Today I explored SQL functions small, built-in commands that transform or summarize data. They’re grouped into: 💡 Practical Example Idea: Use DATEPART() to extract the month from an admission date, then group admissions by month for seasonal trend analysis in respiratory illnesses. I’m still experimenting with these on sample data, but the potential for transforming…
Author: rxpipeline
Advanced Filtering in SQL (AND, OR, NOT)
As I get deeper into SQL, today’s focus was on logical filtering operators — AND, OR, and NOT. These are the building blocks for refining queries and narrowing down datasets. By mastering these logical filtering operators, I can create more precise queries that yield the exact results I need. Understanding these filtering data techniques in…
Set Operators in SQL (UNION, INTERSECT, EXCEPT)
Learning set operators in SQL today reminded me of Venn diagrams. They let you combine or compare results from multiple queries: 💡 Practical Example Idea: Compare medication order lists between two facilities to see common treatments (INTERSECT) or unique ones (EXCEPT). For now, it’s just theory and small sample data, but I’m starting to picture…
Introduction to SQL Joins (INNER, LEFT, RIGHT, FULL)
Today I started with SQL joins, which let you pull data from multiple tables in a single query. So far, I’ve covered: 💡 Practical Example Idea: In a clinical setting, you could join a patient demographics table with a lab results table to see both in one view, could be useful for reviewing patient outcomes…
Filtering Data with WHERE Clauses in SQL
As part of my SQL learning journey, I’ve reached one of the most practical topics so far: filtering data using the WHERE clause. This is where SQL starts feeling useful for real-world applications, even for clinicians like myself. The WHERE clause lets you pull only the data that meets certain conditions. This means you can…
From SQL Basics to Future Clinical Projects
Right now, my SQL skills are at the basics: selecting, filtering, and joining data. But I’m already thinking ahead to what small, portfolio-friendly projects could look like. Some ideas for later: These are future goals, not current work. However, learning SQL now lays the foundation. It’s a lot like starting a new clinical specialty: you…
Small Wins: My First SQL Query That Made Sense
Today, I ran my first SQL query that made complete sense. It wasn’t fancy, just selecting a few columns from a dataset and filtering for a certain value, but it worked exactly as intended. That little “yes!” moment reminded me of when a patient’s INR finally comes into range after days of adjustments. Right now,…
Thinking in Tables: How SQL Changes Perspective
One thing I didn’t expect when starting SQL was how much it would change the way I see data. As pharmacists, we’re used to patient charts, medication profiles, and lab reports; often presented as separate pieces. SQL forces you to think in tables and relationships: Learning about joins showed me how these pieces could be…
The SQL Learning Curve (and Why it Feels Like Pharmacokinetics)
Learning SQL has reminded me of learning pharmacokinetics for the first time. There’s a language, a structure, and certain rules that you can’t ignore if you want the right outcome. In the early lessons, I’ve been experimenting with: Right now, these are just exercises with practice datasets. But in clinical terms, I can see how…
Starting SQL: Why a Clinical Pharmacist is Learning It
As a clinical pharmacist, most of my day revolves around patient cases: verifying doses, adjusting therapies, and reconciling medications. But behind all of this is data, and I’ve always wondered how much more effective I could be if I could pull and shape that data myself. That’s why I’m learning SQL from scratch. I’m starting…