Breaking Free from Tutorial Hell: Tips for Aspiring Data Professionals

Breaking Free from Tutorial Hell: Tips for Aspiring Data Professionals

Starting your journey in Analytics can feel like standing at the base of a big mountain. I’ve seen it around me, and I’ve experienced it myself: there’s an overwhelming amount of information and an endless list of courses and tutorials you can take.

As a Finance undergraduate transitioning into Analytics, I was confused about where to start learning. I signed up for every platform I could find – Coursera, Team Treehouse, Pluralsight, Linux Academy, Cloud Guru, you name it. At first, I learned a lot of things, picked up the basics on Python, brushed up my SQL, learned about what git was. But then, as I was going through them, it felt increasingly like the goal was collecting the certificates at the end, not the journey itself.

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Don't Fall into the Trap

It's easy to fall into the trap of this "tutorial hell" – a cycle where you keep consuming courses without ever applying what you've learned. Sure, I’d want a course to learn the basics of Python or how a Data Engineering stack on GCP looks like, but don’t overdo it. Practice > theory.

Let’s see on how to avoid this common pitfall and make the most of your learning journey.

Start with the Basics

Surely being good with Spark or ${YOUR_COOL_NEW_TOOL} is good and commands a bigger salary, but you still need to have your basics right first. A solid foundation in SQL, a decent grasp of Python, a basic understanding of data modeling, working knowledge of git, and minimal familiarity with products in one big cloud Analytics stack - these could be a good foundation when starting your career.

Choose Wisely

Your time and money are limited resources, so start by picking a good course for your topic. Index the information in your mind: understand the features of the tools or frameworks and use cases where they are suitable. Focus on grasping the bigger picture rather than memorizing specifics. You can always look up those later. Nobody wants you to know the exact order of arguments for a specific function; you just need to remember it exists and it can help in X or Y situation.

Get Hands-On Quickly

One of your primary goals should be to learn the basics and then start applying them as soon as possible. There will always be gaps in your knowledge, but it's better to dive in and start doing rather than endlessly chasing another tutorial. Applying your skills quickly helps cement your understanding and builds your confidence.

Course Certificates Don’t Do Much

If you’re looking for your first job or internship, having a lot of certificates from courses might be a small advantage to your name, signaling your motivation to learn, your perseverance, and consistency. But nowadays I’m not even sure that it would matter in how modern CV screening works and with this strong competition.

Work on Personal Projects in the Long Term

Make it a habit to engage in personal projects or guided projects that simulate real-life scenarios.

These are good:

  • For your understanding - you encounter problems that tutorials don’t cover and fix them

  • For your confidence - you manage to make things work on your own, so you’ll be just fine in a job setting

  • For your employability - it’s not only about the keyword in your resume, it’s also about being a problem solver and finding solutions to problems, business or technical

I’ve previously posted about how personal projects can help you grow as a developer

Here’s two such projects of my own:

It’s time to build

The key takeaway here is to get building. Start creating, experimenting, applying, and sharing what you learn. Whether it's an individual project stemming from your own idea or a guided project, the experience you gain from practical application is invaluable.

Remember, the goal is to become proficient solve real-life problems and be confident in your skills, not just to collect certificates. So, get out there and start building your path to success in Analytics! Don't give in to tutorial hell.

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