In the online learning world, we often see two types of students:

1. Those who are willing to invest in structured learning, like online courses, learning paths or instructor-led bootcamps

2. Those who rely entirely on free, self-guided resources like YouTube, Google, and Stack Overflow

For whatever reason, this tends to be quite a polarizing topic in the analytics & data science community. Those who take a purely self-guided approach tend to wear it like a badge of honor, as if paying for formal training is a sign of weakness or inferiority.

Why would I pay to learn when I can Google code or browse YouTube or Stack Overflow for free?

Fair question.

With such a ridiculous amount of free content at your fingertips, you probably could find an answer to just about any question that pops into your head.

But before I sink my teeth into this one, let’s get a few things out of the way:

• I teach online, professionally, and I charge for my courses. This makes me uniquely qualified to speak about this topic, and also uniquely biased towards structured learning.

• This isn’t an either/or scenario. It’s about recognizing the flaws of purely self-guided learning and appreciating the strengths of structured courses. A mix of the two is ideal, but ultimately there’s no “right” or “wrong” way to learn.

Now to get back to the question at hand, I’d like you to imagine that instead of learning analytics or data science, you’re learning how to speak a new language.

Let’s suppose that instead of learning MySQL, you’re learning Portuguese. And instead of building data skills because you need them for your job, you’re building language skills because you just moved to Lisbon and need to function as part of the local community.

As a self-guided learner, you pull out your iPhone, fire up Google Translate, and scan the scene around you.

There are trees to your left -- árvores! To your right, a woman (mulher) walks her brown dog (cachorro marrom). At this rate, you’ll blend in with the locals in a matter of days…right?

Of course not.

Whether you realize it or not, trying to build fundamental data skills by searching and copying code is like trying to learn a language by plugging words into an online translator. It just doesn't work.

Let's break down four of the biggest flaws of self-guided learning:

FLAW #1: YOU WON'T BECOME FLUENT

If you’ve taken a Maven Analytics course, you’ve likely heard us talk about “fluency”.

Fluency, in this context, is about building skills that are so strong, and so fundamentally sound, that you can deploy them almost subconsciously. To become “fluent” is to understand, at the deepest levels, how a tool or program thinks.

Just like a native speaker can instantly translate thoughts and ideas into sentences without pausing to process each word, a fluent analyst can translate complex business logic into formulas or code as fast as their fingers can type.

Without a structured learning framework, you will NEVER become fluent.

That’s why we spend as much time fine-tuning our course curriculums as we do actually building the content. It’s why we obsess over things like “sequencing”, “bridging”, and “flow”, and why we go to painstaking lengths to explain the WHY behind every topic we teach.

Learning without context -- without a larger framework -- is like collecting puzzle pieces without the box. Structured learning helps you understand not only the individual building blocks, but exactly how they fit together.

FLAW #2: YOU DON’T KNOW WHAT YOU DON’T KNOW

So by now you’ve picked up bits and pieces of the local language. You can order a ham sandwich, buy yourself a bus ticket, and politely ask for the nearest restroom. Get the accent just right, and you might even fool a few people into thinking you’re a local.

But here’s the catch: because YOU are the one driving your own learning experience, your entire potential scope of knowledge is limited to:

1. Topics you know

2. Topics you know you don’t know

Because you’re not an expert, and because you’re not following the guidance of an expert, you are completely blind to all the things you DON’T know you don’t know. And the less expertise you have, the bigger the blind spot.

To give you a real example, I spent several years as a self-proclaimed Excel expert before stumbling across a function called INDIRECT. To my fellow spreadsheet jockeys, this function can interpret a text string (like “A1:D10”) as a cell reference, and use it as an argument within complex and dynamic formulas.

That little function became an absolutely lifesaver. It made my work more efficient, unlocked functionality that had stumped me for years, and quickly became one of my go-to tools.

The thing is, I never knew to search for that function, because I had no idea it even existed.

I was lucky enough to stumble across that one on my own, but this is exactly what makes guided learning so valuable. By trusting the guidance of an expert instructor, you are leveraging a depth of knowledge that likely took years -- if not decades -- to develop.

FLAW #3: YOU’LL DEVELOP BAD HABITS

As the saying goes, “if all you have is a hammer, everything looks like a nail”.

In other words, it’s far too easy to rely on familiar tools. Everyone is guilty of this to some extent (myself included), but self-guided learning tends to amplify the problem.

Just like a tourist might repeat the same handful of words and phrases in an awkward, brute force attempt at communication, young analysts and data scientists have a habit of applying the same trusted tools or algorithms to any case that feels remotely relevant.

The beauty of structured learning is that you don’t just learn the tools you need, but also the ones that you don’t need YET.

This means that you’ll be armed with a deep and versatile set of skills, and ready to deploy them when faced with new or unfamiliar challenges. This ability to adapt and evolve is critical, especially in a field that changes as quickly as data and analytics.

FLAW #4: YOU AREN’T REALLY LEARNING

While there’s absolutely nothing wrong with using self-guided resources from time to time, the act of borrowing and regurgitating code should not be confused with actually ""learning"".

When you rely heavily on Google or YouTube to find quick solutions to specific needs, you are training your brain to be lazy.

Learning, like most worthwhile endeavors, is hard work. It’s painful, it’s tedious, and it’s a downright struggle.

But that struggle is where the magic happens. When you force yourself to think critically, wrestle with difficult topics, and allow yourself to make mistakes and learn from them, you actively promote brain growth and strengthen neural networks.

In other words, you LEARN.

Not only does the “instant gratification” approach limit your actual learning potential, but it can lead to a nasty combination of overconfidence and under-competence; a cognitive bias known as the Dunning-Kruger effect:

Not sure if this applies to you? Here’s a test:

Imagine that you are interviewing for a BI/Analyst/DS role and -- surprise! -- you’ve been asked to walk through the details of a relatively complex project that you built a few months back.

Would you be able to:

1. Summarize the business case and break down your approach, step-by-step?

2. Describe the specific purpose that each line of code serves, in plain language?

3. Discuss the pros and cons of your approach, compared to similar alternatives?

If you can answer all 3 of those questions, it’s a great sign that you’re building (and retaining) a deep and well-rounded skill set -- keep it up!

If you can only answer 1-2 of those questions, I’d urge you to consider how structured learning might help you bridge the gap.

If your only response is something along the lines of “I found code on Stack Overflow and it just works”, you might want to reevaluate your career choices.

FINAL THOUGHTS

To wrap up, let me reiterate that I am NOT opposed to self-guided learning in general. In fact, I use free resources like Google and Stack all the time to explore specific use cases, check formula syntax, diagnose errors, and discover new tools and techniques.

In my experience, self-guided learning resources are great for:

• Finding specific answers to specific questions

• Debugging or spot-checking errors

• Soliciting feedback from experts

• Exploring unique or creative solutions

• Connecting with other users

• Filling knowledge gaps

• Supplementing a structured program

That said, they absolutely should NOT be considered a replacement for a structured training program, and are woefully inadequate when it comes to building the comprehensive, foundational skills that will help you become a successful analyst or data scientist.

Agree? Disagree? Connect with us on LinkedIn or reach out to share your thoughts!