My 30 Days of SQL: What I’m Actually Doing and How You Can Follow Along (or Make It Your Own)
So I’m sitting here, going along talking about myself doing a 30 days of SQL challenge like it’s something someone on TikTok has done and it’s just a regular challenge. When in reality I made it up… And not a single soul knows what I’m talking about. So I decided why not wrap it up in what I’m currently learning about in case someone else wants to do it to?
SQL is actually enjoyable.
Time flies when you’re having fun.
🗓️ Weekly SQL Structure
Because I’m still working at the w2 (soon enough I will be free…FREE) I am letting myself relax a bit and learning at a more relaxed pace. I already know some SQL as I use it at work, but I wanted to really get to the intermediate stages.
Core Focus Days: Monday & Wednesday (60-minute lessons)
Optional Review Days: Tuesday or Friday (flashcards or short review)
Rest Days: Thursday & Sunday (no guilt, no pressure)
Each week I rotate topics:
Week 1: SQL Foundations
SELECT
WHERE
GROUP BY
Basic JOINs
Week 2: Intermediate Logic
JOIN variations
Subqueries
CASE statements
Bonus? I allow myself to skip one full day guilt-free each week — because life is real and learning needs time to soak into the brain.
🧪 The Project: Guided, Free, and Actually Fun
I took inspiration from Maven’s “Guided Projects” format — basically applying SQL to a real-world dataset with a purpose. Here’s how you can do the same:
Step 1: Choose a dataset
Go to a site like Kaggle. Pick something that excites you. I chose a Cybersecurity dataset because I’m a nerdy kitty at heart and love a good digital mystery. But if you’re new to data analytics, think to yourself: what industry would I like to work in? Where do I see myself in the future? Then go find yourself some data that fits.
Step 2: Think industry-specific
If you were a professional in that industry, what kinds of questions would your stakeholders be asking? That’s your anchor. Ask yourself: who even are my stakeholders? Because that is going to determine the kinds of questions you get. Always attune the answer to the person who is asking the question. Such as, is it a manager asking me? A director? A CEO? Each one will need to know different things about the business itself.
Step 3: Practice API basics
Some datasets have APIs. Learn how to grab the key and use it (even if you stick to CSVs now). Trust me — API skills are automation magic in disguise. It’s the key to getting sucked into the automation rabbit hole. At least it was mine.
Step 4: Import the data
Use Excel, Power BI, or your favorite tool. Doesn’t have to be fancy. The goal is familiarity and play.
Step 5: Simulate a real job
Ask your AI buddy (hi, ChatGPT 👋) something like:
“Pretend you’re the Chief Cybersecurity Officer. What would you ask your data analyst to find in this dataset?”
Instant project requirements. Good practice for beginning to think in terms of not just cleaning data for the sake of it, but for answering business questions.
Step 6: Query, analyze, refine
Practice the basics:
SELECT
WHERE
GROUP BY
HAVING
Maybe even some JOINS if you’re feeling spicy
Try cleaning the data a little. Sort and group things. See what insights you can surface.
Step 7: Get feedback
Share your queries and insights with an AI or a friend. Ask for feedback, not just answers. That’s how you grow. Be specific. Share how your thought process worked when you found the answer. This way it trains not just what you’re pulling, but how you think.
Step 8: Package it
Summarize the project. Screenshot the dashboard or visual. Write a short reflection. That’s it! You now have a portfolio piece. Doesn’t have to even be pretty. Although it can be. The point is that you are learning and showing off the fact that you want to learn and you can do the work. That’s the key.
Where Am I?
Well I just finished up learning about GROUP BY clauses, now I’m moving towards Aggregate functions and HAVING. All of these make sense, and it’s quite fun to do the actual writing of the SQL instead of just watching a video where someone else does it. Solidifying my learning through doing has increased my ability to learn all of this quite a lot, and I feel it sticking. Despite the fact that we use incredibly old legacy SQL code from the early 2000s at work (I’m not kidding) I can read it better now and understand why it does what it does, making me able to actually predict at least a little bit better, if my queries are going to actually work without me screaming at the monitor.
Final Thoughts
This isn’t about being perfect or sprinting through. This is about showing up, learning one concept at a time, and building confidence with real data. And believe me, it feels good to say, “I made this.”
You can absolutely do it too.
If you want to join in, just let me know. I’ll be cheering for you!
PS: ☕ If you liked this and want to support the journey, you can buy me a coffee — every little boost helps keep the pawprints moving forward.
PPS: I wrote a book and made a template!
Check out my new book: The Ghosts Who Say No: How to Say Yes to Yourself, and explore the Data Analytics Learning Lite Notion template. Built for anyone who just needs a little organization and a gentle push in the right direction. Get them here!
