#Sql Syntax And Data Types

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Comment “SQL” and I’ll send it. If you’re serious about working with data, you can’t avoid SQL. This guide shows what actually matters, plus hands-on resources to practice it right away.

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SQL concepts you need to know vs. don’t need to know… for your first Data Analyst job. ✅ Must know SQL concepts 1. SELECT statements 2. WHERE clauses 3. GROUP BY 4. ORDER BY 5. JOINs (INNER, LEFT, RIGHT, FULL) 6. Aggregate functions (COUNT, SUM, AVG, etc) 7. Aliases (AS) 8. Filtering with HAVING 9. Subqueries 10. CASE WHEN logic 11. Data types (VARCHAR, INT, DATE, etc.) 12. NULL handling (IS NULL, COALESCE) 13. LIMIT / OFFSET 14. Basic date functions (e.g., DATE_TRUNC, CURRENT_DATE) 15. UNION vs UNION ALL 16. Basic window functions (ROW_NUMBER, RANK, PARTITION BY) ❌ Nice-to-know (i.e. not a requirement) SQL concepts 1. Recursive CTEs 2. Pivot/Unpivot 3. Temporary tables 4. Indexing and query optimization 5. Stored procedures 6. Triggers 7. Views and materialized views 8. Permissions and user management 9. JSON parsing in SQL 10. Writing custom functions 11. Database normalization theory (3NF etc.) 12. Query execution plans Repost if this was helpful for you! #dataanalyst #dataanalytics #sql #datascience

1. QUALIFY + ROW_NUMBER() Lets you rank rows and filter results in the same query — perfect for grabbing the most recent or top record without subqueries. 2. LAG / LEAD Used to look at the previous or next row — great for comparing changes over time (day-over-day, month-over-month). 3. CTE (WITH clause) Creates a temporary, named query so you can break complex SQL into clean, readable steps. #data #analyst #dayinthelife #dadlife #sql

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Let’s get DIRTY!! 😉 It’s gonna be 80% of your job! Comment “clean” for my SQL skills cheat sheet, perfect for data projects and interview prep! Save these 4 tips to handle messy datasets w/ ease 😮‍💨 • Clean strings: remove extra spaces, weird quotes, and fix casing • Fix dates: standardize formats and remove stray characters • Recode variables: turn “CA”, “California”, and “calif” into one consistent value • Handle NULLs: don’t let missing data mess with your analysis #data #datacleaning #sql 🏷️ data, data cleaning, sql, data analytics, data science