SQL functions like COUNT(), SUM(), AVG(), MIN(), and MAX() allow you to summarize and calculate data directly in your queries. Example: This counts patients on warfarin. 💡 Future clinical idea: Use AVG() to calculate the average length of stay for patients on a specific antibiotic regimen to track stewardship outcomes.
Category: Learning Journal
Set Operators
Set operators: UNION, INTERSECT, and EXCEPT, help combine or compare query results. Example: This merges patients from both lists. 💡 Future clinical idea: Use INTERSECT to identify patients who are both in a high-risk med list and have a recent adverse event record.
SQL Joins Advanced
Beyond INNER JOIN, SQL also has LEFT, RIGHT, and FULL joins. These can bring in unmatched rows, letting you see what’s missing. LEFT JOIN retrieves all records from the left table and the matched records from the right table, while RIGHT JOIN does the opposite, fetching all records from the right table along with matched…
SQL Joins Basics
The INNER JOIN lets you combine related data from multiple tables. It’s a core SQL concept for connecting information. By using INNER JOIN, you ensure that only the records with matching values in both tables are retrieved, which helps maintain data integrity and relevance. For those looking to deepen their understanding of database interactions, sql…
Filtering Data in SQL
One of the first essential skills in SQL is filtering data with the WHERE clause. It’s how you narrow your results to exactly what you need. Example: This query shows only patients older than 65. In the future, I could use this approach to quickly find all geriatric patients on a high-risk medication to support…
SQL Functions for Data Transformation
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…
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…