Data Analytics

Now, let’s move to the next topic: Views (Virtual Tables) 🧠 1. What is a VIEW? A VIEW is a virtual table based on a SQL query 👉 It does NOT store data 👉 It stores the query Think like this 👇 👉 “Saved SQL query → reuse anytime” ⚡ 2. Why Use Views? - Simplify complex queries - Reuse logic - Hide sensitive data - Improve readability ⚡ 3. Create a VIEW CREATE VIEW high_salary_emp AS SELECT name, salary FROM employees WHERE salary > 50000; 🔍 4. Use a VIEW SELECT FROM high_salary_emp; ✔ Works like a normal table 🔄 5. Update a VIEW CREATE OR REPLACE VIEW high_salary_emp AS SELECT name, salary, department FROM employees WHERE salary > 50000; ❌ 6. Drop a VIEW DROP VIEW high_salary_emp; 🎯 7. Real Example 👉 Create view for department-wise average salary CREATE VIEW dept_avg_salary AS SELECT department, AVG(salary) AS avg_salary FROM employees GROUP BY department; 👉 Use it: SELECT FROM dept_avg_salary; ⚡ 8. Important Points - View does NOT store data - Changes in table → reflect in view - Can be used like a table 🎯 9. Practice Tasks 1. Create view for employees with salary > 40k 2. Create view for IT department employees 3. Create view for avg salary per department 4. Query data using created views 5. Drop a view 🔥 Here are the solutions for VIEW practice tasks ✅ 1. Create view for employees with salary > 40k CREATE VIEW high_salary_emp AS SELECT FROM employees WHERE salary > 40000; ✅ 2. Create view for IT department employees CREATE VIEW it_employees AS SELECT FROM employees WHERE department = 'IT'; ✅ 3. Create view for avg salary per department CREATE VIEW dept_avg_salary AS SELECT department, AVG(salary) AS avg_salary FROM employees GROUP BY department; ✅ 4. Query data using created views SELECT FROM high_salary_emp; SELECT FROM it_employees; SELECT FROM dept_avg_salary; ✅ 5. Drop a view DROP VIEW high_salary_emp; ⚡ Mini Challenge 🔥 👉 Create a view to show top 3 highest salary employees ⚡ Mini Challenge Solution CREATE VIEW top_3_salary AS SELECT FROM employees ORDER BY salary DESC LIMIT 3; 👉 Use the view: SELECT FROM top_3_salary; 🔥 Pro Tip: Views are heavily used in: 👉 Dashboards 👉 Reporting systems 👉 Data analytics projects Because they simplify complex SQL 💯 👉 Table → stores data 👉 View → stores query Double Tap ❤️ For More

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀😍 Kickstart Your Data Science Career In Top Tech Companies 💫Learn Tools, Skills & Mindset to Land your first Job 💫Join this free Masterclass for an expert-led session on Data Science Eligibility :- Students ,Freshers & Working Professionals 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 :- https://pdlink.in/42hIcpO ( Limited Slots ..Hurry Up‍ ) 🔥Date & Time :- 8th May 2026 , 7:00 PM

✅Skills Required to Become a Data Analyst 📊 🧠 ANALYTICAL THINKING 1. Problem Solving 2. Logical Reasoning 3. Pattern Recognition 4. Critical Thinking 5. Decision Making 6. Root Cause Analysis 7. Attention to Detail 8. Business Understanding 📊 DATA HANDLING 1. Data Cleaning 2. Data Transformation 3. Data Validation 4. Handling Missing Values 5. Data Wrangling 6. Data Integration 7. Data Formatting 8. Data Quality Checks 🗄️ SQL SKILLS 1. Writing Queries 2. Joins (INNER, LEFT, RIGHT) 3. Aggregations (SUM, COUNT, AVG) 4. Subqueries 5. CTEs 6. Window Functions 7. Indexing Basics 8. Database Optimization 🐍 PYTHON / R 1. Pandas / dplyr 2. NumPy 3. Data Cleaning Scripts 4. EDA (Exploratory Data Analysis) 5. Visualization Libraries 6. Automation 7. Statistical Analysis 8. Basic Machine Learning 📊 DATA VISUALIZATION 1. Dashboard Creation 2. Chart Selection 3. Storytelling with Data 4. Power BI / Tableau 5. KPI Design 6. Report Building 7. Interactive Visuals 8. Data Presentation 📈 STATISTICS 1. Mean, Median, Mode 2. Probability Basics 3. Hypothesis Testing 4. Correlation 5. Regression 6. Distribution 7. Sampling Techniques 8. A/B Testing 💼 BUSINESS SKILLS 1. Requirement Understanding 2. Stakeholder Communication 3. Business Metrics 4. Domain Knowledge 5. Problem Framing 6. Reporting Insights 7. Decision Support 8. Documentation ⚙️ TOOLS TECHNOLOGIES 1. Excel 2. SQL Tools (MySQL, PostgreSQL) 3. Power BI / Tableau 4. Python / R 5. Google Sheets 6. Jupyter Notebook 7. Git (Basics) 8. Cloud Basics (AWS / Azure) 💬Tap ❤️ for more

🔥 Now, let’s move to the next topic: ✅ CTE (Common Table Expressions) 🧠 1. What is a CTE? A CTE (Common Table Expression) is a temporary result set 👉 defined using WITH 👉 used to simplify complex queries Think like this 👇 👉 “Create a temporary table → use it in your query” ⚡ 2. Basic Syntax WITH cte_name AS ( SELECT ... ) SELECT * FROM cte_name; 🎯 3. Simple Example 👉 Get employees with salary > 50k WITH high_salary AS ( SELECT * FROM employees WHERE salary > 50000 ) SELECT * FROM high_salary; ✔ Makes query more readable 🔥 4. CTE with Aggregation 👉 Average salary per department WITH dept_avg AS ( SELECT department, AVG(salary) AS avg_salary FROM employees GROUP BY department ) SELECT * FROM dept_avg; ⚡ 5. CTE vs Subquery CTE -> More readable, Reusable Better for complex queries Subquery -> Hard to read Not reusable 🎯 6. Real Example (Interview Level) 👉 Employees earning above department average WITH dept_avg AS ( SELECT department, AVG(salary) AS avg_salary FROM employees GROUP BY department ) SELECT e.name, e.salary, e.department FROM employees e JOIN dept_avg d ON e.department = d.department WHERE e.salary > d.avg_salary; 🎯 7. Practice Tasks 1. Create CTE for employees with salary > 40k 2. Find average salary using CTE 3. Get employees above average salary using CTE 4. Count employees per department using CTE 5. Find highest salary per department using CTE 🔥 Here are the solutions for CTE practice tasks ✅ 1. Create CTE for employees with salary > 40k WITH high_salary AS ( SELECT * FROM employees WHERE salary > 40000 ) SELECT * FROM high_salary; ✅ 2. Find average salary using CTE WITH avg_sal AS ( SELECT AVG(salary) AS avg_salary FROM employees ) SELECT * FROM avg_sal; ✅ 3. Get employees above average salary using CTE WITH avg_sal AS ( SELECT AVG(salary) AS avg_salary FROM employees ) SELECT e.* FROM employees e, avg_sal a WHERE e.salary > a.avg_salary; 👉 Alternative (JOIN style): WITH avg_sal AS ( SELECT AVG(salary) AS avg_salary FROM employees ) SELECT e.* FROM employees e JOIN avg_sal a ON e.salary > a.avg_salary; ✅ 4. Count employees per department using CTE WITH dept_count AS ( SELECT department, COUNT(*) AS total_emp FROM employees GROUP BY department ) SELECT * FROM dept_count; ✅ 5. Find highest salary per department using CTE WITH max_sal AS ( SELECT department, MAX(salary) AS max_salary FROM employees GROUP BY department ) SELECT * FROM max_sal; ⚡ Mini Challenge 🔥 👉 Find top 2 highest salary employees per department using CTE ⚡ Mini Challenge Solution 🔥 WITH ranked_emp AS ( SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn FROM employees ) SELECT * FROM ranked_emp WHERE rn <= 2; 🔥 Pro Tip: Whenever query looks messy: 👉 Replace subquery with CTE Double Tap ❤️ For More

🔥 Now, let’s move to the next topic: ✅Window Functions 🧠 1. What are Window Functions? Window functions perform calculations without grouping rows 👉 Difference: • GROUP BY → reduces rows • Window Function → keeps all rows + adds extra column 📊 Example Table name → Amit, Ravi, Neha department → IT, IT, HR salary → 60000, 70000, 40000 ⚡ 2. Basic Syntax SELECT column, FUNCTION() OVER (PARTITION BY column ORDER BY column) FROM table; 🔥 3. ROW_NUMBER() Assigns unique rank to each row SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank FROM employees; ✔ Rank employees within each department 🥇 4. RANK() vs DENSE_RANK() 👉 RANK() (skips numbers) SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS rank FROM employees; 👉 DENSE_RANK() (no skipping) SELECT name, salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rank FROM employees; 📊 Visual Difference If salaries are 100, 90, 90, 80: • RANK() gives: 1, 2, 2, 4 • DENSE_RANK() gives: 1, 2, 2, 3 ⚡ 5. PARTITION BY (Very Important) 👉 Splits data into groups (like GROUP BY but without collapsing rows) SELECT department, name, salary, AVG(salary) OVER (PARTITION BY department) AS avg_salary FROM employees; ✔ Shows avg salary per department for each row 🎯 6. Practice Tasks 1. Rank employees by salary 2. Rank employees within each department 3. Find highest salary per department 4. Add average salary column per department 5. Find second highest salary using window function ✅Practice Tasks Solution ✅1. Rank employees by salary SELECT name, salary, ROW_NUMBER() OVER (ORDER BY salary DESC) AS rank FROM employees; ✅2. Rank employees within each department SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank FROM employees; ✅3. Find highest salary per department SELECT name, department, salary FROM ( SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn FROM employees ) t WHERE rn = 1; ✅4. Add average salary column per department SELECT name, department, salary, AVG(salary) OVER (PARTITION BY department) AS avg_salary FROM employees; ✅5. Find second highest salary using window function SELECT name, salary FROM ( SELECT name, salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk FROM employees ) t WHERE rnk = 2; ⚡Mini Challenge 🔥 👉 Get top 2 highest paid employees in each department ⚡ Mini Challenge Solution 🔥 SELECT name, department, salary FROM ( SELECT name, department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn FROM employees ) t WHERE rn <= 2; 🔥 Pro Tip: • Use ROW_NUMBER() → unique ranking • Use DENSE_RANK() → handle ties Double Tap ❤️ For More