SQL Joins with Aggregate Functions and Group By

This weekend, I’ll be revisiting one SQL topic that can be a little confusing at first but is very important to understand: JOINS In simple terms, “joins” help us combine data from different tables so we can get more meaningful insights. The common types I’m revising are: INNER JOIN – returns only the matching records from both tables LEFT JOIN – returns all records from the left table and the matching ones from the right RIGHT JOIN – returns all records from the right table and the matching ones from the left FULL JOIN – returns all matching and non-matching records from both tables CROSS JOIN – returns every possible combination of rows from both tables One thing I’m learning is that understanding joins is not just about memorising definitions. It’s about knowing when to use each one and what kind of result you want from your data. So this weekend is for more revision, more practice, and more clarity - one query at a time🤗 Which SQL concept are you currently revising or trying to understand better? #SQL #DataAnalytics #DataAnalysis #Omolabakethedataanalyst

Window functions are one of the most useful SQL skills you can learn! I just uploaded a full breakdown of SQL Window Functions using healthcare data examples, and the goal was simple: make this topic feel clear, practical, and actually useful. Window functions are one of the most valuable SQL skills you can learn because they help you do deeper analysis **without losing row level detail**. That means you can rank records, compare values within groups, build running totals, and analyze trends in a much smarter way. In this video, here’s what to expect: * A simple explanation of what window functions are * A clear breakdown of `OVER()`, `PARTITION BY`, and `ORDER BY` * Easy examples of `ROW_NUMBER()`, `RANK()`, and `DENSE_RANK()` * How to calculate averages and running totals without collapsing your data * How `LAG()` and `LEAD()` help you compare rows * How functions like `NTILE()`, `FIRST_VALUE()`, `LAST_VALUE()`, and `PERCENT_RANK()` work * Real healthcare style examples to make the concepts easier to understand Why are window functions worth learning? * They come up in real analytics work all the time * They make your SQL much stronger and more advanced * They are great for interviews * They help you think like a real analyst, not just someone writing basic queries * Once you understand them, a lot of advanced SQL starts to feel much easier If you're learning SQL and want to get more confident with intermediate concepts, this is definitely a video worth watching. #sql #mysql #sqltutorial #windowfunctions #dataanalytics #learnsql #healthcareanalytics #healthinformatics #healthtech #analytics #dataskills https://lnkd.in/g4NwuTKg

🚀 Day 36/100 — SQL Indexes: Speeding Up Queries ⚡📊 Today I learned how to improve query performance using Indexes in SQL — a key concept in real-world systems. 📊 What is an Index? 👉 A data structure that improves the speed of data retrieval 👉 Works like an index in a book — helps you find data faster 📌 What I explored today: 🔹 Creating indexes 🔹 How indexes improve performance 🔹 When to use (and avoid) indexes 🔹 Impact on large datasets 💻 Example: CREATE INDEX idx_customer_id ON orders(customer_id); 📊 Without Index: ❌ Full table scan (slow) 📊 With Index: ✅ Faster data retrieval 🔥 Key Learnings: 💡 Indexes significantly improve query performance 💡 Very useful for large datasets 💡 Overusing indexes can slow down inserts/updates 🚀 Real-world use cases: ✔ High-performance applications ✔ Large databases (millions of rows) ✔ Frequently searched columns 🔥 Pro Tip: 👉 Use indexes on: Columns used in WHERE Columns used in JOIN Columns used in ORDER BY 📊 Tools Used: SQL | MySQL ✅ Day 36 complete. 👉 Quick question: Do you focus more on writing queries or optimizing performance? 🤔 #Day36 #100DaysOfData #SQL #Indexes #PerformanceOptimization #DataAnalytics #LearningInPublic #CareerGrowth #JobReady #InterviewPrep

📅 SQL Date & Time Functions (Simple Explanation) Working with dates and time is very common in SQL. These functions help you get, format, and calculate date values easily. 👉 1. GETDATE() Returns the current date and time Example: SELECT GETDATE() 👉 2. CURRENT_TIMESTAMP Also gives current date and time (same as GETDATE) 👉 3. GETUTCDATE() Returns current UTC date and time (global time) 👉 4. DATEADD() Adds or subtracts time from a date Example: Add 5 days → DATEADD(DAY, 5, GETDATE()) 👉 5. DATEDIFF() Finds difference between two dates Example: DATEDIFF(DAY, '2024-01-01', '2024-01-10') → 9 days 👉 6. DATENAME() Returns name of date part (like month or day) Example: DATENAME(MONTH, GETDATE()) → April 👉 7. DATEPART() Returns numeric value of date part Example: DATEPART(YEAR, GETDATE()) → 2026 👉 8. FORMAT() Formats date in different styles Example: FORMAT(GETDATE(), 'dd-MM-yyyy') 👉 9. ISDATE() Checks if value is a valid date Example: ISDATE('2026-04-27') → 1 (Valid) --- 💡 Why these are important? Used in reports 📊 Helps filter data by date 📅 Useful in real-time applications ⏱️ --- #SQL #DataAnalytics #SQLServer #Learning #TechBasics #Database #ITSkills

As part of my journey into Data Analytics, I continued strengthening my SQL skills today by practicing data filtering techniques in MySQL Workbench. Today’s focus was on: • Using the WHERE clause to filter specific records • Applying logical operators like AND, OR, and NOT • Working with the LIKE statement to search for patterns within datasets Practicing these queries is helping me better understand how analysts extract meaningful information from raw data. Going back to the fundamentals is improving my confidence and accuracy when working with databases. Sharing a snapshot of my practice session as I continue learning and growing step by step. Consistency is key, and I’m committed to becoming better every day. #SQL #DataAnalytics #LearningJourney #WomenInTech #MySQL

Day 18/30 of SQL Challenge Today I learned: FULL JOIN After exploring INNER, LEFT, and RIGHT JOIN, today was about combining everything together. Concept: FULL JOIN returns all records from both tables. If there is a match, data is combined. If there is no match, NULL values appear for the missing side. Basic syntax: SELECT columns FROM table1 FULL JOIN table2 ON table1.column = table2.column; Example: SELECT customers.name, orders.id FROM customers FULL JOIN orders ON customers.id = orders.customer_id; Explanation: * All customers are included * All orders are included * Matching records are combined * Non-matching records show NULL values Key understanding: FULL JOIN gives a complete view of both tables, including matched and unmatched data. Practical use cases: * Finding all matched and unmatched records * Data comparison between two tables * Identifying missing relationships on both sides Important note: Not all databases support FULL JOIN directly (like MySQL). In such cases, it can be simulated using UNION of LEFT JOIN and RIGHT JOIN. Example (conceptual idea): SELECT ... FROM customers LEFT JOIN orders ON ... UNION SELECT ... FROM customers RIGHT JOIN orders ON ... Reflection: Today helped me understand how to analyze complete datasets, including gaps and mismatches not just perfect matches. #SQL #LearningInPublic #Data #BackendDevelopment #SQLPractice #BuildInPublic

🚀 Day 9 of My SQL Learning Journey — Understanding the HAVING Clause 📊 Today I learned a small SQL concept that makes a big difference in real-world data analysis — HAVING. Most people stop at WHERE. But real insights start after grouping data. 🔹 WHERE filters rows 🔹 HAVING filters grouped results 👉 That means HAVING helps answer questions like: ✔ Which departments have more employees? ✔ Which customers appear multiple times? ✔ Which groups cross a certain threshold? 💡 Key Realisation: Data analysis isn’t just about retrieving rows — it’s about filtering meaningful summaries. Learning SQL this way is changing how I think about data, not just how I write queries. 📈 One concept at a time. Done right. On to Day 10… 🚀 #SQL #DataAnalytics #LearningInPublic #MySQL #HAVING #Consistency #CareerGrowth #Placements #CSE

📊 Today I Learned: 3 More SQL Operator Groups! Continuing my data journey, today I explored some important SQL operators under the guidance of Satish Dhawale Sir from SkillCourse. 🔹 1. BETWEEN, LIKE, and IN Operators BETWEEN helps filter data within a range LIKE is used for pattern matching IN allows matching multiple values easily 🔹 2. Other SQL Operators Comparison operators like =, !=, >, <, >=, <= Special conditions like IS NULL and IS NOT NULL 🔹 3. Set Operators in SQL UNION – combines unique results UNION ALL – combines all results (including duplicates) INTERSECT – returns common data EXCEPT – returns data from one query not in another These operators are essential for writing efficient and powerful SQL queries. Slowly building strong fundamentals in data analysis 🚀 #SQL #DataAnalytics #LearningJourney #SkillCourse #SatishDhawale #Database #SQLQueries #DataScience #TechSkills #CareerGrowth #PracticeMakesPerfect

Day 13 of learning SQL 🚀 Today I learned how to use Temporary Tables in MySQL to store and reuse data during a session. This concept helped me understand how to break down complex queries and work with intermediate results more efficiently. Topics I covered: ✔ Creating temporary tables manually ✔ Inserting data into temp tables ✔ Creating temp tables directly from SELECT queries ✔ Using temp tables for filtering and analysis Example I practiced: CREATE TEMPORARY TABLE salary_over_50k AS SELECT * FROM employee_salary WHERE salary >= 50000; Key learning today 💡 Temporary tables exist only during the session They help simplify complex queries Useful when the same filtered data is needed multiple times Make SQL workflows more structured and efficient Step by step, moving towards writing cleaner and more practical SQL queries. Goal: Become job-ready in SQL & Data Analysis 💪 #SQL #DataAnalytics #LearningInPublic #100DaysOfCode #Consistency

SQL JOIN'S 📌 While learning SQL, one thing became clear to me — data is usually not stored in one place. It’s divided into multiple tables, and that’s where JOIN becomes important. So, what is SQL JOIN? SQL JOIN is used to combine data from different tables using a common column, so we can see the complete picture. Simple example: We have: Customers table (Customer_ID, Name) Orders table (Customer_ID, Order_Amount) Using JOIN, we can connect both tables and understand: which customer made which purchase Types of JOIN: INNER JOIN– gives only matching data from both tables, LEFT JOIN– gives all data from left table + matching from right, RIGHT JOIN – gives all data from right table + matching from left, FULL JOIN– gives all data from both tables, What I understood: JOIN is not just about writing queries… it’s about connecting data to understand what’s actually happening. #SQL #DataAnalytics #LearningJourney #BusinessAnalytics #DataScience