How I Revisited Data Analytics (Google Sheets, SQL, Python) to Build My Own App

I didn’t start completely from scratch.

I have a background in programming—working with languages like COBOL and C++—but like many people, I spent years away from actively building systems.

This time, I’m approaching data differently: not just as code, but as something to structure, understand, and apply in real life.

Why I Started -The Research

The Belle app could have been outsourced.
Instead, I chose to build it myself.

Throughout my career, I’ve consistently been the person teams turn to when systems need to make sense—whether that means fixing workflows, translating business needs into technical solutions, or building processes from the ground up.

At Arch Insurance, I designed and launched their website, developed an internal referral system that scaled from national to global use, and restructured their internal platform to improve adoption.

In a hospital system, I worked directly with IT teams and C-level leadership to build systems supporting their CHIP program and broader operational workflows.

Now, with Belle, that same approach continues—where every system, process, and structure is designed, built, and refined end-to-end.

This time, I decided to go deeper and build the system from the ground up.

Where I Started

To rebuild my foundation, I started with Khan Academy.

Not because I’m new to programming—but because strong fundamentals matter.

It gave me a clean, structured way to reconnect with logic, syntax, and problem-solving in a modern context.

What I Revisited

Google Sheets (Data & Automation)

Google Sheets became the first practical layer.

• QUERY function (SQL-like logic)

• Named ranges

• Dropdown systems

• Apps Script

This is where data becomes usable—fast, flexible, and directly tied to real workflows.

SQL (Foundations)

SQL reinforces structure.

• SELECT

• WHERE

• ORDER BY

It’s straightforward, but it changes how you think about organizing and retrieving data.

Python (Logic & Automation)

Python connects everything! <- notice exclamation point

• Strings

• Lists

• Loops

This is where logic scales—and systems start to automate.

Why I Document Everything

I document everything as I learn.

Not as theory—but as applied understanding.

Because:

• It sharpens how I think

• It creates clarity in complex systems

• It becomes something others can actually follow

Where to Access My Notes

I’ve organized everything into one place:

👉 https://belle.com.mx/data-analytics-notes

This page includes:

• Structured notes

• Real workflows

• Tools I actively use

Final Thought

You don’t need to start over to go deeper.

You just need to reconnect, rebuild, and apply what you know—with purpose.

This isn’t about learning code.
It’s about building systems that actually work.