If you’re interested in analytics, detail-driven decision-making, or simply enjoy thoughtful notes from a Virgo mind, I invite you to follow along.
Welcome to Mercury Musings.
“In the Realm of Data, we tame dragons — not dashboards.”
So… I swear I started this blog to share my data projects and build my portfolio. Somewhere along the way, I accidentally created an entire Targaryen-inspired data meme series instead. 😅
Because honestly? Data professionals are a little like Targaryens.
We’re passionate, fiery, and sometimes one bad join away from chaos. Our tools are our dragons — powerful, unpredictable, and capable of absolute brilliance or disaster, depending on how we use them.
So in true Mercury Musings fashion, meet the House of Data…
⚔️ House of Data: The Targaryens of Analytics
🩸 Rhaenyra Targaryen — The Data Analyst Turns chaos into clarity, one query at a time.
🐍 Daemon Targaryen — The Data Engineer Builds the pipelines that keep the realm alive.
☀️ Aegon II Targaryen — The KPI Executive Rules by vanity metrics and victory charts.
🧠 Aemond Targaryen — The AI Strategist Sees every move before it happens — and still plays to win.
🔮 Helena Targaryen — The Predictive Analyst Sees patterns others can’t — the data whisperer of the realm.
👑 Viserys Targaryen — The Data Architect Builds empires of tables and schemas that outlive kings.
💜 Rhaenys Targaryen — The Data Governance Lead The queen who never was — but still led.
⚔️ Baela Targaryen — The Data Operations Manager Keeps the data realm running — one project at a time.
If you were in the House of Data, which one would you be — the analyst, the engineer, the strategist, or the dragon?
Until next time,
Ardonna •ᴗ•
author’s note
Hi, I’m Ardonna Cardines — a data analyst and creator of Mercury Musings, where business meets imagination through data. I love blending analytics, design, and storytelling to make learning data modeling and visualization approachable — one creative dataset at a time.
If you’ve enjoyed this post, I’d love for you to follow along. You can subscribe to my blog for new tips + tutorials, creative datasets, and behind-the-scenes projects — or connect with me on LinkedIn where I share updates and learning resources for analysts and data storytellers.
There’s something oddly humbling about standing in front of a packed closet and realizing you still have “nothing to wear.”
As I stared at the rows of shirts, dresses, and jeans (some I hadn’t worn in years) I couldn’t help but draw data parallels to my closet… which was its own dataset. Messy, redundant, and full of null values.
So, I decided to treat it like a data problem.
If I can clean, model, and transform millions of rows of data — surely I could handle a hundred hangers.
STEP 1: EXTRACT – PULL OUT THE PIECES THAT MATTER
In data, extraction means pulling from a messy source and capturing what’s worth analyzing.
In closets, it means facing the pile and being brutally honest with yourself… and your consumption problems 🥲
I pulled everything out: clothes, shoes, purses, even those “just in case” items that hadn’t seen daylight in years.
This was my data extraction phase, full transparency.
As I started sorting, I noticed a pattern.
The pieces that truly stayed with me weren’t the trend-driven ones I’d bought on impulse — they were the classics. The high-quality basics I’d invested in over the years: the crisp white button-up shirt, the houndstooth work pants , the heavy grey cardigan. Many of my favorite pieces from Everlane and Zara.
It reminded me of the difference between fast data and clean data.
Fast fashion can be tempting, just like downloading flashy datasets for quick results but in the long run, it’s always the timeless pieces that hold their value.
ELT QUERY
SELECT * FROM closet WHERE joy = TRUE AND quality = ‘timeless’;
Step 2: Load — Stage Your Data (and Your Rack)
Once I’d extracted my favorite pieces, I loaded them onto a single clothing rack — my staging table. I went on Amazon the day before and bought one for $30 and telling myself: everything you choose has to fit on here. Instead of putting everything back in my closet, I needed to force myself to part ways with pieces I haven’t touched in so long and free up space that I could use for other storage like my yarn collection – ha.
Just like loading data into a staging environment, this step helped me visualize patterns and relationships.
I began noticing color palettes (my “columns”), favorite fits (my “key values”), and duplicates (“Do I really need three beige bottoms?”).
The rack became my mini data warehouse.
Step 3: Transform — Preparing for the Next Phase
In data, transformation is where separate tables come together through joins to create new meaning.
In fashion, the same principle applies. My base outfit, a classic striped button-up and wide-leg denim is like my primary table.
From there, I layered on new pieces: a denim vest, a paisley patterned jacket, a gingham coat. Styling instead of just “wearing”. Each addition felt like a join, combining two clean, distinct datasets to create a new, more insightful result.
🧮 A left join: keeping the core outfit, adding a jacket that changes the tone.
✳️ An inner join: when the base and layer perfectly align, polished yet effortless.
🧤 A cross join: when pattern meets pattern, bold, unexpected, but still connected.
Every transformation kept the base intact — proof that when your foundation is strong, creativity has infinite combinations.
Step 4: Optimize — Maintain and Measure
A capsule wardrobe isn’t just a one-time cleanup. It’s database maintenance.
I’ve learned it’s important to:
• Revisit each season (scheduled refresh).
• Add only what complements what I already own (controlled data inputs).
• Retire pieces that no longer align with my lifestyle (data depreciation).
And just like a well-designed data model, it’s made my life more efficient.
Capsule Wardrobe: The Loaded Dataset
(Every ETL project needs a final dataset — this one just happens to hang on a clothing rack.)
After the extract phase, here’s what officially made it into my Fall/Winter 2025 Capsule Wardrobe — the timeless pieces I’ve collected, loved, and worn through the years. Each one feels intentional, classic, and true to my style.
The Final Pieces
Tops & Layers
6 button-ups
6 tops
2 polos
3 sweaters
1 cardigan
2 jackets
The “primary keys” — the foundation of every future outfit join.
Bottoms
4 skirts
4 pants
4 pairs of denim
Most are neutral, but the occasional statement color (hello, red skirt) keeps things interesting.
Dresses
3 classic dresses
2 tunic shirt dresses
Functionality > Flash: These are versatile, comfortable, and can easily move from casual to polished.
Footwear
2 pairs of boots
3 pairs of clogs (yes… I love clogs)
1 pair of flats
1 pair of sneakers
Balanced Load: Equal parts practicality and personality — because good footwear is basically good indexing.
Color Story: Classic Meets Creative
As I stepped back and looked at my final capsule, the color story felt like a reflection of me — grounded yet expressive. The foundation is built on soft neutrals: beige, cream, tan, and black — the kind of timeless tones that quietly do the heavy lifting, much like clean, reliable data. But then there are the pops of red and green, my visual outliers that make the dataset interesting. They’re bold, unapologetic, and full of life.
The mix of patterns — from paisley to gingham to classic stripes and leopard — adds just the right level of texture and personality. Together, it’s the perfect balance between classic and fun, a wardrobe that feels both analytical and artistic.
wrapping up
What surprised me most wasn’t how many clothes I had but how freeing it felt to simplify.
Decluttering my closet mirrored the process of decluttering my life, my workspace, and even my creative energy.
When we clear out what’s no longer serving us, whether it’s old data, cluttered dashboards, or unworn clothes, we make room for clarity.
For intention.
For transformation.
Because sometimes, the best insights don’t come from adding more, they come from refining what’s already there.
And yet, there’s one category I refuse to normalize or declutter, my handbags and purses.
They’re my beautiful exceptions to the rule and my little “data anomalies.” Each one carries a story, a moment, or a milestone.
If the rest of my closet is a clean, optimized dataset, my handbag collection is the carefully preserved archive, the one I’ll never delete.
But let’s be real… I’m probably gonna fail hard at this capsule thing because it’s too hard when you love pretty things 😭
Until next time,
Ardonna •ᴗ•
author’s note
Hi, I’m Ardonna Cardines — a data analyst and creator of Mercury Musings, where business meets imagination through data. I love blending analytics, design, and storytelling to make learning data modeling and visualization approachable — one creative dataset at a time.
If you’ve enjoyed this post, I’d love for you to follow along. You can subscribe to my blog for new tips + tutorials, creative datasets, and behind-the-scenes projects — or connect with me on LinkedIn where I share updates and learning resources for analysts and data storytellers.
If you’ve ever worked with relational databases, you know that most data doesn’t live in a single table. That’s where JOINs come in. They let you connect data across multiple tables to create a complete story.
During my undergrad, I remember learning the different joins in one of my classes and I was just like:
But in this post, I’m going to break down the four most common JOIN types in a fun and easy way. If you’re an analyst and job skills mention SQL, these are essential to your success! But before I do, I want to cover Table Aliases.
table aliases
When you’re working with joins, table names can get long and repetitive. That’s where aliases come in.
WHAT’S AN ALIAS?
An alias is just a nickname for a table that makes your query shorter and easier to read.
Aliases become a game-changer and you can instead write:
SELECTc.customer_id, c.first_name, o.order_id FROM customers AS c JOIN orders AS o ONc.customer_id = o.customer_id;
Here, I’ve given customers the aliascand orders the alias o.
c stands for Customers
o stands for Orders
This way, the query is cleaner and it’s easier to see which table each column comes from.
✨TIPS
CONSISTENCY IS KEY: Always use the same alias convention so others can follow your queries.
KEEP IT INTUITIVE: Use short, obvious letters like c for customers, o for orders, p for products.
IMPROVES READABILITY: Especially helpful when you join 3+ tables.
Now, let’s get into the meat of this post!
1. inner join
the matchmaker.
USE CASE: Return only the rows that exist in BOTH tables.
CODE SNIPPET:
SELECT c.customer_id, c.first_name, o.order_id, o.order_date FROM customers AS c INNER JOIN orders AS o ON c.customer_id = o.customer_id;
RESULTS: Only the customers who have placed orders. Anyone without an order gets excluded.
2. left join
EVERYTHING FROM THE LEFT
USE CASE: Keep all rows from the left table, and match data from the right if it exists.
CODE SNIPPET:
SELECT c.customer_id, c.first_name, o.order_id FROM customers AS c LEFT JOIN orders AS o ON c.customer_id = o.customer_id;
RESULTS: A list of every customer — even those who haven’t placed an order yet. Missing values from the right table will show as NULL.
3. right join
EVERYTHING FROM THE RIGHT
USE CASE: The mirror of LEFT JOIN. Keep all rows from the right table, and match data from the left if possible.
CODE SNIPPET:
SELECT c.customer_id, c.first_name, o.order_id FROM customers AS c RIGHT JOIN orders AS o ON c.customer_id = o.customer_id;
RESULTS: You’ll see every order in the system — even if the customer record is missing (maybe due to data entry issues).
4. full outer join
THE UNION OF BOTH
USE CASE: Return all rows from both tables, with matches.
CODE SNIPPET:
SELECT c.customer_id, c.first_name, o.order_id FROM customers AS c FULL OUTER JOIN orders AS o ON c.customer_id = o.customer_id;
RESULTS: This shows every customer and every order, whether or not they match. It’s the widest view — useful for finding gaps in data where possible.
Visual Summary
INNER JOIN: Only matches
LEFT JOIN: Everything from the left + matches
RIGHT JOIN: Everything from the right + matches
FULL OUTER JOIN: Everything from both sides
wrapping up
I hope you enjoyed this little lesson on SQL Joins!
Remember, JOINS are the glue of SQL. Mastering them will allow you to: ✅ Combine multiple datasets into one view ✅ Identify gaps and mismatches ✅ Build richer insights for business questions
Next time you’re writing a query, think: Do I need just the matches, or everything from one side (or both)?
That answer will guide which JOIN you use.
💡 CONFESSION TIME: I didn’t fully understand JOINs until I had to write complex queries in the real world. In my next post, I’ll share how JOINs finally “clicked” for me — and why it’s normal if they still feel confusing at first.
👉 In a future post, I’ll share how JOINs came together for me — and why it’s completely normal if you don’t feel 100% confident with them yet.
Until next time,
Ardonna •ᴗ•
author’s note
Hi, I’m Ardonna Cardines — a data analyst and creator of Mercury Musings, where business meets imagination through data. I love blending analytics, design, and storytelling to make learning data modeling and visualization approachable — one creative dataset at a time.
If you’ve enjoyed this post, I’d love for you to follow along. You can subscribe to my blog for new tips + tutorials, creative datasets, and behind-the-scenes projects — or connect with me on LinkedIn where I share updates and learning resources for analysts and data storytellers.
Mercury rules Virgo, and as a Virgo, I’ve always been detail-oriented, analytical, and curious about how things connect. If you asked me ten years ago that my career would revolve around data and analytics, I wouldn’t have believed you. I was a pre-nursing major in college and that first year, I realized the medical field wasn’t for me. By sophomore year, I was lost trying to figure out a new major to switch to and I spent weeks trying to decide until I came across Information Management.
I know, soooooooooo interesting and cool right?!
Tbh, I don’t remember exactly how I discovered it but I remember part of my decision stemmed from taking a business class that all freshman students were required to take. It was an introductory course to using tools like Excel and PowerPoint. Out of all the classes I took my freshman year, that was the one I enjoyed the most.
It wasn’t until my senior year of my undergrad that I finally realized I was on the right path and doing something I enjoyed. Funnily enough, it wasn’t until a couple of years ago that I made the connection between my sun sign (Virgo) and the field I ended up in — ha ha ha. If you’re even a *little* into astrology, then you’d know that Virgo is ruled by Mercury.
Which leads me into the next part: my blog name.
the name: mercury musings
I’ve never been one for boring usernames or titles — I’ve lost count of all the moments in my academic career where I’d sit down to write an essay or report and I’d spend an absurd amount of time just trying to think of a title. If you’re a millennial who grew up watching SpongeBob, this is me just thinking of a title:
Any who, back to why I chose the name. I wanted something unique and creative that would play on both my personality and professional style:
Mercury = planet of communication, detail, and intellect
+
musings = casual reflections, notes, and insights.
Together, Mercury Musings reflects both my professional mindset and my personal curiosity: thoughtful, precise, and always seeking connections.
And if you’re a Sailor Moon fan, you can already guess which character speaks to me the most:
what you’ll find here.
Mercury Musings is a place where I’ll share:
Tips + Tutorials — breaking down concepts and techniques in data science and analytics.
Project Reflections — walking through real-world analyses, from dashboards to clustering models.
Insights — thoughts on decision-making, detail-oriented work, and the learning process.
My goal is to write in a way that’s approachable, useful, and a little personal — mixing the precision of analytics with the curiosity that Mercury inspires. And I probably won’t be able to help myself from throwing in a meme or two or many…
Fair Warning: You’ll probably even see me post about random non-data related things that I think are cool or stuff that I’m obsessed with like a new book, my favorite brand of yarn, my go-to purse, or my new favorite game •⩊•
looking ahead.
For now, this space is a blog — a place for me to write, reflect, and share. Over time, it will grow into a larger portfolio, with project showcases, teaching resources, and eventually research.
In other words, Mercury Musings is not just about where I am today, but where I’m headed. So —
welcome.
If you’re interested in analytics, detail-driven decision-making, or simply enjoy thoughtful yapping from a Virgo mind, I invite you to follow along.
Welcome to Mercury Musings.
Until next time,
Ardonna •ᴗ•
author’s note
Hi, I’m Ardonna Cardines — a data analyst and creator of Mercury Musings, where business meets imagination through data. I love blending analytics, design, and storytelling to make learning data modeling and visualization approachable — one creative dataset at a time.
If you’ve enjoyed this post, I’d love for you to follow along. You can subscribe to my blog for new tips + tutorials, creative datasets, and behind-the-scenes projects — or connect with me on LinkedIn where I share updates and learning resources for analysts and data storytellers.
