Extract. Transform. Read.

A newsletter from Pipeline​

Hi past, present or future data professional!

One of the most loaded terms, after AI, is upskilling. It’s something everyone should always be doing, yet, only the most dedicated can consistently dedicate time to learning and expanding beyond their comfort zones.

If you’re on the path to becoming a data professional, you’ve probably spent countless hours learning, only to find yourself wondering if you’re actually making progress. I’ve been there.

The number of hours I spent learning data science is only slightly more than the amount of time I've considered quitting data science (obviously data engineering is a different story!).

Learning, especially technical skills, is a skill in itself that takes years to master. Along the way, it’s all too easy to fall into traps. After looking back at my own journey, I’ve identified some common upskilling traps I fell into face first and that I hope you can avoid.

Trap 1: Tutorial Hell

Like an endless loop in a Python script, you find yourself consuming an endless stream of tutorials on the same subjects, never truly finishing one. You learn about loops and functions ten different times, but you never feel like you know anything for sure. This happens because platforms like YouTube are designed to keep you watching, often feeding you redundant content.

Escaping Tutorial Hell

• Create a dedicated folder in your IDE for code snippets and a playlist for videos.
• “Time box” yourself by imposing a strict time limit to find the answer you need.
• Close the tabs with redundant information and, most importantly, stop a video to take notes. This forces you to digest the information instead of passively ingesting it.

Trap 2: Color-By-Numbers Data Science

This is when you mindlessly follow a walkthrough, filling in the blanks without truly understanding the underlying logic. You’re just coloring inside the lines someone else has already drawn.

Escaping Color-By-Numbers Data Science

• Use a different methodology to solve the same problem.
• Change input parameters.
• Copy the code into my own notebook and write comments to ensure I understood every line before executing it.

Trap 3: Aimless Projects

On the surface, it seems productive. But if it lacks a clear purpose or business value, it won’t impress a potential employer. Employers can spot a project created just to fill a portfolio a mile away, especially if it uses a tired dataset like the Titanic.

Escaping Aimless Projects

• My escape from this was to find datasets that genuinely interested me. I once created a dashboard using data scraped from The Onion. It was fun, original, and showed that I could apply my skills to something unconventional.

Finally, there's…

Trap 4: Learning the Tech, Not the Skill.

I used to think of my work in terms of “Python tasks” or “SQL tasks.” But programming languages are just tools. The real skill is knowing how to apply those tools to solve a business problem. I wasted hours reading the entire BigQuery SQL documentation only to find that I would only use a fraction of it.

Escaping Learning the Tech, Not the Skill

• Find a business problem and then learn just enough of the technology to solve it
• Focus on the demonstration of domain knowledge and business application over tech stack. It may feel counterintuitive to spend less time coding when trying to improve yourself professionally, but sometimes that’s the answer.

When it comes to upskilling, remember this: the goal is not to learn everything.

The goal is to learn enough to be proficient and confident.

Your learning journey doesn't end when you get a job; that's when the real learning begins.

For more details and the last upskilling trap, read the original Pipeline article.

Thanks for ingesting,

-Zach Quinn