How I Completed My First SQL Project (And What I Learned)

I finally got to do my first (of many) SQL project. I was sitting on the idea of going into data analytics for quite a while. A good friend of mine suggested that it would be a suitable addition to my skills following my background in finance. He pointed me in the right direction, I did some own research to find out the learning path to data analytics/engineering and now here we are, work in progress. SQL armor into my arsenal!

When I first started learning SQL, I had no idea how much power those simple queries could hold. But after completing my first real-world project, I now see why SQL is the backbone of data analysis. Now, without further ado...

🚀 The Project
I set out to analyze job postings for Data Analyst roles, asking questions like:

What are the highest-paying jobs?
What skills do these jobs require?
Which skills are the most in-demand and lucrative?
The goal was to not just learn SQL but also uncover trends in the job market that could help me (and others) prioritize learning specific skills. The data is from a year ago (2023) but the insights from running the queries remain invaluable!

The Tools I Used
To get started, I worked with:

1. SQLite for "lightweight" querying. SQLite was the main editor while I learnt the basics. I loved the convenience of accessing it from my browser.
2. PostgreSQL for database management. I was able to create an IDE and a create a local database.
3. SQL for querying job and skill data.
4. VS Code for editing my SQL queries. I was fascinated that it allows to write and edit code in any programming language and its flexibility with integration to other resources such as PostgreSQL and GitHub.
5. GitHub to organize and share my scripts. The database itself had four tables: job postings, company data, skills required, and skill details. Pretty standard stuff, but enough to dig into some interesting insights.

What I Did
Here’s how it went:

First, The Basics
I started with simple queries like filtering (WHERE), sorting (ORDER BY), and joining tables (INNER JOIN). Writing those first few lines of SQL felt empowering—I was finally communicating with data.

Going Advanced
Next, I leveled up with Common Table Expressions (CTEs), CASE statements, and even some date functions. It was challenging, but these tools turned complex queries into manageable steps.

The Fun Part: The Project
Finally, I applied everything I’d learned to answer real questions:

Top-Paying Jobs: Remote Data Analyst roles at companies like Meta and AT&T offered salaries up to $650,000.
In-Demand Skills: SQL ruled the list, appearing in 90,000+ job postings. Python and Tableau weren’t far behind.
Optimal Skills to Learn: Balancing demand and salary data, I found SQL, Python, and Tableau to be must-haves for career growth.

My Key Takeaways
SQL is Your Superpower
SQL transforms data into insights—quickly and effectively. Knowing it opens doors to solving real problems.

Skills Matter
Some skills, like SQL and Python, are timeless. But staying updated with newer tools like cloud computing, Snowflake or Go can unlock greater possibilities in the data career.

Start Small, Think Big
This project began with simple queries, but as I connected the dots, it evolved into a powerful analysis of skills and salaries.

Why This Matters
Learning SQL wasn’t just about gaining a technical skill, it was about empowering myself to make data-driven decisions. Now, I can identify trends, spot opportunities, and prioritize skills that matter most in today’s job market.

If you’re new to SQL, I’d highly recommend starting with a project like this. It’s not just a learning experience, it’s a confidence booster.

You can find this project here: https://github.com/commacap/SQL-Project.git

Got thoughts on SQL or your own beginner projects to share? Let me know in the comments—I’d love to hear your story!