📊 Interviewer: How do you remove duplicate records in SQL?

👋 Me: We can remove duplicates using DISTINCT, GROUP BY, or delete duplicate rows using ROW_NUMBER().


✅ 1️⃣ Using DISTINCT (to fetch unique values)

SELECT DISTINCT column_name
FROM employees;

👉 Returns unique records but does not delete duplicates.


✅ 2️⃣ Using GROUP BY (to identify duplicates)

SELECT name, COUNT(*)
FROM employees
GROUP BY name
HAVING COUNT(*) > 1;

👉 Helps find duplicate records.


✅ 3️⃣ Delete Duplicates Using ROW_NUMBER() (Most Important ⭐)
(Keeps one record and deletes others)

DELETE FROM employees
WHERE id IN (
  SELECT id FROM (
    SELECT id,
           ROW_NUMBER() OVER (
             PARTITION BY name, salary
             ORDER BY id
           ) AS rn
    FROM employees
  ) t
  WHERE rn > 1
);

🧠 Logic Breakdown:

- DISTINCT → shows unique records
- GROUP BY → identifies duplicates
- ROW_NUMBER() → removes duplicates safely


✅ Use Case: Data cleaning, ETL processes, data quality checks.

💡 Tip: Always take a backup before deleting duplicate records.

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✅ Excel Interview Questions with Answers 📊💼

1️⃣ How do you clean a messy dataset in Excel?
Steps:
- TRIM() → removes extra spaces =TRIM(A1)
- CLEAN() → removes non-printable characters =CLEAN(A1)
- Remove Duplicates → Data → Remove Duplicates
- Text to Columns → split data
- Find & Replace (Ctrl+H) → fix values
- Filter → remove blanks or errors

2️⃣ Absolute vs Relative References
Relative (A1) → changes when copied
Absolute ($A$1) → stays fixed
When to use:
- Relative → normal calculations
- Absolute → fixed values (tax rate, constants)

3️⃣ Create PivotTable for Sales Analysis
Steps:
1. Select data
2. Insert → PivotTable
3. Drag: Region → Rows, Product → Columns, Sales → Values
Used for fast data summarization.

4️⃣ VLOOKUP Formula + #N/A Fix
Formula: =VLOOKUP(A2, Sheet2!A:B, 2, FALSE)
Fix #N/A:
- Check lookup value exists
- Match data types
Use: =IFERROR(VLOOKUP(A2, A:B, 2, FALSE),"Not Found")

5️⃣ INDEX-MATCH vs VLOOKUP
VLOOKUP: =VLOOKUP(A2,A:B,2,FALSE)
INDEX-MATCH: =INDEX(B:B, MATCH(A2,A:A,0))
✅ Why INDEX-MATCH?
- Faster for large data
- Works left lookup
- More flexible

6️⃣ COUNTIF vs SUMIF vs COUNTIFS
COUNTIF → count condition =COUNTIF(A:A,"East")
SUMIF → sum condition =SUMIF(A:A,"East",B:B)
COUNTIFS → multiple conditions =COUNTIFS(A:A,"East",B:B,">500")

7️⃣ Goal Seek
Used for what-if analysis.
Steps:
1. Data → What-if Analysis → Goal Seek
2. Set cell → target value
3. Change variable cell
Example: target revenue calculation.

8️⃣ Conditional Formatting Top 10%
Steps: Select data
Home → Conditional Formatting
Top/Bottom Rules → Top 10%

9️⃣ Dynamic Dashboard + Slicers
Create PivotTable
Insert → Slicer
Insert → Timeline (for dates)
Connect slicers to multiple visuals
Used for interactive dashboards.

🔟 SUMPRODUCT (Multi-condition sum)
=SUMPRODUCT((A2:A10="East")(B2:B10>500)C2:C10)
Used for weighted or multiple-condition calculations.

1️⃣1️⃣ What is Power Query?
Excel’s ETL tool.
Steps:
- Get Data → Load data
- Remove columns
- Change types
- Remove duplicates
- Load cleaned data
Used for automation and transformation.

1️⃣2️⃣ Freeze Panes vs Split Panes
Freeze Panes → lock rows/columns while scrolling
Split Panes → divide screen into sections

1️⃣3️⃣ XLOOKUP vs VLOOKUP
XLOOKUP: =XLOOKUP(A2,A:A,B:B)
✅ Advantages:
- Left lookup
- No column index
- Default exact match
- Handles errors

1️⃣4️⃣ Circular References Fix
Occurs when formula refers to itself.
Fix:
Formulas → Error Checking → Circular References
Correct formula logic

1️⃣5️⃣ Data Validation + Named Range
Steps:
1. Formulas → Define Name
2. Data → Data Validation → List
3. Select named range
Used for dropdown lists.

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

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SQL Interview Questions with Answers

✅ 16. Write a query to find the 2nd highest salary from Employee table using subquery OR window function.
⭐ Using Subquery

SELECT MAX(salary) AS second_highest_salary 
FROM employees 
WHERE salary < (SELECT MAX(salary) FROM employees);

⭐ Using Window Function

SELECT salary 
FROM (
  SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk 
  FROM employees
) t 
WHERE rnk = 2;

✅ 17. Explain INNER JOIN vs LEFT JOIN vs FULL JOIN with examples for employees and departments.
⭐ INNER JOIN → Only matching records

SELECT e.name, d.department_name 
FROM employees e 
INNER JOIN departments d ON e.department_id = d.id;

⭐ LEFT JOIN → All employees + matching departments

SELECT e.name, d.department_name 
FROM employees e 
LEFT JOIN departments d ON e.department_id = d.id;

⭐ FULL JOIN → All records from both tables

SELECT e.name, d.department_name 
FROM employees e 
FULL JOIN departments d ON e.department_id = d.id;

✅ 18. Find and remove duplicate records using CTE + ROW_NUMBER().
⭐ Find Duplicates

WITH cte AS (
  SELECT *, ROW_NUMBER() OVER(PARTITION BY email ORDER BY id) rn 
  FROM employees
)
SELECT * FROM cte WHERE rn > 1;

⭐ Remove Duplicates

WITH cte AS (
  SELECT *, ROW_NUMBER() OVER(PARTITION BY email ORDER BY id) rn 
  FROM employees
)
DELETE FROM cte WHERE rn > 1;

✅ 19. Explain WHERE vs HAVING with GROUP BY. Show department-wise avg salary > 50k.
👉 Difference
WHERE → filter before grouping
HAVING → filter after grouping

SELECT department_id, AVG(salary) AS avg_salary 
FROM employees 
GROUP BY department_id 
HAVING AVG(salary) > 50000;

✅ 20. Explain RANK vs DENSE_RANK vs ROW_NUMBER partitioned by department ordered by salary.

SELECT name, department_id, salary, 
  ROW_NUMBER() OVER(PARTITION BY department_id ORDER BY salary DESC) rn,
  RANK() OVER(PARTITION BY department_id ORDER BY salary DESC) rnk,
  DENSE_RANK() OVER(PARTITION BY department_id ORDER BY salary DESC) drnk
FROM employees;

✅ 21. Find top 5 products by total sales using GROUP BY + LIMIT.

SELECT product_id, SUM(sales_amount) AS total_sales 
FROM sales 
GROUP BY product_id 
ORDER BY total_sales DESC 
LIMIT 5;

✅ 22. Write a self join to show employee name and manager name.

SELECT e.name AS employee, m.name AS manager 
FROM employees e 
LEFT JOIN employees m ON e.manager_id = m.employee_id;

✅ 23. Handle NULL salaries using COALESCE, IS NULL, IFNULL.
⭐ Using COALESCE

SELECT name, COALESCE(salary, 0) AS salary 
FROM employees;

⭐ Using IS NULL

SELECT * FROM employees WHERE salary IS NULL;

✅ 24. Pivot sales data by month using CASE statement.

SELECT 
  SUM(CASE WHEN month = 'Jan' THEN sales ELSE 0 END) AS Jan,
  SUM(CASE WHEN month = 'Feb' THEN sales ELSE 0 END) AS Feb,
  SUM(CASE WHEN month = 'Mar' THEN sales ELSE 0 END) AS Mar
FROM sales;

✅ 25. Subquery vs JOIN — which is faster? Why?
JOIN is usually faster, subquery is easier to read.

✅ 26. Write a recursive CTE for company hierarchy (CEO → managers → employees).

WITH RECURSIVE emp_hierarchy AS (
  SELECT employee_id, name, manager_id 
  FROM employees 
  WHERE manager_id IS NULL
  UNION ALL
  SELECT e.employee_id, e.name, e.manager_id 
  FROM employees e 
  JOIN emp_hierarchy h ON e.manager_id = h.employee_id
)
SELECT * FROM emp_hierarchy;

✅ 27. Explain clustered vs non-clustered indexes. When to use each?
⭐ Clustered Index: physically sorts table data
⭐ Non-Clustered Index: separate structure pointing to data

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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✅ 🔤 A–Z of Data Analyst Terms 📊💻🚀

A – A/B Testing
Experiment comparing two versions to see which performs better.

B – Business Intelligence (BI)
Technologies and processes for analyzing business data.

C – Correlation
Measure of relationship between two variables.

D – Data Cleaning
Process of fixing or removing incorrect/incomplete data.

E – ETL (Extract, Transform, Load)
Process of moving and preparing data for analysis.

F – Forecasting
Predicting future trends based on historical data.

G – Granularity
Level of detail in data (daily, monthly, yearly).

H – Hypothesis
Assumption made for testing using data.

I – Insight
Meaningful interpretation derived from data analysis.

J – Join
Combining data from multiple tables.

K – KPI (Key Performance Indicator)
Metric used to measure performance.

L – Linear Regression
Statistical method to model relationship between variables.

M – Metrics
Quantifiable measures used to track performance.

N – Normalization
Organizing data to reduce redundancy.

O – Outlier
Data point significantly different from others.

P – Pivot Table
Tool to summarize and analyze data.

Q – Query
Request to retrieve specific data.

R – Regression Analysis
Technique for predicting relationships between variables.

S – Segmentation
Dividing data into groups for analysis.

T – Trend Analysis
Identifying patterns over time.

U – Unstructured Data
Data without predefined format (text, images).

V – Visualization
Presenting data graphically (charts, dashboards).

W – Warehouse (Data Warehouse)
Central repository for integrated data.

X – X-Axis
Horizontal axis in charts.

Y – YoY (Year-over-Year)
Comparison of metrics from one year to another.

Z – Z-Score
Statistical measurement of how far a value is from mean.

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📊 Don’t Overwhelm to Learn Data Analytics — Data Analytics is Only This Much 🚀

🔹 FOUNDATIONS

1️⃣ What is Data Analytics
- Collecting data
- Cleaning data
- Analyzing data
- Finding insights
- Supporting decision-making

2️⃣ Excel (Basic Tool)
- Formulas (SUM, IF, VLOOKUP, INDEX-MATCH)
- Pivot Tables
- Charts
- Data cleaning
- Conditional formatting

🔥 Still heavily used in companies

3️⃣ SQL (Most Important ⭐)

- SELECT, WHERE
- GROUP BY, HAVING
- JOINS (INNER, LEFT, RIGHT)
- Subqueries
- CTE
- Window functions
- Indexing basics

🔥 If you practice SQL daily — big advantage

4️⃣ Statistics Basics
- Mean, median, mode
- Variance & standard deviation
- Probability basics
- Distribution concepts
- Correlation

🔥 CORE DATA ANALYTICS SKILLS

5️⃣ Python for Data Analysis
- NumPy
- Pandas
- Data cleaning
- Handling missing values
- Data transformation

6️⃣ Data Visualization
- Matplotlib
- Seaborn
- Power BI
- Tableau

🔥 Storytelling with data is key

7️⃣ Data Cleaning (Very Important ⭐)

- Handling null values
- Removing duplicates
- Data standardization
- Outlier detection

8️⃣ Exploratory Data Analysis (EDA)
- Understanding patterns
- Finding trends
- Correlation analysis
- Feature understanding

9️⃣ Business Understanding
- KPIs
- Metrics
- Business problems
- Stakeholder communication

🔥 What separates analyst from report generator

🚀 ADVANCED ANALYTICS

🔟 Dashboard Development
- Power BI dashboards
- Tableau dashboards
- Interactive reports
- Drill-down analysis

1️⃣1️⃣ Data Storytelling
- Presenting insights
- Creating reports
- Communicating findings clearly

1️⃣2️⃣ Basic Machine Learning (Optional)
- Regression
- Classification
- Forecasting

(Helpful but not mandatory for analyst role)

1️⃣3️⃣ A/B Testing
- Hypothesis testing
- Statistical significance
- Business experiments

1️⃣4️⃣ Data Warehousing Concepts
- Fact & dimension tables
- Star schema
- ETL basics

⚙️ INDUSTRY SKILLS

1️⃣5️⃣ Data Pipelines
- Extract → Transform → Load
- Data automation

1️⃣6️⃣ Automation
- Python scripts
- Scheduled reports

1️⃣7️⃣ Soft Skills
- Communication
- Presentation skills
- Explaining technical results simply

🔥 Extremely important in interviews

⭐ TOOLS TO MASTER
- Excel
- SQL ⭐
- Python
- Power BI / Tableau
- Basic statistics

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📊 Data Analytics Fundamentals — Part:1

Data Analytics is the process of collecting, cleaning, transforming, and analyzing data to find useful insights that help businesses make better decisions.

👉 In simple words:

Data Analytics = Turning raw data into meaningful information.

Companies generate huge amounts of data daily (sales, customers, website visits, transactions). A data analyst converts this raw data into insights that improve performance and solve business problems.

✅ Why Data Analytics is Important
- Helps companies make data-driven decisions
- Improves business performance
- Identifies trends and patterns
- Predicts future outcomes
- Reduces risks
- Improves customer experience

👉 Example:
- Amazon recommends products → data analytics
- Netflix suggests movies → data analytics
- Companies track sales performance → data analytics

🔄 Data Analytics Process (Step-by-Step)

1️⃣ Data Collection
Gathering data from different sources.
Sources include:
- Databases
- Excel files
- Websites
- Surveys
- Business applications
- APIs
👉 Example: Sales data, customer data, website traffic.

2️⃣ Data Cleaning (Most Time-Consuming Step ⭐)
Raw data is messy and contains errors. Cleaning includes:
- Removing duplicates
- Handling missing values
- Fixing incorrect data
- Standardizing formats
👉 Example: Fixing names like “Rahul”, “rahul”, “RAHUL” into one format.
💡 Fun Fact: Data analysts spend ~70–80% of time cleaning data.

3️⃣ Data Analysis
Applying techniques to understand data. Includes:
- Finding trends
- Comparing values
- Calculating metrics
- Identifying patterns
👉 Example: Finding which product sells the most.

4️⃣ Finding Insights
Converting analysis into meaningful conclusions.
👉 Example:
- Sales drop on weekends
- Customers prefer online payments
- Certain regions generate more profit
Insights answer “Why is this happening?”

5️⃣ Supporting Decision Making (Final Goal ⭐)
Using insights to help businesses take action.
👉 Example:
- Increase marketing in high-performing regions
- Improve weak products
- Optimize pricing strategy
💡 Final purpose of data analytics = Better decisions.

🧠 Types of Data Analytics (Interview Important)

1️⃣ Descriptive Analytics — What happened?
- Past data analysis
- Reports and dashboards
👉 Example: Monthly sales report.

2️⃣ Diagnostic Analytics — Why it happened?
- Root cause analysis
👉 Example: Why sales dropped last month.

3️⃣ Predictive Analytics — What will happen?
- Forecasting future trends
👉 Example: Next month sales prediction.

4️⃣ Prescriptive Analytics — What should we do?
- Suggests best actions
👉 Example: Best pricing strategy.

💼 Real-Life Example of Data Analytics
🛒 E-commerce Company
- Collect customer purchase data
- Clean incorrect records
- Analyze buying patterns
- Find popular products
- Recommend products to customers
Result → More sales.

⭐ Role of a Data Analyst
A data analyst:
✅ Collects data
✅ Cleans data
✅ Analyzes data
✅ Finds patterns
✅ Builds reports/dashboards
✅ Communicates insights
👉 Not just numbers — solving business problems.

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📊 Data Analytics Fundamentals — Part:2

📊 Excel in Data Analytics

• Microsoft Excel is a spreadsheet tool used for data cleaning, analysis, and visualization using formulas, pivot tables, and charts.
• Companies use Excel daily for reporting, dashboards, and quick analysis.

⭐ Why Excel is Important for Data Analysts
• Used in almost every organization
• Best tool for quick analysis
• Helps clean messy data
• Creates reports and dashboards
• Used in interviews and real jobs
• Many companies expect strong Excel skills before SQL/Python.

🔑 Core Excel Skills for Data Analytics

1️⃣ Formulas  Functions (Most Important ⭐)
• Formulas help perform calculations automatically.
• Common formulas:
    – SUM() → Adds numbers
    – AVERAGE() → Finds average
    – IF() → Conditional logic
    – VLOOKUP() → Search data vertically
    – INDEX + MATCH → Advanced lookup
    – COUNT() / COUNTIF() → Count values
• Examples:
    – Find total sales
    – Check pass/fail results
    – Merge data from two sheets

2️⃣ Pivot Tables (Very Important ⭐)
• Summarize large data quickly
• Used for:
    – Grouping data
    – Calculating totals
    – Comparing categories
    – Creating reports
• Examples:
    – Total sales by region
    – Employee count by department
    – Monthly revenue summary

3️⃣ Data Cleaning in Excel
• Raw data contains errors — Excel helps fix them.
• Common cleaning tasks:
    – Remove duplicates
    – Handle missing values
    – Trim extra spaces
    – Split text into columns
    – Standardize formats
• Tools used:
    – Remove Duplicates
    – Text to Columns
    – Find  Replace
    – TRIM function

4️⃣ Sorting  Filtering
• Helps explore and understand data.
• Used for:
    – Finding top values
    – Filtering specific records
    – Organizing data logically
• Examples:
    – Top 10 customers
    – Filter sales above ₹50,000

5️⃣ Conditional Formatting
• Highlights important data visually.
• Examples:
    – Highlight highest sales
    – Mark low performance
    – Show trends using color

6️⃣ Charts  Visualization
• Excel creates visual reports.
• Common charts:
    – Bar chart
    – Line chart
    – Pie chart
    – Histogram
• Used for:
    – Showing trends
    – Comparing performance
    – Presenting insights

🔄 How Excel is Used in Real Data Analyst Workflow
• Step 1 → Import data
• Step 2 → Clean data
• Step 3 → Analyze using formulas/pivot tables
• Step 4 → Create charts
• Step 5 → Share report

💼 Real-World Example 🛒 Sales Analysis
• Import sales data
• Remove duplicate records
• Use pivot table for total sales
• Create chart for trends
• Share report with manager

🎯 Excel vs SQL vs Python
• Excel → Small/medium data, quick analysis
• SQL → Large database queries
• Python → Advanced analysis  automation

⭐ Excel Topics in Interviews
• VLOOKUP vs INDEX MATCH
• Pivot tables
• Conditional formatting
• Removing duplicates
• Data cleaning techniques
• Charts  dashboards

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

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🚀 Top 10 Careers in Data Analytics (2026)📊💼

1️⃣ Data Analyst
▶️ Skills: Excel, SQL, Power BI, Data Cleaning, Data Visualization
💰 Avg Salary: ₹6–15 LPA (India) / 90K+ USD (Global)

2️⃣ Business Intelligence (BI) Analyst
▶️ Skills: Power BI, Tableau, SQL, Data Modeling, Dashboard Design
💰 Avg Salary: ₹8–18 LPA / 100K+

3️⃣ Product Analyst
▶️ Skills: SQL, Python, A/B Testing, Product Metrics, Experimentation
💰 Avg Salary: ₹12–25 LPA / 120K+

4️⃣ Analytics Engineer
▶️ Skills: SQL, dbt, Data Modeling, Data Warehousing, ETL
💰 Avg Salary: ₹12–22 LPA / 120K+

5️⃣ Marketing Analyst
▶️ Skills: Google Analytics, SQL, Excel, Customer Segmentation, Attribution Analysis
💰 Avg Salary: ₹7–16 LPA / 95K+

6️⃣ Financial Data Analyst
▶️ Skills: Excel, SQL, Forecasting, Financial Modeling, Power BI
💰 Avg Salary: ₹8–18 LPA / 105K+

7️⃣ Data Visualization Specialist
▶️ Skills: Tableau, Power BI, Storytelling with Data, Dashboard Design
💰 Avg Salary: ₹7–17 LPA / 100K+

8️⃣ Operations Analyst
▶️ Skills: SQL, Excel, Process Analysis, Business Metrics, Reporting
💰 Avg Salary: ₹6–15 LPA / 95K+

9️⃣ Risk & Fraud Analyst
▶️ Skills: SQL, Python, Fraud Detection Models, Statistical Analysis
💰 Avg Salary: ₹10–20 LPA / 110K+

🔟 Analytics Consultant
▶️ Skills: SQL, BI Tools, Business Strategy, Stakeholder Communication
💰 Avg Salary: ₹12–28 LPA / 125K+

📊 Data Analytics is one of the most practical and fastest ways to enter the tech industry in 2026.

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📊 Essential SQL Concepts Every Data Analyst Must Know

🚀 SQL is the most important skill for Data Analysts. Almost every analytics job requires working with databases to extract, filter, analyze, and summarize data.

Understanding the following SQL concepts will help you write efficient queries and solve real business problems with data.

1️⃣ SELECT Statement (Data Retrieval)

What it is: Retrieves data from a table.

SELECT name, salary
FROM employees;

Use cases: Retrieving specific columns, viewing datasets, extracting required information.

2️⃣ WHERE Clause (Filtering Data)

What it is: Filters rows based on specific conditions.

SELECT *
FROM orders
WHERE order_amount > 500;

Common conditions: =, >, <, >=, <=, BETWEEN, IN, LIKE

3️⃣ ORDER BY (Sorting Data)

What it is: Sorts query results in ascending or descending order.

SELECT name, salary
FROM employees
ORDER BY salary DESC;

Sorting options: ASC (default), DESC

4️⃣ GROUP BY (Aggregation)

What it is: Groups rows with same values into summary rows.

SELECT department, COUNT(*)
FROM employees
GROUP BY department;

Use cases: Sales per region, customers per country, orders per product category.

5️⃣ Aggregate Functions

What they do: Perform calculations on multiple rows.

SELECT AVG(salary)
FROM employees;

Common functions: COUNT(), SUM(), AVG(), MIN(), MAX()

6️⃣ HAVING Clause

What it is: Filters grouped data after aggregation.

SELECT department, COUNT(*)
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

Key difference: WHERE filters rows before grouping, HAVING filters groups after aggregation.

7️⃣ SQL JOINS (Combining Tables)

What they do:

Combine tables.
-- INNER JOIN
SELECT orders.order_id, customers.customer_name
FROM orders
INNER JOIN customers
ON orders.customer_id = customers.customer_id;

-- LEFT JOIN
SELECT customers.customer_name, orders.order_id
FROM customers
LEFT JOIN orders
ON customers.customer_id = orders.customer_id;

Common types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN

8️⃣ Subqueries

What it is: Query inside another query.

SELECT name
FROM employees
WHERE salary > (SELECT AVG(salary) FROM employees);

Use cases: Comparing values, filtering based on aggregated results.

9️⃣ Common Table Expressions (CTE)

What it is: Temporary result set used inside a query.

WITH high_salary AS (
SELECT name, salary
FROM employees
WHERE salary > 70000
)
SELECT *
FROM high_salary;

Benefits: Cleaner queries, easier debugging, better readability.

🔟 Window Functions

What they do: Perform calculations across rows related to current row.

SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees;

Common functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD()

Why SQL is Critical for Data Analysts
• Extract data from databases
• Analyze large datasets efficiently
• Generate reports and dashboards
• Support business decision-making

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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