I've just recently finished their Data Scientist Professional Career Track and I was very satisfied with it. Indeed, datacamp is not the place where you can learn theoretical concepts. If you want to learn those, search for youtube videos, articles or go to the university at the end of the day :D. As I feel, datacamp is more about implementation of the concepts. As I have solid theoretical background from my master's studies this track was just as a quick learning how to put theory to practice using Python instead of R, which is usually used at statistical classes. So, depending on your initial goals, datacamp can be a good or a bad place to learn.
@@olzhasshortanbai6012 Yeah, kind of. The initial name is Economic Data Analysis, Specialization of Data and Modeling. Statistical and Prob concepts + I spent an exchange semester focusing on ML and Deep Learning.
I've never used DataCamp, but I recognize what you're saying about learning things in a "vacuum". The concepts just don't stick that way. Nothing beats real-world practice, working on actual projects.
Here's my experience with it so far. I've spent a lot of time developing proper study techniques, and learning how to learn. DataCamp works REALLY well for exposure, and showing concepts that you should learn. What I'll do is do the exercises, practice them, and write down the concepts. After I'm done I'll take a random DataFrame and start using the skills they taught in a HANDS-ON project. If you start with the right courses, they DO explain topics like libraries, but there is a lot of assumed knowledge. There are ways to make Data Camp work well for you, but if you're truly a beginner, chances are you won't learn it the way you should. I already had a lot of my study methods planned out, and had a lot of understanding to begin with.
That's some great insight, and I 100% agree. Now that I know the fundamentals of data science data camp is incredibly useful, but as a true beginner...
Agree as well. Im currently a beginner/intermediate with the programming stuff and I just started to see that you'll actually learn from doing projects outside of lessons or problems within the courses. Just completed my first project through DataCamp and it was actually time consuming given how quickly I got through a few courses in the SQL fundamentals. I also agree with you that its not an issue with DataCamp as I came from doing some courses in Coursera's Data Scientist Cert Program and felt that DataCamp has done a way better job with introducing concepts and allowing users to practice it. I plan on doing what you have spoken about in terms of downloading datasets and practicing bits of things I learn from the DataCamp courses on them as I progress. I know it will be tough because I've tried it before but gave up too soon because I didn't get the results I wanted instantly. But I think understanding that in the realm of Computer Science and many other things in life, there will be a lot of failures and confusion on the journey to get the results, but with enough time and practice the results will come through and I'll be thankful for the learning experience and improvement gained from it
I'm doing it this way too, especially using Anki flashcards and working with the datasets locally with jupyter notebooks or just typing into notepad. Having great results, but it requires a lot of dedication and patience. just going through the courses and working the exercises wont be enough for retaining the information
Some of the problems described can be solved by working with projects related to the course material (a longer, larger assignment with analysis of some data sets, etc., from start to finish) and then entering competitions. I use DataCamp with some programming background and it definitely helps. Overall, nice summary!
I'm very comfortable saying the same about the context for almost every degree and course I've ever attained. In my experience, the academy rarely provides updated context, making an autodidactic spirit necessary. The vast majority of my learning happened outside the class even when I was studying in the best colleges. That is how I think Datacamp, or any course to be honest, should be aproached. Although there are courses in the market that offer good projects to apply concepts, most of the time, they provide clean datasets free from real-world problems. I don't think it's realistic to approach any course with those expectations. Instead, we should take responsibility for this ourselves and be more intentional with our studies @@datanash8200
I have done a lot of online learning across a variety of platforms. In almost all cases, you can roll your way through the material only half paying attention and still finish with the cert. It's important to try hard to really pay attention as you go and genuinely learn the stuff though. If you have your own work or projects to do on the side in addition to the online learning, it makes things a lot more meaningful, and the content will still better. Just my two cents.
As someone who spent 80 hours on the Datacamp course for data scientists, I can relate to this video. It only helped me because I work with R and SQL for my job but most of the knowledge I obtained is lost due to some of the reasons you described. Then again, that happens to most knowledge that is obtained but not used, right?
I'm using datacamps resources and also auditing some of Andrew Ngs ml and deep learning courses so I have a grasp on both the maths and syntax... I'm seeing improvements
I understand what you actually said. I still subscribe to DataCamp now because of some codes I can use.but in terms of learning, your video was spot on
i have been using DC and you are right, but it is really good place to start the solution is to work on personal projects and rewatch videos to understand concepts overall a 7 out of 10
DC is actually very good if you have the right expectations. I wanted to gain familiarity with the topic of data science and some of its specific tools. And I'm pretty satisfied with the structured learning path. I already have many yrs as a pro developer, so I get a lot of mileage from an overview. In my current job, I'm already applying the things I"ve learned.
I never used DataCamp, but almost my entire AI/ML/DS curriculum was self-designed and done through Udemy. The courses I took combined coding, theory and projects in a way that not only taught me what to do but why I'm doing it. Thus, I actually use the materials learned on my job. My first machine learning project at work is made up of code snippets Frankenstein-ed together from various projects from those courses. I'll always plug Udemy or at least the specific courses I took. They literally helped me switch career paths.
@@datanash8200 For the beginner to junior level student, I'd recommend Machine Learning A-Z by SuperDataScience. It's a 40+ hour bootcamp-style course taught using TensorFlow. They also have a few curriculums made up of 5-6 individual courses depending on the direction/career path you wish to go. If you want a deeper understanding of what goes on behind the scenes of ML libraries, I'd recommend anything by Lazy Programmer. Do not let the name fool you: his courses are anything but lazy. I'd recommend anyone to brush up on calculus and statistics before taking his courses or you'll likely get lost. His deep learning courses will have you coding neural networks from scratch, not using TensorFlow or Pytorch, although he does have high-level courses dedicated to those libraries too. Again, LP courses are not for the feint of heart, but boy do you really learn if you can push yourself through them!
Also, it's pretty much an open secret now that Udemy has flash sales that last from one day to a whole week several times throughout the year so you can purchase a $200 course for like $15. I have never once paid full price for any course except a few that are not subject to sales discounts. If you miss a sale, don't worry -- there'll be another one in a few weeks. You also have lifetime access to purchased courses, so there is no subscription fee unlike with some other platforms.
@@reygaji4001 My curriculum consists of about 30 courses, and I found out later that I really didn't need a number of them. If you tell me in which particular direction you'd like to go, perhaps I can better tailor my response.
As an aspiring IT professional that wants to get into data, this is refreshing to hear from another person about their struggles in self-learning. I have feelings of regret for using online resources such as data camp to learn data as a way to teach myself only to end up hitting a dead end the moment I had to start creating my own projects. I spent nearly 5 months on this and it definitely feels like I wasted my time since I am planning to get into a more generalized IT role such as help desk instead of getting into data right away just to get my foot in the door in IT. I wish I did this right after graduating from college if I had known the competition for entering data science is tough.
DataCamp has been absolute great for me. The reason is because I am more familiar with 90% of the theory for things they teach. In 2023 I spent an entire year coding with Python only and working with Numpy,Pandas and Matplotlib. Moreover in 2023(my final year) statistics I did Sampling Theory,Linear Regression , Time Series and Statistical Analysis for Experiments as individual modules at Varsity. I did Stats for 3 years,hence I enjoy it more than anything because I also do projects after learning. And to be fair,I agree with you,it's not that good for someone who doesn't know fundamentals and statistics
I really like your analysis it is very insightful and truly reflect some of the loopholes in their courses but I would say your course selection wasn't too ideal if you didn't have any prior experience about the data analytics you should have choose the data literacy track it will help you make your fundamentals strong and when your fundamentals are sorted you can easily grab the practical and theoretical concepts quite easily. And I can assure you DataCamp user interface is one of the best in the industry because I have done some courses on Udemy and Coursera they are too tirelessly long and sometimes irrelevant as well so it's very hard to keep focused and motivated throughout the courses.
I would suggest some Coursera courses that I feel really address the problem. Google's Python crash course. A single course. You learn the concept and go the exercies and bam. A task that requires so much thought(at least for me) that uses a simple concept like a loop. Even though I took this course after an entire Python specialization, I felt like it solidified the ideas more than that entire specialization. Python 3 programming specialization by Michigan University. Again it addresses the problems you talked about. After learning the concept, you go to a lab where you do like 10 different exercises on the subject. I would recommend that people take these courses/specializations because they greatly helped me with learning.
Totally agree. Everywhere is pushing short bootcamps and these online courses and I am being told that my MSc in Data Science is pretty useless in comparison. This is rubbish as I learned so much during my degree, mostly from my own research and working and struggling on individual projects and testing myself. I had a base level of knowledge of basically zero having no coding background. The bootcamps and short courses are so shallow in comparison to this experience.
I love datacamp's style. The points he talked about are true about the shortcomings of the courses offered because it focuses on the technical skill. I realized if I also started on Datacamp without prior Knowledge I would have had a very shaky foundational understanding. But luckily I finished Coursera Google Data Analytics first before Datacamp. Coursera courses lack the same amount of application like Datacamp, and Datacamp courses lacks the same amount of theoretical knowledge, concepts explanation and giving real work scenarios as Coursera. In the end I think one source won't really be enough.
Thank you for you perspective. I actually find it refreshing the way you analyzed the whole learning experience. I feel like a lot of online learning is unfortunately that way, hard to link everything together. Also, I believe that's how the American education system teach. A lot of facts up front, then later the foundation, which I believe that's why a lot of kids failed. Because they keep piling on things to learn, but not how to apply it. So after a while they get frustrated and give up. In your quest, did you find another learning platform that you would recommend? I appreciate your work.
If youi want to write a novel, you can't spend all your time reading grammar books. But that's how many online courses are constituted. The courses should be made up ONLY of projects, progressing from simple to complex (you can learn the rules and syntax as you go), so you get a better understanding of how to code in the real world.
Interesting take. However, I do think like most learning, the key is practice practice practice... Datacamp provides the basics, if i may. On that foundation, you build.
Hi, I hope you read this. I love the way you are progress on your journey. I am first year CS undergraduate and I aspire to be data scientist/ML engineer. I would like to let you know that you are one of the most relatable youtubers in the field of data science as I feel that you had a journey similar to the one we'd probably go through. I promise to stay here and see your journey and keep you updated about mine as well!❤
So here’s my situation. I am currently an IT Service Desk analyst. I have been coding for a couple years as a web dev on the side, but I am planning to get my masters in Business Analytics - which has a ton of data curriculum. I am considering getting a sub to Data Camp so I can focus on that content. I also like that they give you discounts on Microsoft Azure certs, which the start up business I work for values and pushes for us to get.
Thank you for the video. You are spot on, but it's not only DataCamp that has this problem. I'm trying to transition into Data Analytics, but in the future, I don't know if I'll bleed over into data science.
Well, if I want to learn systematically some data science or data engineering principles and some coding for that, then where else could I go? Seems to me datacamp has a good structure, but I am really not too far with it at this point, so I could use some advice about where else to learn well (with certifications and hands on examples and good explanations)
The projects guys. The projects address this problem of chunking a task. Otherwise good review. I am not new to coding so I am finding DC very effective to practise.
Thank you for such a great and informative review. I would love to hear your opinion on DataCamp’s competitor Dataquest. They attempt to address some of DataCamp’s problems.
Thanks for the valuable information. I’m deciding between datacamp and codeacademy. I’m 41 years old and want to become a data scientist, as I don’t have any portfolio I want to be able to do it while learning. I have the math foundation as I am an actuary, what is your recommendation?
how are you doing now? i come from an actuarial background but trying to transition into a data science role, so i'm happy to see someone on a similar path :D
I get you thanks for putting all these into a video I am seeing this now where I am at the stage just having complete my career track and making my portfolio I agree with you that datacamp is not nearly enough for a noobie I have so much trouble with my portfolio now 😅 there is no fill in the blanks for a portfolio project only googling and finding your way around in stack overflow and some RU-vid videos. I find myself having to supplement my learning through other various courses in udemy and Coursera so my entire journey of learning something is not 3 months as datacamp promised but 6-9 months in reality 😂 I went down a rabbit hole there is no way back and I am still hoping to land my first job as a data analyst associate one day
It's a "You" problem if those concepts didn't remain in your memory, and also that's why they have worspace with somehow a large number of datasets for you to practice the new knowledge you acquired.
Great explanation as always 👍 You've actually summarized a common problem of similar websites&apps. Most of the time, you feel like you're learning stuff but you actually don't, and you're still coming back because of the feeling gamification creates. We all wish for a simple and enjoyable way to learn stuff, but in reality putting the hard work is the only way to improve oneself. It also gives a satisfaction in the end though, so it is worth it I guess :)
I think data camp is for me, I have a strong understanding in python and sql, just need to data engineering on it, and practice practice, as a student its only £11 a month as well
This just made me subscribe to the channel.. Probably one of the most honest videos I’ve seen on RU-vid .. 🤘🏾 .. I’ve spoken with countless coding bootcamp recruiters … they all sound like they’re reading from a transcript 😩.. Been playing around with python since November of 2022 .. haven’t made any significant progress .. went down the rabbit hole of ML (deep learning) and became obsessed .. I have 61 tabs open on one screen and 23 on another all related to Python , Linear Algebra and Machine learning.. OBSESSED OBSESSED!! 😩.. but I’m stuck and I really enjoy exploring kaggle datasets .. I would love a road map, specifically for internalizing Python .. I really want to lock down the syntax and how to “think programmatically”.. as a solid foundation in Python, I’d flourish in the machine learning field.. I get the concepts (struggling with math) 😂.. I appreciate you sharing your journey.. very inspirational..
Really glad I found your video. I got the Datacamp ad and jumped on their website to see what's up and I thought it looks pretty decent, but from previous experience learning on Codecademy, I was hesitant if the teaching method will work for me. This video sort of confirms to me that it's not for me. I work in Digital Marketing and Digital Analytics and have increasingly gotten more interested in the data side (especially building stuff and fixing stuff, generally just solving problems around digital business. So, I thought I'd support my learning on the job with some education, but I think I will go the path of doing a Master's in Data Science since there they usually go through the theory pretty well too. I have a bachelor in business, so it's not data-related, but I think I will be fine. Oh, and I also bought a book called Learning to Code With Hockey which has Python and analytics in the subject of hockey (which I love), so might read and study that before the master's degree. Damn long text, sorry to anyone who goes through the trouble of reading it 😄
DataCamp does have fundamental and theory courses but you don’t have to review them to obtain the certification unfortunately. If you’re coming from a non-technical background and just think “All I need to do is learn how to code good” then yeah it’s a tough realization that fundamentals and theory is equally important.
Hey! thanks for sharing, Nash!, i do find your insights were insightful, however from all the cons you have mentioned it lead me to think, there must be sort of way to keep our knowledge relevant to the practical real-world problem, which you haven't mentioned or talk about it enough in this video (or you have in another video), I still somewhat blind to this field and just start learn it few months, taking my journey far away and decide to put an investment still a bit shaky to me, if a single learning sources wouldn't help me to get my way to a job, then how could i really do it? i need resource and advice on this, like how do i keep my knowledge applied and relevant to real-world cases, how to practice and where to, it's just so many gaps of question that makes me doubt to walk it through, Hope you catch on this comment, looking forward for your answer.
Thanks so much for your comment and I understand your concerns. I recently made this super in depth video it will explain everything you need in this regard - ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-6DxBaphvap4.html&lc=UgyFeXBqhtrx-5Iq4gZ4AaABAg
Great video! what do you think is the optimal way to learn ML and DS concepts before doing projects because you need that initial knowledge before you dive into kaggle and real world projects
I am a Java Developer in my day job, but the company I work at employs data scientists. I may need Data Science skills in the future. Is going the Data Camp route ideal for my situation? I dont have the money or the time to pursue a degree, at least not an in-person degree.
i'm a data scientist and learned from datacamp u can't just watch the videos and do the exercices and wish to remember all that shit u need to practise everyday and they got that also so please stop with the click bait people can se ur video without even watching it and decide to pass on a great plateform
I just started learning HTML and CSS. When I see the code, i can tell what it does. When i see a test question, I can easily know the answer. But when it asks me to write a webpage or a form from blank, I have no idea where to start. All of a sudden i just can't remember the syntax, elements, attributes and so on...😮💨
If you have a file template to work from, its easier than producing the code from scratch. You have "recognition memory", where you can say "I recognize this is an HTML file". You also have "total recall" memory, where you memorize the components of a file, and you can reproduce the file line by line without any hints. To achieve "total recall", you need to quiz yourself every day on specific parts of the file, until you have it down cold. If you do this for long enough, you will have the file type memorized, beyond just "I recognize this is an HTML file". If you want to be a proficient developer, you may only ever need "recognition memory", but you may be challenged down the road to produce it cold, and then you will be happy if you spent the time upfront to be prepared.
Your point on feeling good but learning less is very true. I am facing lot of challenge learning Python. Wish some one could guide me what approach I shall be taking
Personally I really like Datacamp, but it is true that is so easy so you are not going to learn anything in the easy way I decide to use it as a guide to learn the concept and always make the projects with unguided option, that's the only way I will be forced to research a little bit so I get filled my learning holes, also I don't really like that they teach you a lot of ways to basically do the same thing, I mean, I understand that they want you to know all the available tools but, how would I learn that one specific tool is better than another if I never felt difficult to use a first tool at the time? That makes a real flat learning sensation Sure I first did some other Python courses so I know the coding basics
I dont agree with this video , data science is great and explains what each role does, maybe it has changed recently since I can't say I have experienced the issues you mentioned
You also sound like the kind that will say you know exactly how much persons are alive right now in the world. Give giving yourself a bad rep... it will catch up with you.
You are 100% responsible for you learning success, stop finding excuses. You would have learned the concepts, if you apply it on a real project besides learning with DataCamp.
Relying solely on paid courses, whether from platforms like Datacamp or traditional universities, is a flawed mindset. I'm an MSc in Computer Science student with the goal of obtaining a professional certificate for job interviews. However, the true essence of learning emerges when you amalgamate diverse information sources, such as journal papers, RU-vid videos, online lectures, Udemy courses, and more. It's through assimilating techniques from these varied outlets that you can embark on authentic learning experiences, culminating in the creation of your own projects, such as interactive websites or mobile game apps.
