Day 3 of 30 SELECT: Your First Real SQL Query
SELECT is the most-used statement in SQL.
Get ALL columns:
SELECT * FROM students;
Get SPECIFIC columns:
SELECT id, name, age FROM students;
#30DaysOfSQL #SQLForBeginners #SQL
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Day 13 of 30. #30DaysOfSQL
NULL is not zero. NULL is not blank. NULL = no value exists. Period.
And this WILL break your queries:
WHERE Email = NULL ❌
This WILL work:
WHERE Email IS NULL ✅
You can NEVER use = with NULL. Always IS NULL or IS NOT NULL.
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Day 2 of 30
The 8 SQL Keywords You NEED to Know
Before writing your first query, memorize these:
SELECT → what columns you want
FROM → which table
WHERE → filter rows
Start here. Everything else builds on this.
#30DaysOfSQL #SQLForBeginners #SQL
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Day 12 of 30. #30DaysOfSQL
Stop writing this:
WHERE City = 'Lagos'
OR City = 'Abuja'
OR City = 'Kano'
Write this instead:
WHERE City IN ('Lagos', 'Abuja', 'Kano')
Same result. One line. Zero mess.
IN = SQL's list filter. NOT IN = SQL's exclusion tool.
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Day 11 of 30. #30DaysOfSQL
Don't know the exact value?
Use LIKE — SQL's pattern matcher
WHERE CustomerName LIKE 'Ade%'
Adebayo, Adewale, Adenike
WHERE Email LIKE '%@gmail.com'
Every Gmail user
WHERE ProductName LIKE '%rice%'
Everything with rice in the name
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Day 1 of 30 What is SQL?
SQL (Structured Query Language).
Think of it as giving instructions to a filing cabinet that holds millions of records.
You can:
Store data
Retrieve data
Update & manage data
Every app you use runs on SQL behind the scenes.
#30DaysOfSQL
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Day 10 of 30. #30DaysOfSQL
You don't always need all the data.
Sometimes you need the TOP 10.
SELECT CustomerName, Balance
FROM Customers
ORDER BY Balance DESC
LIMIT 10;
LIMIT controls how many rows come back.
ORDER BY LIMIT = instant leaderboard.
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Day 9 of 30. #30DaysOfSQL
Unsorted data is unreadable data.
Fix it with ORDER BY
SELECT CustomerName, Balance
FROM Customers
ORDER BY Balance DESC;
DESC = highest first
ASC = lowest first
Before ORDER BY → random rows
After ORDER BY → ranked results
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Day 8 of 30. Week 2 starts NOW. #30DaysOfSQL
Duplicate data is one of the most annoying things in analytics
One word fixes it
SELECT DISTINCT City FROM Customers;
Without DISTINCT → Lagos Lagos Lagos Abuja Lagos
With DISTINCT → Lagos, Abuja, Kano, PH
Clean. Unique. Done
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Day 7 of 30. Week 1 DONE. #30DaysOfSQL
Here's your Week 1 cheat sheet
SELECT * FROM Table;
SELECT Col1, Col2 FROM Table;
WHERE City = 'Lagos'
WHERE Amount > 500000
WHERE City = 'Lagos' AND Status = 'Active'
WHERE City = 'Lagos' OR City = 'Abuja'
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Day 6 of 30. #30DaysOfSQL
One condition = useful. Two conditions = powerful.
AND: both must be true
WHERE City = 'Lagos' AND Status = 'Active'
OR: either can be true
WHERE City = 'Lagos' OR City = 'Abuja'
AND narrows. OR widens.
Stack your filters. Get precise answers.
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Day 5 of 30. #30DaysOfSQL
WHERE filters your data.
But THESE are what go inside it
WHERE Salary > 500000 -- above threshold
WHERE Status != 'Inactive' -- exclude a value
WHERE Score >= 70 -- pass mark and above
WHERE Price < 1000 -- budget range
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Day 4 of 30. #30DaysOfSQL
Days 1–3: pulling data.
Day 4: FINDING data.
SELECT * FROM Customers
WHERE City = 'Lagos';
WHERE is your filter.
Only Lagos customers get through. Everything else? Gone.
One word that changes everything. 🎯
RT if WHERE just made SQL click for you.
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Day 3 of 30. #30DaysOfSQL
SELECT * = pull everything.
But pros don't pull everything.
They pull exactly what they need
sql
SELECT FirstName, Email, City
FROM Customers;
Swap * for specific column names. Separate with commas. Done.
Less noise. More signal.
RT if Day 3
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Day 2 of 30. #30DaysOfSQL
Your first SQL query:
SELECT * FROM Customers;
Translation: "Show me everything in the Customers table."
SELECT = what you want
* = all of it
FROM = where it lives
3 words. All your data. Just like that.
RT if this just made SQL less scary.
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Day 1 of 30. #30DaysOfSQL
SQL = Structured Query Language.
It's how you talk to a database.
500,000 rows of data? SQL finds what you need in 3 seconds.
Every bank, fintech, and company with data uses it.
The people who know it get ahead.
You're learning it this month. Let's go.
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HEY DATA TWITTER
I'm launching the #30DaysOfSQL Challenge tomorrow.
One SQL tip. Every day. 30 days straight.
RT to spread it. Follow so you don't miss Day 1.
Who's doing this with me? Reply below
#30DaysOfSQL #LearnSQL #DataCommunity
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Starting tomorrow:
30 days. 30 SQL tips. Zero cost.
Follow the thread. Don't fall behind.
#30DaysOfSQL
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Day 20/30: Overall fraud rate 🚨
13,335 out of 50,000 transactions flagged as fraudulent. That’s 26.67%.
Real world fraud rates sit between 1% and 5%. This is a synthetic dataset but that number set the tone for everything else in the analysis.
#30DaysOfSQL #FraudDetection
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Day 18/30: SQL query spotlight 🔍
Used CASE WHEN inside SELECT to audit nulls across all key columns at once. 50,000 rows, one query, full picture.
Protecting the analysis before it even starts.
#30DaysOfSQL #DataCleaning #LearningInPublic #SQLChallenge
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