks like 52 bucks plus tax for it because um again like i think this is really good but it kind of still fills in more like for the beginner entry level kind of thing for me personally i'm not i'm already a data scientist so it's not like super helpful to me so i don't want to pay more than 50 bucks for it so yeah i'm gonna probably cancel that any comments on pricing any comments for pricing no no no comments about pricing gupta i'm confused for what for what to start with for a data analysis project um i mean these platforms they kind of guide you through it they will tell you what to do for a data analysis project like kind of how to approach it there's also a lot of free resources available as well so you can always check those out too she's jesus or jesus tina has commitment issues they didn't want to commit to the whole year i mean it's kind of like okay truthfully you guys this is because i like i paid 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 it this is really designed for people who are pretty entry level where they're trying to change the career and stuff and for me it's like not like it's not super useful for me um but i did it for you guys do 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 benny thanks for using your own money to review these platforms you're welcome you're welcome you're welcome uh you're hanging out we owe tina 50 bucks buy me some boba sometimes all right um okay so moving on moving forward over here uh pricing and talk about pricing over here what else i want to talk about okay pros and cons all right let's go back to the actual page i'm like too scared to put my actual face on now because i'm like worried that i'm gonna like not turn on my screen so bear with me even though i'm talking you're gonna see my screen here so in terms of pros and cons um i really really like that it's project centric you know that's just my jam i just love it it's very project-centric it's very realistic um from the very beginning um so i think that you can just do data questions and then just be like yeah i'm a field 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 um so i would say it's also a lot more so i'm going to compare this to date i can't just because like they're pretty they're they're kind of similar at least in the way that they market themselves is quite similar um compared to data camp it's less bloaty like data camp was 10 months this was like what how much 68 months i think it was and the reason for that is like data camp has more bloat um like more information that's not exactly necessary for it while for data quests 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 so i really i quite like that um also great support system they have like the support system is like very built out um and very fast response rate um data camp as well very fast response rate so that's just a pro for both of them um and for data quests a lot of career resources and job board so this one is a good is a big emphasis on data on am i showing you data camp now i am damn it data quest okay yeah so another thing about data quests um is that they were focused on career from the very very beginning um it's all about like changing your career and then like helping you find a job and all these different things afterwards so it's like variable topic 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 boards and stuff so it's very built out um i think you if you're going with data quests like definitely utilize that resource and it will be helpful too moving forward even if you don't get a job just from the job post just being able to like structure your resume and they will help you like figure out how to um like get a job essentially you know and i tell you about some like tips that you can use and how to like 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 um in in something in computer science or data science it's hard for you to get that interview because people are like oh you just like self-learn yourself so employers are not sure that you actually know how to do things right because there's no like guaranteed kind of so yeah appreciate that um i also really like how they focus on storytelling and they have used api so that focus on apis as well um 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 um definitely don't skip this the cool part i feel like sequel 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 is a big plus in my mind um 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 i don't think you can just be like i know ml from from doing from doing a data quest but it does give you a really good it gives you more of a basis and it gives you a really good sampling of like different ml goes goes into deep learning a little bit more as well so it kind of tells you what you can focus on in the future um so i appreciate that some cons okay so if this is less hand-holdy um it's more problem problem-solving based so as i was going through some of the lectures and stuff um i don't call them lectures so some of the like assignments i guess i would say like i obviously know python i obviously know sql i know these things already but i was working on it like it's like you know i i was like forgot the syntax maybe or something and that they like go look it up um 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 having an answer like directly uh available to you um i think data quest uh is is really like difficult to be good for you but if you're like more of a nervous person you haven't really coded at all before and you're not very like comfortable problem solving then it could be a little bit discouraging um my opinion but they do have a very good support system so you can always you know just go and ask questions about that um 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 science and python track it's like you're trying to become a data scientist in python it doesn't have a selection of skills um and it's it's just the breadth of it is just not very large uh what they do cover is great and they really they cover the things that are very useful in the job but they don't really cover like some extra information that data camp for example covers uh what else what else what else another con is that it's like it's similar it's another quantization it's not super in-depth so with data quests 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 um they kind of gloss over like stats for example that might be quite useful to you um yeah 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 gonna 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 quests while you do that i'm gonna run to the bathroom all right pros and cons no pros and cons okay daniel what program teaches more about sql and database design it's a good question um i want to say data camp does a little bit more because they have more focus on like specific topics uh 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 purchase oh that would be my guess though let's see sami you're an industrial engineering senior student in istanbul slash turkey i will focus data science and operational research and masters uh what are you thinking about industrial engineering and data science um 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 with data science just because data science like data is literally everywhere right all right um for us how much time to get five block per month salary as a data scientist can someone tell me what luck is is it i've seen that word before by not familiar with it is it like a hundred thousand is that what a lock is um max you prefer dataquest because i won't forget as easily yeah yeah uh polenta i personally prefer data quests as i'm more of a text-based learner type of person makes sense yeah it is it is like a more text based as opposed to video based approach all right um i will go and talk about just kind of finish up on dataquest here and then we can move on to our kind of one so i think in terms of who it's best suited for i think it's it's best suited for career changers um as opposed to like people who want to brush up on my specific skill set um and let's see it's also more compatible with people who are okay with problem solving a little bit more uh like if you run into issues if if because like for data dataquest 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 like solving the problem yourself or like asking people on the community and things like that you're interrupting me interrupting here go go beep over here okay uh let's see yes so that would be like who i think it's it's better for and of course if you're like more if you like the text-based approach a little bit better then you're gonna like data class more and it's like more implementation-focused what do you guys think some of you guys already told me that you like data quests but like so far what do you think data class for data count i will not say anything to the very end okay so some i also wrote down some of the tips that i have if you're going to choose using this resource uh 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 that's very like memorization heavy like for example pharmacology like me um you might be like oh man i can't figure this out god damn it i give up um you have to be more patient and realize that data science itself is going to be very problematic problem solving based um so that's not like a it's not only that you can just what is it called like you he you can't expect to just know everything like straight up from it i just changed my face back but i'm gonna remember this time to actually change it so what else uh in terms of yeah so you have to be more patient um 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 um and you know they are there's a lot of different variations and they really help you figure out uh like you know all these different concepts that 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 teach you about apis and web scripting 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 skill sets and actually apply them to things without guidance um because that's what's gonna what it's gonna be like on the job and also build all that portfolio so whenever you're making a project like this build on a front end to it um just because it's just nice like if you have a visualization or something like that i'll plug it into streamlabs 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 and as part of a portfolio when recruiters are looking at your portfolio they'll be like oh like with all these projects you can easily see the things that you're doing it just makes it look more professional um 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 make some effort um while you're working through data quests to start looking at different jobs and stuff because it's 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 you know that's kind of what they say that will happen maybe that does happen to some people but truthfully it probably doesn't happen 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 um like being able to solve problems by yourself and trying to like a self-directed project so what am i trying to get sorry yeah so okay make an effort like while while you're like going through data quests like look at look at career stuff i look at like the different jobs that are there and don't wait until you finish everything to start looking for a job um try to do that earlier on it kind of feels scary because you're like i don't even know what these what these words mean but it's really important for you to like kind of prime yourself for the things that you need to know and that will help direct your while you're learning it will help direct you just almost like 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 that is all i have for dataquest uh 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 uh savvy data quest seems to be better but subjective in the end completely agreed so i'm trying to present my that's why i'm like i'm not like this is better than the other like i try to like i hope i should i'm doing an okay job i'm trying to present you like just more like factual information actual information were like things that i noticed that i think are good for learning um and i try 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 um because i also i also don't think like one is really better than the other because even though they're quite similar they are serving different populations right for data for data quests specifically they're very career focused and it's not like career track but for data cam you can also be like a working data scientist we're working data analyst who wants to you know just elevate their skill set a little bit more i especially can see if you're a data analyst and you really want to become um you know more proficient in coding for example in 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 quests and just have to like go through everything you know um let's see let's see byron spiral hey how's it going i remember your username remember the it's it's such a unique username uh sohit hey it's mola hello uh so ruja datacan is mostly video based whereas datacore seems to be a lot subjective but the latter has more topics to dive into that's just my take on it okay that's fair that's fair uh max do more portfolio videos i actually do have something in store oh it's more like i'm gonna be building my website soon um so watch out for that which is kind of similar um 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 yes that's why i'm afraid but do feel free to drop and just catch up in the morning i won't be offended um on 75 i just had a look on reddit and someone wrote this i just had a look wait user claims he's a data scientist that makes 250k per year uh for just writing a few sql scripts a day is this false tina what is your thoughts oh 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 a few sql scripts all right moving on we have no more comments about data quests now 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 um and you know they have like course libraries and modules and a bunch of different courses as well as bunch of different tracks however what you guys asked me to review was not the 365 data science actual platform itself um which is you know it has all the courses that are available to it and it's completely separate what you wanted to review is the udemy course which is this one uh what is it called here it is it's the data science course 2021 complete data science bootcamp so this is this is like one of the courses i believe i don't know if it's like different um on the 365 data science platform itself but this is like the specific course um that's the boot camp 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 uh let's see just see some of the notes that i do have okay all right so this they have like a 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 um so 365 data science this boot camp is 28.5 hours long and it's comprised mostly of video lectures although um it does have articles and it has a lot of downloadable downloadable material because 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 they'll be like implementing things that you're supposed to implement along with them um and like play around with it yourself so it can be deceptive it's like 28.5 hours of videos and it's like oh like i can finish this super quick um they don't have like a estimated amount of timeline for how long it will take to complete this i would say honestly it would probably takes you like at least six months i want to say like if you're gonna here's something okay if you really want to just cover everything then you can probably breeze through it um but you actually go through it implement it yourself also my recommended approach is to not just implement things that the video is talking about but actually do projects related to those concepts and just like expand upon the projects that they present yourself i think it's gonna take you like quite a few months due to get that done let me know in the comments what you guys think where anybody says that's taking a udemy course um let me know what you think uh but yeah that's kind of like my estimate i couldn't like find any references for this um 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 complete data science book and they mean that they really mean that it really is the complete data science boot camp it's like you know like what we talked about previously it's like we're skimping 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 in this boot camp let me let me show you guys what what i mean so indus is briefly their descriptions um 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 um advanced applying advanced statistical techniques in python data visualization machine learning and deep learning and they're not just glossing through it let me see there's like if you actually scroll through it um right here they have 62 sections well 63 including the bonus lecture so it's really really substantial over here um 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 um 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 um 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 what this is then definitely go through it um it talks about what machine learning is as well and some of the career stuff um and then they go on to probability and they really cover probability quite well so they cover all these different things and then combinatorics you know they they covered underlying math behind it um bayes inference like that's substantial as well over here uh probability distributions and then after covering that they all they go into like statistics as well it's like you know descriptive statistics and this is like a course in itself like if you're taking like a university course each of these sections are essentially a course uh 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 um so yeah they go through all of this um descriptive statistics differential statistics confidence intervals and like and it's like not brushed through all we're like literally learning it very specifically this is t distribution how to actually use it uh what's a t-test in reality what does it mean for confidence and supposed to overlap what does it not mean for it to overlap um cohen's d like all those different things they cover that hypothesis testing and then for like python is again it's like each part is its own course seriously um very in-depth like kind of going from ground up and again like when we're going through them they have these resources on the side so i would actually recommend you download all the resources in the beginning as opposed to download each one of them because it's actually quite hands-on like it's not really just you sitting there watching videos like it's quite hands-on um in the math section it's like i actually want to say the masterpiece is like relatively hands-on as well yeah i 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 you know that that makes sense right but it does it is still quite hands-on um in doing that especially in the programming part it's like you're going to be following along and doing all these things and it goes all the way up um in terms of like python tools all the way to modules and explaining to like what modules are and it does a very good like basis right it's not just like here's numpy here's pandas and let's just do numpy pandas and explain like 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 um yeah and then more statistics like actually implementation of statistics within python very very thorough i really must say myself like like holy crap this is like thorough i think a multi-linearity like none of the poppers that we talked about earlier cover this at all and it's like we were really implementing all the things that you've learned right so really appreciate that um and then it for the ml stuff like gaming's cluster and things like that they also they also really like to show you the math behind it as well like it's not just telling you how to use stuff um it really goes very very in-depth like how it works exactly um 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 um and we have a whole section dedicated to linear algebra what's matrix linear algebra the algebraic geometry um 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 dive on into deep learning which makes sense um also entire section dedicated deep learning i want to say like the platforms that we talked about earlier uh so data came 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 explained it does explain what it is but like not at this level definitely not this level all of that dedicated to it and then you do implementation of that using tensorflow uh 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 um absolutely in-depth like i just that's it i'm like i was actually really very surprised by how in-depth all of this was um and then one more notable thing before moving on so like i'm kind of just like covering them really quickly uh so but i do want to make note of one thing 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 your project um 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 very real life um and similar to what you would actually do if you are studying the project which is called absenteeism um at the workplace so they go through like you know this is the plan actually how to plan your projects out which is very important because if you don't plan your projects you're gonna have a bad time i learned that from from experience but they tell you that so you don't have to do it like that um and then it's like here's how you approach it and then you know pre-processing you go through it um so they demonstrate how to like go through each of these and eda i'm not gonna go into like too much detail about this uh but like you can also like look look at it on the like course website itself but it really just goes from zero all the way up um like selecting how to use ml models for it and then in the end they also like showing you predicted outputs um and they and then also in terms of like tableau 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 thorough it was like and it's very realistic um i would just call that realistic very realistic yep and then they also give you additional tools as well so there's just like so much information that's that's what all i can say like there's just when they say complete data science bootcamp really really mean complete data science bootcamp all right so check them back on the comments any comments um pull us 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 would be very hard to be in such depth um that you're talking about any comments questions no comment no question okay cool any comments no questions that is fine um oh link is too big for the chat oh for this chat maybe put in discord okay cool all right so if there's no like questions or concerns let's dive deeper into i'll show you guys what it actually looks like um so in terms of like the learning style it's a very like video focus so and very animation driven like i'm just thinking about how much i prefer this is what happens when you're like you're youtube like wow there must have been so much effort in terms of editing but it's like very pretty like the animation style uh very well visualized here let me show you like for compliments for example since i have it here already um yeah you guys can see right i i can't get the audio to play because then i just can't figure it out pretty much but this is kind of they talk through it it's like very clearly animated and very clearly like spoken as well it's like this is a coin toss 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 um that might give you a good idea what like demonstrations look like what the demos look like okay um why don't we look at numbers and boolean values in python so yeah there's python and it's like see how it's like literally animated with boxes it's not just like some some the instructor's just saying stuff like they actually animate each step which i think is is very nice it's like so clear cut whatever it is as they go through these demos yeah hopefully that gives you a good idea of kind of what the lectures look like um 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 um and it i do want to make you know 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 these projects that the instructor is showing you um by yourself you should be doing that but you could technically like not do that um 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 um and and it's gonna depend a lot more on your own discipline to actually work through it and not just like watch the videos and just assume that you know how to do it which is a great trap watching 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 um and yeah so it and the actual like case study itself is like an extended project essentially so there is that project and i if you're going to use this like highly recommend for you to actually follow through um 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 a good idea what the learning style feels like um let's see that's pretty much comments questions i'm watching the chat right now um okay i'm gonna move on and then we can discuss things a little bit longer oh moon you have a question how long is the statistics point it is significant um i feel like if i move this over oops and move this it's very significant like i want to say at least a third of the class i want to say is is his stats maybe like a fourth something 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 machine learning stuff um and it's not stats but like the math behind machine learning as well so yeah it is very significant all right um so let's talk about support um so i think support it's com like because it's a course right so it's it's not like you there's like some form dedicated to it it's on udemy it's like the same as udemy's other support channels i'm just opening that up right now for you is the q a so you can just like make you can ask questions and stuff um and it's like a lot of people have taken this course is a really really popular course um for very good reason in my opinion so most of the questions that you're going to ask is probably already 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 um so i would usually evaluate in 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 and what kind of careers that you can expect but there isn't really like direct career support because it's not meant to be meant to do that let's talk about cost um let's see i thought it opened up the cost but past tina was not intelligent enough or like did not have enough foresight to remember the cost i was just going to tell you if you want you don't have to see it just just do you believe me if i tell you what the cost is even even if i don't show it to you so um it was like if you're just buying it directly it's like 99.99 but it's you to me we all know udemy it's like there's a way for a huge ass sale um and like right now there's a sale and it's 14.99 so it's so cheap when you get lifetime access to everything all the resources all the articles like this is just such great bang for your buck here i'm like this is insane right and it's not subscription-based so you don't have to pay for a monthly or anything by one time and it's yours forever incredible all right let's talk about pros and cons um well let's see if there's any questions first paulette's the guy who teaches the python course and knowledge for phd i'm not surprised not surprised at all looking at the quality of it uh then daniel wow that's impressive yes i am very very impressed by this by this the just like the breadth and depth of the things are offered like the lectures just people just clearly know what they're talking about like these are experts in their field um let's see it's a hush i feel like there is no defined data science rules and most of the companies is still pretty much refilled um i'm not going to comment on this because i kind of just want to focus and get through with this part first uh so let's just feel free to comment afterwards okay we can talk about this stuff afterwards so pros and cons pros and cons okay so in here's a pros i just think it has amazing breath and depth it's just really pretty incredible by how much material it is and 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 to actually perfect them over time like this is not someone who's doing a rush shop or anything like that like not i'm not saying like you know data camp or data quests they were doing a restaurant or anything they definitely weren't either but this is just it's very very well productionized um and then and the information that's being covered it's taught by people who really know what they're talking about you know um what else do i have as a pro yeah so i can't read my own handwriting right now yeah so kind of related to that it does cover deep learning and it covers the math behind both deep 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 that's a pretty great pro um and as i was saying earlier great bang for your buck like at least for me was on sale for 14.99 like holy crap even it was it was 1999.99 like this is like such a great thing for your buck here and it's not even a subscription based um so i don't think anyone complained about the price here okay and then another comment that i had as a pro is that it's very hands-on um and it's very applied even though it is video lectures but because you're supposed to be following along it might be deceptive into you thinking you're just gonna sit there and watch videos but it's not supposed to be you're supposed to be following along a lot while you're while you're actually going through it so it's very very hands-on the case study is like very in-depth and very very realistic so if you go through that case study and you follow along if you come out you can come across like another project that you want to do it would that leap between doing your own project is going to be less um because you already did something that's quite comprehensive you know with that being said though because it's not project focused necessarily that'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 like you don't get to experience as many projects uh whereas many like you know small little pieces to 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 i really think like 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 um i actually just realized you guys probably can't even see the animation style because udemy blocks the um animations don't they well that's awkward well 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 um anyways moving on more to the cons um yeah so it's if you don't have the discipline to actually follow through with it that that could be an issue and another thing that i want to talk about is that because it's actually covering so many of those fundamentals first that can be difficult for some people like i said earlier there's like this balance between like implementing stuff and keeping your interest and getting that gratification and then learning more deeply about it as opposed to just like learning all the fundamentals you know and then they're diving into that because it's gonna take like sitting through a bunch of math lectures that are super theoretical um even though you're like applying it but you're not applying it to like actual data science or data sets um that you're interested in that could that could be like not super motivating 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 let's see 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 um that are like what's data science like all these different information like what the field is what the careers are and debunking myths and stuff and that's like a significant part of the course um this is more like a if you know stuff about data science at all you probably don't really need to go through that part very much i think you can probably condense this through just a few lectures honestly like not necessarily go into so much detail about it um so i just think it's 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 pretty much all i have for the cons um so i want to talk about who is best suited for moving on next oh yeah let me know in comments what do you think the pros and cons are as well very interested in seeing um so whoever 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 um like this is this is good for people like that you want to if you want to like understand things at a deeper level um i also think that this is good for people who are motivated enough to actually follow through and patient enough to like go through all the lectures and actually like implement everything yourself as opposed to just watching the 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 and 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 um by someone else right it's more like self-direction in that sense so i think it's quite it's it's 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 the platform itself pushing you to be hands-on i need some water 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 um it's just gonna if you're gonna buy this course it's gonna be for learning like you're not you shouldn't be like i'm gonna do this and then just get a job using this um although on actual 365 data science platform they do have courses to cover interview process and things like that and um i have a a link for 57 off for the like full data science platform which is like subscription based itself and it offers different modules but for this course specifically i don't think we should talk about career stuff so that's not that's not really really what it's meant for all right let me see your pros and cons um savvy why are you still awake oh my god go to sleep send me all right um i'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 um let me see arturo should you binge the chorus and do projects or go along with the walkthrough i think you should go along with the locks but do not binge the course um just you're not going to remember all that information anyway if you don't start implementing them so definitely don't just watch everything um let's see daniel how did you get to work as a data scientist without experience i'm going to explain that later just tldr i did have experience uh let's see soy boomer doomer low iq be no flu in python you pretty much are a master i guess you're a master of python at least semi you guys did see the animations that's good i guess like sometimes you get to see it uh sometimes you don't knob 365 would be way too crazy and death for me yeah i can definitely understand that so that's why i was like emphasizing the point like it has to be someone who like really wants to know and know stuff i also want to say like if you're going to be taking this uh 365 i think i think it's a bad idea just to use it as more of a resource or a guide as opposed to like you know just like go through every single thing and then you know do like that i don't think it's a bad idea just to use it as more of a supplementary information um as you learn more and more about data science i start implementing your own projects and stuff and they use it more as like supplementary and maybe introducing you to like deep learning like topics like that in the future as like a beginner course i can see it as being a bit overwhelming for sure virus prio just got the udemy course can i start in a few days awesome let me know how it goes um this max ball this course looks fantastic yeah i mean it's pretty it's awesome it's pretty in-depth poland i love knowing my stuff so i prefer 365 data science and i have an active subscription with them cons i find it more difficult to actually follow along yep i definitely can see that um happening i think in general it's just like because it's not that instant gratification like applied learning kind of thing it's harder to like stay focused and you know be disciplined enough to implement stuff definitely understand okay cool um if we don't have any more comments let's see what do i have okay last the last thing i want to say is like for tips for drawers that are choosing this specific resource um my first one is that i think i've kind of like just rambled on with this so many times i'm just gonna say it one more time i apologize for being rambly implement things yourself uh 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 udemy course at least um it is 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 a really really really big trap so please please please don't do that download everything yourself um and actually go through it and 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 um but especially important for this resource because it's covering things in so much depth and the video style not trying to follow along is just more passive than you implementing 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 um it's it's hard to conceptualize these things if you're not implementing them and then you probably just like forget very easily or just like not understand it at all to begin with um and finally my other tip is that don't be so afraid to skip over some of the concepts um 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 um i'm just gonna switch my face again it's not something that is like you have to go through every single step to master data science um and i my hunch is that this is the kind of course where you're going to be going through once 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 they're so in-depth there's so much information that's being involved i really doubt that you're able to learn and understand everything perfectly the first time around you're gonna come back and re-reference them over and over again um so it's it's just like don't see this as like uh let me do this one time and then forever be okay kind of kind of course and also don't be afraid to like skip over some concepts in the beginning if they're too difficult or you don't really understand what they're talking about that's okay right implement the stuff that you can implement um 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 resources for tips on using this resource futures i'm done with my little notebook all right well we covered through everything oh are you guys tired i think i'm like i was pretty pumped i'm not tired i'm like it's like sigh of um we did it we went through it i'm pretty pumped i really love doing these live streams and i love chatting with you guys as well all right i'm gonna stick around um or i'm gonna say like 15 minutes maybe that's i'm gonna set a cap 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 subby tina reading her nose like she's reading scripture do you think i want to see how ugly my notes are though i'll show you like i can't even read my notes you see it it's like if you see me like going like this it's because i can't i can't read my room um let'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 learn is that is that i don't think you meant it to be like you should choose data account but i'm sorry which data camp would you recommend them to learn and what additional assignments would you ask them to complete yo that's hard it's hard i think it really depends on what kind of company you are like and okay here 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 um for that and then it would be like whatever extra resource that you need for your data scientist to learn right like maybe maybe something that for data scientists is like tableau or something because most data scientists at least if you come from a cs background like i do it's like you don't really touch that kind of stuff so you kind of got to figure it out yourself in fact i didn't even know how to use excel i had to learn how to use excel um i also think that d365 data science course is a really great reference material to come back to over time like it's something that you should be going back to because it's easy to forget a lot of these concepts or like the application of these concepts um so it's always 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 or stick to software engineering since i haven't developed an interest in a job is the goal at present well 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 has it's 5 a.m in pakistan love from pakistan oh my god i appreciate you thank you for being here oh my gosh um ibrahim your med degree 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 abraham what am i having for dinner it's a good question that's what i was thinking about i'm like that's why i'm capping at 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 what else would i have yeah it's probably gonna 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 hey teen 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 but i will be posting that information and 75 how do you learn from a course and remember all the content as i am not a data scientist yet and therefore can i apply the content in the courses to my job any tips well my answer to that is that you don't you don't remember 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 like i remember learning about this at some point like i better go and look it up how it works that's a good example oh here's a good example like i can never pronounce this word hetero this this you guys know what i'm talking about hetero oh man okay can someone pronounce it can someone type it i don't even know how to type it i don't know i assassinate oh man i hope you guys know what i'm talking about but it's like that concept right you like probably forget what you're supposed to do in that case but then when you notice it um in your data you're like oh oh crap you know and then you're like i better look up how to deal with that situation again because i don't remember so you go and look that up so it's all like you just sit there and remember everything can someone type what that word is can't pronounce it or type it heterodynacity just hetero heterodynacity um i apologize i hope that i hope that made sense i hope that helped um i see i have major 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 random i'm actually doing an intern in amazon as data analysts do you know any anyone in amazon to share how they're feeling about the workplace and if it is rewarding slash profitable i will not be tricked into seeing where i work i'm kidding i know you're not trying to trick me but um i am 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 i find that if you reach out to people at your 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 or like talking to your manager or like people who are also in your role and just and you'll you'll learn very quickly because everybody likes to gossip and talk about other companies as well um let's see actz you're going to school for data science but you're working as a suite i did a cs masters so and then working as a data scientist i guess i'm the opposite of you pikachu boot camp versus ds masters i think it depends on the person boot camp if you're going for a boot camp you have to be more self like driven i want to say and for boot camps they generally data science boot camps they have like certain requirements like some of them literally only accept map 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 a boot camp yes master is more conservative approach i would say yeah i i'm not going to go into like too much detail because i can 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 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 technical 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 like maybe now maybe i would be able to but at that time i would not have had the confidence to be able to do well uh let's see more questions more questions heterodiastasis yeah i'm sorry i literally can't pronounce it um i on 75 what i'm trying to explain previously was just that there's gonna 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 use 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 [Music] you do freelancing as a web developer but it's too time consuming like working on a website i made all the jobs i mean i don't think data science is less time consuming to be honest uh i guess it depends on where you work specifically um let's see maxwell i would love to do a comparison between online courses and my data science masters nyu in one year yeah why not you should make a video about it [Music] um do i work at amazon actc who knows who knows i don't know could be 20 20 chance oh duck meat thank you thanks so much i really appreciate it i 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 no okay i appreciate it um sepi actc depends on company for example technique got fired because he revealed he worked for facebook if i'm not wrong so yeah i'm not gonna comment anymore about where i work that's just that's just not hard i'm scared um let's see hugh 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 china um again it's gonna be like a whole video on it well i'm talking some i can tell you about some of the differences i feel like some of the differences is that data analyst is 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-to-line 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 analysis 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 some sort of visualization for example so that's something that a typical data analyst will do a lot with 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 to solve like how do we detect anomalies in this specific thing and then you're like as a data scientist 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 hope that made sense um let's see oh no the max go by max bye max thanks for joining um what are machine learning what what to what to do for ml engineers yeah it's a great question so most machine learning engineers that i know come from a software engineering background because machine learning these days is very cs heavy 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 and 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 gonna go and use like gpc3 you know like things like that um that's generally what ml engineers are gonna be using a lot does that make sense so if you're going to want to be a ml engineer i would say like learn to be a software engineer like the things the skill sets um that software engineers do is going to be more relevant to you know how to code properly know how to use uh yeah the major thing is i know how to code very important and also the software engineering lifecycle and these things is going to be more relevant um shamit what are the advantages of subscribing to the 365 data science program on their website compared to just doing a udemy course uh so if you're subscribing to 365 data science on their website you get access to all the courses on the udemy 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 yeah there you go so this is what the platform looks like there you go 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 as well 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 really want to compare directly like data quest cam and through secure data science you would be comparing them based upon platform as opposed to the course itself because it's more like cohesive i hope that made sense um that mean no platform is the wrong option more knowledge is better regardless of the platform yes that really is true no platform is the wrong option it really is like whatever works better 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 i think that's the thing that's why i'm refraining from saying like you should definitely choose this one because i don't think that's the case at all okay 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 savvy thank you so much thank you so much really appreciate it this is for keeping me entertained thank you i'm really glad i was entertaining to you and it's like oh my god it's like 5 18 savvy go to bed go to bed semi will you still be able to wake up tomorrow i'm very sorry that you have to stay up all the way until 5am if you're hanging out with me i promise next time i'll try for i'll do a poll i think mornings will work better for people on indian indian time and colleagues they call it isd like india's standard time i think it is i think it's indian time right oh it's 5 49am holy crap okay please if you're from india standard time i ask that you go to bed okay i care about you guys but i feel really bad um okay so i'm probably gonna go and bounce now savvy it's sunday okay i'm gonna leave somebody so you go to bed as well everybody else wears a terrible like ungodly hour go to bed please don't feel obligated to hang out and stay um thank you guys all so much again for joining um 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 pudding 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 streams 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 i really hope it was helpful i'll be 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 or just like suggestions for live streams or like 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 my next video see you bye you

Channel: Tina Huang

Views: 33,598

Keywords: dataquest, datacamp, 365 data science, dataquest review, datacamp review, 365 data science review, dataquest vs datacamp, data science, tina huang

Id: pgHwk5HvHmI

Length: 140min 45sec (8445 seconds)

Published: Sat Apr 17 2021