YouTube Is Not a Place to Learn Data Science.

 If you say YouTube is your last resort, I have no objection. 

But being free and super popular doesn’t make YouTube the best learning platform. Having tons of tutorial videos doesn’t either. 

YouTube is a social media built for videos. Over time it has proven valuable in many ways—people from different domains post their helpful videos on this platform, and many benefits from them. 

Of course, It’s a great place to find quick tips and fixes. But it doesn’t support progressive learning. , YouTube is not built for it. 

Related: How to Become a Terrific Data Scientist (+Engineer) Without Coding

There’s no doubt that Youtube is an excellent resource for learning. You can find videos on just about any topic, many of which are well-made. However, Youtube has some definite drawbacks when learning data science online.

This post is not intended to discourage using Youtube altogether but to use it correctly. I find lots of helpful videos on Youtube. But what I look for on Youtube is different.

I don’t look to Youtube to learn something from scratch. Not data science. If I were learning data science for the first time, I’d go with a Coursera specialization or a Udacity nano degree (No affiliate interest here.)

Courses from these reputed education providers offer well-curated learning paths. They don’t have the distraction of video recommendations and ads like Youtube does. You don’t even have to wonder what to learn next and what after.

Importantly, they are provided by industry experts from reputed firms like Google and Nvidia. They have a great learning community where peers help each other.

Fair enough, these resources aren’t free, whereas Youtube is. But given your circumstances, these fantastic EdTech companies may offer generous financial aid.

Here are my top reasons why I don’t recommend YouTube for someone who begins their data science career.

#1 Reason: YouTube is full of distractions

Is there a better place to learn data science than on YouTube?

After all, what could be more distracting than a never-ending stream of videos featuring talking heads, music, and ads? And if you’re lucky, you might even find some shorts to spend your entire day on something that will never lift your career. 

They are just addictive. 

But seriously, if you want to learn data science, YouTube is not the place to do it. Plenty of other online resources can provide you with the information and resources you need to get started in data science.

So take a few minutes to explore those options, and then return to YouTube when you’re ready to be distracted. 

Just get addicted to a platform that’s committed to your growth. 

Learning on Youtube is unidirectional and unchallenging.

If you’ve used other learning platforms, you’d have noticed they have quizzes and assignments in the middle. Youtube has nothing like that; you’re just receiving and receiving information. 

In other ways learning on Youtube is unidirectional. 

Why it’s important?

Our brains are goal oriented. We’re not motivated to advance in anything we do without little challenges.

I’ve started to learn so many things and stopped after a week or two. But I later discovered the lack of motivation because of a lack of challenges. That’s regardless of where I’m learning it. 

Of course, some Youtube videos ask you to do some exercises. They are the good ones. But still, the nature of the platform doesn’t challenge you enough to spend more time in it. Nothing stops you from skipping the exercise and moving further.

Youtube has a lack of peer learning opportunities.

You can’t ask questions or get help from other learners in the comments section. This can be very frustrating when you’re stuck on a problem and need guidance.

Additionally, the comments section can be pretty distracting, full of people arguing with each other or promoting their thoughts. Finding a more focused resource might be better if you’re trying to learn data science online.

Yet, in a good learning platform, peers are committed to improving each others’ understanding of the topic. Often these are some real problems learners face and not made-up solutions to look nice in the comments section.

Though we can not warrant that the communities from the other learning platforms are entirely out of rubbish talks, interaction is primarily professional. Conversations on youtube are usually not.

YouTube content isn’t curated.

There are a lot of videos on YouTube that claim to be about data science. However, most of them are not very well-produced or organized.

The Youtube algorithms do an excellent job of showing quality content first. But still, there are so many ways YouTubers hack in and promote their mediocre videos. 

Haven’t we seen clickbaity titles with poor content on Youtube before? That’s what I’m talking about. 

Yet, data science is a complex subject, and it cannot be easy to find videos that explain things clearly. In addition, many data science concepts are best learned through practice, so watching a video can only take you so far.

For these reasons, I often turn to other resources when I want to learn about data science. 

Of course, you can also find some good data science videos on YouTube, but you’ll need to dig to find them. And you have to do it before you die of old age.

Youtube has content created by Experts (A small percentage)

Data science is a complex field that requires years of study and experience to master.

Yet, in recent years, it has become one of the most popular topics on YouTube. A quick search for “data science” yields a never-ending stream of results, with new videos uploaded every hour. Most of these videos are created by people without formal training in data science.

I don’t discourage people from doing it. It’s a great way to share your learnings. And mistakes are okay too. But if YouTubers have a Socratic mindset and not portraying themself as experts only after a year of experience, that would be great.

Yet YouTube often contains inaccurate or outdated information. What’s more, they rarely offer any insights that Data scientists who have spent years studying the topic would be able to provide. In other words, the vast majority of data science videos on YouTube are created by amateurs.

Thus, viewers should be skeptical of the information presented in these videos and always consult with experts before taking action. 

In a previous post, I picked a few Youtube Channels as the best free resources to learn Python. You can see the quality of these videos against most other Youtube content. But such channels and videos are rare, especially for data science. 

Related: How to Learn and Improve Your Python Coding Skills With Free Resources?

What is YouTube good for?

Youtube isn’t a wrong place altogether. You can find fantastic resources there.

But Youtube isn’t suitable as a primary learning platform.

YouTube is an excellent resource for data science tutorials on specific niche topics. For instance, I wouldn’t recommend learning data engineering from scratch with Youtube. But you could find how-to guides on Google to connect Salesforce with Redshift.

You can find expert interviews on Youtube. Most course providers also offer these, but there are plenty on Youtube. You could also regularly subscribe to some podcasts introducing new and exciting data science tools and concepts.

Also, youtube is free and available worldwide.

Simply speaking, Youtube is not an excellent platform for progressive learning, but it may be suitable for finding quick solutions to specific problems. Think of it as a video version of StackOverflow, not a substitute for a data science module. 

Final thoughts

Youtube has been a valuable resource for lean just anything. 

But I don’t use it as a tool to learn data science; not anything I’m serious about. 

Youtube is primarily a social media platform. Like any other social media, it’s built for distraction (personal opinion.) It lacks vital features like peer learning opportunities or Q&As and challenges. Leaning is relatively passive on Youtube. 

But Youtube is a great place to find novel ideas and expert opinions. Use it wisely for an exceptional data science career. 

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