Data Analytics

This produces a compact business summary. For example: Total_Sales: 15,000,000, Average_Sales: 75,000, Minimum_Sales: 1,000, Maximum_Sales: 500,000, Total_Orders: 200 🔟 Why Do We Need GROUP BY? Aggregate functions give you an overall summary. But what if the business asks:

"What are total sales for each region?"

You need to divide the data into groups. That's what GROUP BY does. 1️⃣1️⃣ Basic GROUP BY Suppose: North: 50,000 and 70,000 → Total 120,000 South: 40,000 and 60,000 → Total 100,000 West: 80,000 → Total 80,000 Query:

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Region;

Result: North = 120,000, South = 100,000, West = 80,000 Now you've answered:

"How much did each region sell?"

1️⃣2️⃣ GROUP BY Department Suppose you have: John - IT - 75,000 Sarah - HR - 60,000 Mike - IT - 82,000 David - Finance - 90,000 Alice - HR - 65,000 Query:

SELECT
    Department,
    AVG(Salary) AS Average_Salary
FROM Employees
GROUP BY Department;

Result: Finance: 90,000, HR: 62,500, IT: 78,500 1️⃣3️⃣ GROUP BY with COUNT() Question:

How many employees are in each department?

SELECT
    Department,
    COUNT(*) AS Employee_Count
FROM Employees
GROUP BY Department;

Result: IT: 2, HR: 2, Finance: 1 1️⃣4️⃣ GROUP BY with Multiple Columns You can group by more than one column. Suppose your sales data contains: North Electronics: 80,000 North Furniture: 40,000 South Electronics: 70,000 South Furniture: 50,000 Query:

SELECT
    Region,
    Category,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Region, Category;

Result: North Electronics = 80,000, North Furniture = 40,000, South Electronics = 70,000, South Furniture = 50,000 This lets you analyze combinations of dimensions. 1️⃣5️⃣ GROUP BY vs PivotTable This is an important connection. In Excel: Region → Rows Sales → Values In SQL:

SELECT
    Region,
    SUM(Sales)
FROM Orders
GROUP BY Region;

The analytical concept is very similar. You're grouping records and calculating an aggregate. 1️⃣6️⃣ HAVING Now suppose you want:

"Show only regions where total sales are greater than ₹100,000."

You can't simply use WHERE on the aggregate result. You use: HAVING

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Region
HAVING SUM(Sales) > 100000;

Result: North = 120,000 1️⃣7️⃣ WHERE vs HAVING This is a very common SQL interview question. WHERE Filters individual rows before grouping. Example:

SELECT *
FROM Orders
WHERE Region = 'North';

HAVING Filters groups after aggregation. Example:

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Region
HAVING SUM(Sales) > 100000;

Remember:

WHERE → Filter rows HAVING → Filter groups

1️⃣8️⃣ WHERE + GROUP BY + HAVING You can use all three. Question:

Find regions where 2026 sales exceed ₹100,000.

Conceptually:

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
WHERE Order_Date >= '2026-01-01'
  AND Order_Date < '2027-01-01'
GROUP BY Region
HAVING SUM(Sales) > 100000;

The process is: WHERE → Filter rows GROUP BY → Create groups SUM → Calculate totals HAVING → Filter groups This sequence is fundamental to SQL analysis. 1️⃣9️⃣ ORDER BY with GROUP BY You can sort aggregated results. Suppose you want regions with the highest sales first:

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Region
ORDER BY Total_Sales DESC;

Result: North: 500,000, South: 350,000, West: 200,000, East: 150,000 2️⃣0️⃣ Top 3 Regions You can combine: GROUP BY + ORDER BY + LIMIT For example, in PostgreSQL/MySQL:

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Region
ORDER BY Total_Sales DESC
LIMIT 3;

This answers:

"Which three regions generated the most sales?"

2️⃣1️⃣ GROUP BY Dates Suppose you have: Order_Date and Sales You might want:

Total sales by year.

The exact date function varies by database system. For example, in PostgreSQL:

SELECT
    EXTRACT(YEAR FROM Order_Date) AS Sales_Year,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY EXTRACT(YEAR FROM Order_Date)
ORDER BY Sales_Year;

Result: 2024: 8,500,000, 2025: 10,200,000, 2026: 12,400,000 2️⃣2️⃣ Grouping by Month In PostgreSQL, you can use:

SELECT
    DATE_TRUNC('month', Order_Date) AS Sales_Month,
    SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY DATE_TRUNC('month', Order_Date)
ORDER BY Sales_Month;

This creates monthly sales totals. Different SQL platforms have different date functions, so always check the database you're working with. 2️⃣3️⃣ Calculate Average Order Value A common business KPI is: Average Order Value (AOV) A simple version is:

SELECT
    SUM(Sales) / COUNT(*) AS Average_Order_Value
FROM Orders;

If each row represents exactly one order. If the table can contain multiple rows per order, however, you need to calculate the denominator based on distinct orders:

SELECT
    SUM(Sales) / COUNT(DISTINCT Order_ID) AS Average_Order_Value
FROM Orders;

This distinction is extremely important. 2️⃣4️⃣ COUNT(DISTINCT) in Real Analytics Suppose a customer places multiple orders: Customer 101 → Orders 5001, 5002 Customer 102 → Order 5003 Customer 103 → Orders 5004, 5005 Total orders: 5 Unique customers: 3 Query:

SELECT COUNT(DISTINCT Customer_ID) AS Unique_Customers
FROM Orders;

Result: 3 This is commonly used for metrics such as: Active customers Unique users Unique accounts Distinct orders Distinct products 2️⃣5️⃣ Conditional Aggregation One powerful technique is combining CASE WHEN with aggregate functions. For example:

Count how many orders were above ₹50,000.

SELECT
    SUM(
        CASE
            WHEN Sales > 50000 THEN 1
            ELSE 0
        END
    ) AS High_Value_Orders
FROM Orders;

This allows you to create customized metrics. You'll use this technique much more in advanced SQL. 2️⃣6️⃣ Common SQL Analytical Pattern A very common query structure is:

SELECT
    Dimension,
    AGGREGATE_FUNCTION(Metric) AS KPI
FROM Table
WHERE Condition
GROUP BY Dimension
HAVING Aggregate_Condition
ORDER BY KPI DESC;

For example:

SELECT
    Region,
    SUM(Sales) AS Total_Sales
FROM Orders
WHERE Order_Date >= '2026-01-01'
GROUP BY Region
HAVING SUM(Sales) > 100000
ORDER BY Total_Sales DESC;

🚀 Data Analyst Roadmap — Part 12 🗄️ SQL — Level 2: Aggregate Functions, GROUP BY & HAVING Now that you've learned SQL fundamentals, it's time to move from retrieving individual records to summarizing data. This is one of the most important SQL skills for Data Analysts. In real interviews and jobs, you'll frequently be asked questions like:

What is the total sales by region? What is the average salary by department? How many customers are in each city? Which products generated more than ₹10 lakh in sales?

To answer these questions, you need: Aggregate Functions + GROUP BY + HAVING 1️⃣ What Are Aggregate Functions? Aggregate functions perform calculations across multiple rows and return a summarized result. The most important ones are: SUM() COUNT() AVG() MIN() MAX() Think of them as the SQL equivalent of the basic Excel functions you learned earlier. 2️⃣ SUM() SUM() calculates the total of a numeric column. Suppose you have: Order_ID: 1001, Sales: 50,000 Order_ID: 1002, Sales: 70,000 Order_ID: 1003, Sales: 30,000 Query:

SELECT SUM(Sales) AS Total_Sales
FROM Orders;

Result: Total_Sales = 150,000 Business question

What is our total revenue?

Answer → SUM() 3️⃣ COUNT() COUNT() counts records.

SELECT COUNT(*) AS Total_Orders
FROM Orders;

If there are 10,000 orders: Total_Orders = 10,000 Why COUNT(*)? COUNT(*) counts rows. This is often useful when you want the total number of records. 4️⃣ COUNT(Column) You can also count values in a specific column.

SELECT COUNT(Customer_ID) AS Customer_Count
FROM Orders;

One important distinction: COUNT(column) generally doesn't count NULL values. Whereas: COUNT(*) counts rows regardless of whether individual columns contain NULLs. 5️⃣ COUNT(DISTINCT) Suppose your Orders table contains: Order 1001 → Customer 101 Order 1002 → Customer 102 Order 1003 → Customer 101 Order 1004 → Customer 103 There are: 4 orders but only: 3 unique customers Use:

SELECT COUNT(DISTINCT Customer_ID) AS Unique_Customers
FROM Orders;

Result: 3 This is extremely important in analytics. 6️⃣ AVG() AVG() calculates the average. Suppose salaries are: 50,000, 60,000, 70,000 Query:

SELECT AVG(Salary) AS Average_Salary
FROM Employees;

Result: 60,000 Business questions

What is the average order value? What is the average employee salary? What is the average product price?

Answer → AVG() 7️⃣ MIN() MIN() returns the smallest value.

SELECT MIN(Salary) AS Minimum_Salary
FROM Employees;

Example result: 35,000 Useful for: Minimum salary Lowest sales Earliest date Lowest transaction value 8️⃣ MAX() MAX() returns the largest value.

SELECT MAX(Salary) AS Maximum_Salary
FROM Employees;

Result: 150,000 Useful for: Highest salary Highest sales Largest transaction Latest date 9️⃣ Using Multiple Aggregate Functions You can use several aggregate functions in one query.

SELECT
    SUM(Sales) AS Total_Sales,
    AVG(Sales) AS Average_Sales,
    MIN(Sales) AS Minimum_Sales,
    MAX(Sales) AS Maximum_Sales,
    COUNT(*) AS Total_Orders
FROM Orders;

SELECT * FROM Employees WHERE Department = 'IT' OR Department = 'Finance'; Both departments will be included. 1️⃣9️⃣ IN When checking multiple values, IN makes your query cleaner. Instead of: WHERE Department = 'IT' OR Department = 'Finance' OR Department = 'HR' you can write: WHERE Department IN ('IT', 'Finance', 'HR'); This is easier to read and maintain. 2️⃣0️⃣ NOT IN You can exclude multiple values. SELECT * FROM Employees WHERE Department NOT IN ('HR', 'Finance'); This returns employees who aren't in those departments. 2️⃣1️⃣ BETWEEN BETWEEN checks whether a value falls within a range. For example: SELECT * FROM Employees WHERE Salary BETWEEN 50000 AND 80000; This returns salaries within the specified range. For numeric data, this is often useful for: • Salary ranges • Sales ranges • Age ranges • Scores • Transaction values 2️⃣2️⃣ LIKE LIKE is used for pattern matching. Suppose you want employees whose names start with J. SELECT * FROM Employees WHERE Name LIKE 'J%'; % means:

Any number of characters.

So this could match: • John • James • Jennifer 2️⃣3️⃣ LIKE with Wildcards • Starts with J LIKE 'J%' • Ends with n LIKE '%n' • Contains "oh" LIKE '%oh%' Wildcards are extremely useful when searching text data. 2️⃣4️⃣ DISTINCT DISTINCT removes duplicate values from the result. Suppose your employee table contains: • IT • HR • IT • Finance • HR • IT Use: SELECT DISTINCT Department FROM Employees; Result: IT HR Finance This is useful for discovering categories in a dataset. 2️⃣5️⃣ ORDER BY ORDER BY sorts your results. Suppose you want employees with the highest salary first. SELECT * FROM Employees ORDER BY Salary DESC; DESC means: Descending Highest → Lowest 2️⃣6️⃣ ASC ASC means ascending. SELECT * FROM Employees ORDER BY Salary ASC; Lowest → Highest Ascending is generally the default sort direction. 2️⃣7️⃣ LIMIT / TOP The syntax depends on the database system. In systems such as PostgreSQL and MySQL: SELECT * FROM Employees ORDER BY Salary DESC LIMIT 5; This returns the top 5 employees by salary. In SQL Server, you would commonly use: SELECT TOP 5 * FROM Employees ORDER BY Salary DESC; This is an important point:

SQL is a language, but different database systems have slightly different syntax.

2️⃣8️⃣ Aliases Aliases give columns or tables temporary names within a query. For example: SELECT Name AS Employee_Name, Salary AS Annual_Salary FROM Employees; The result displays: Employee_Name Annual_Salary John 75,000 Sarah 60,000 Aliases make results easier to understand. 2️⃣9️⃣ SQL Comments You can add comments to explain your queries. For example: -- Get employees earning more than 70,000 SELECT Name, Salary FROM Employees WHERE Salary > 70000; Comments don't affect the query result. They're useful when queries become complex. 🧪 Practical Interview Challenge Suppose you have: Employees ID Name Department Salary 101 John IT 75,000 102 Sarah HR 60,000 103 Mike Finance 82,000 104 David IT 90,000 105 Alice HR 65,000 Q1. Retrieve all employees. SELECT * FROM Employees; Q2. Retrieve only names and salaries. SELECT Name, Salary FROM Employees; Q3. Find employees earning more than ₹70,000. SELECT * FROM Employees WHERE Salary > 70000; Q4. Find IT employees. SELECT * FROM Employees WHERE Department = 'IT'; Q5. Find IT or Finance employees. SELECT * FROM Employees WHERE Department IN ('IT', 'Finance'); Q6. Sort employees by salary from highest to lowest. SELECT * FROM Employees ORDER BY Salary DESC; Q7. Find the top 3 highest-paid employees. PostgreSQL/MySQL: SELECT * FROM Employees ORDER BY Salary DESC LIMIT 3; SQL Server: SELECT TOP 3 * FROM Employees ORDER BY Salary DESC; Q8. List unique departments. SELECT DISTINCT Department FROM Employees; 🏆 Double Tap ❤️ For More

This allows us to connect orders to customers. 8️⃣ Understanding Relationships The relationship is: Customers Customer_ID ↓ Orders One customer can have multiple orders. For example: John ↓ Order 5001 Order 5003 Order 5010 This is a: One-to-Many relationship It's one of the most important database concepts for Data Analysts. 9️⃣ What Is a Relational Database? A relational database stores data in related tables. For example: Customers ↓ Orders ↓ Order Details ↓ Products Instead of storing the customer's name repeatedly in every order, the database can store: Customer_ID and retrieve the customer information through relationships. This helps reduce unnecessary duplication. 🔟 What Is SQL Syntax? SQL queries generally consist of keywords and expressions. For example: SELECT * FROM Customers; This asks:

Return all columns from the Customers table.

Let me break it down. SELECT: Specifies what you want to retrieve. FROM: Specifies the table. Customers: The table you're querying. 1️⃣1️⃣ SELECT SELECT is one of the first SQL commands you need to learn. Suppose you have: Employees Employee_ID Name Department Salary 101 John IT 75,000 102 Sarah HR 60,000 103 Mike Finance 82,000 To retrieve all columns: SELECT * FROM Employees; 1️⃣2️⃣ Selecting Specific Columns You don't always need every column. Suppose you only want: Name and Department Use: SELECT Name, Department FROM Employees; Result: Name Department John IT Sarah HR Mike Finance This is generally better than using SELECT * when you only need specific fields. 1️⃣3️⃣ Why Avoid SELECT * in Production Queries? You may see beginners writing: SELECT * FROM Employees; all the time. It's useful while learning and exploring data. But in production queries, explicitly selecting the required columns is often better because: • It makes the query clearer • It avoids retrieving unnecessary data • It can reduce data transfer • It makes downstream dependencies more predictable For example: SELECT Employee_ID, Name, Salary FROM Employees; is more intentional. 1️⃣4️⃣ WHERE WHERE filters records. Suppose you want employees from IT. SELECT * FROM Employees WHERE Department = 'IT'; Result: Employee_ID Name Department Salary 101 John IT 75,000 The database only returns records satisfying the condition. 1️⃣5️⃣ Filtering Numeric Values Suppose you want employees earning more than ₹70,000. SELECT * FROM Employees WHERE Salary > 70000; Result: Employee_ID Name Department Salary 101 John IT 75,000 103 Mike Finance 82,000 1️⃣6️⃣ Comparison Operators You should know these operators: Operator Meaning = Equal to <> Not equal to

Greater than < Less than = Greater than or equal <= Less than or equal

Examples: WHERE Salary >= 80000 WHERE Department <> 'HR' 1️⃣7️⃣ AND AND requires all conditions to be true. Suppose you want: IT employees earning more than ₹70,000. SELECT * FROM Employees WHERE Department = 'IT' AND Salary > 70000; The record must satisfy both conditions. Think: IT AND Salary > 70,000 1️⃣8️⃣ OR OR requires at least one condition to be true. Suppose you want: IT or Finance employees.

🚀 Data Analyst Roadmap — Part 11 🗄️ SQL — Level 1: SQL Fundamentals & Databases You've completed the major Excel section of the roadmap. Now we're moving to one of the most important skills for a Data Analyst: SQL If Excel helps you analyze spreadsheet-based data, SQL helps you work directly with data stored in databases. A Data Analyst should be able to use SQL to: • Retrieve data • Filter records • Sort results • Summarize information • Join tables • Find trends • Calculate KPIs • Investigate business problems 1️⃣ What Is SQL? SQL stands for: Structured Query Language It's a language used to communicate with relational databases. For example, suppose a company stores millions of sales records in a database. Instead of opening a huge spreadsheet, you can ask the database:

"Give me all sales from the North region."

Or:

"What was total revenue last month?"

Or:

"Which 10 products generated the most revenue?"

SQL allows you to ask these questions directly. 2️⃣ Why Is SQL Important for Data Analysts? Imagine a company has: 50 million transactions. Excel isn't the right tool for storing and querying all that information. The data may be stored in a database such as: • PostgreSQL • MySQL • Microsoft SQL Server • Oracle Database • Snowflake • BigQuery As a Data Analyst, you may connect to the database and use SQL to extract the data you need. A typical workflow looks like: Database ↓ SQL Query ↓ Required Data ↓ Analysis ↓ Dashboard / Report ↓ Business Decision 3️⃣ What Is a Database? A database is a system used to store and manage data. For example, an e-commerce company might have: • Customers • Products • Orders • Payments • Employees Each represents a different type of information. Instead of putting everything into one enormous table, relational databases typically organize related information into separate tables. 4️⃣ What Is a Table? A table is a structured collection of data organized into: Rows + Columns For example: Customers Customer_ID Customer_Name City 101 John Mumbai 102 Sarah Pune 103 Mike Delhi Each row represents one customer. Each column represents an attribute. This should look familiar from Excel. 5️⃣ Rows vs Columns Just like Excel: Row Represents a record. Example: 101 | John | Mumbai represents one customer. Column Represents an attribute. For example: • Customer_ID • Customer_Name • City A useful rule:

One row = one record One column = one attribute

6️⃣ What Is a Primary Key? A Primary Key uniquely identifies each record in a table. For example: Customer_ID Customer_Name 101 John 102 Sarah 103 Mike Here: Customer_ID can be the primary key. Each customer should have a unique ID. 101 → John 102 → Sarah 103 → Mike You shouldn't have two different customers with the same primary key. 7️⃣ What Is a Foreign Key? A Foreign Key is a column used to establish a relationship between tables. Suppose: Customers Customer_ID Customer_Name 101 John 102 Sarah Orders Order_ID Customer_ID Sales 5001 101 50,000 5002 102 70,000 5003 101 30,000 Here: Customers.Customer_ID is the primary key. Orders.Customer_ID can be a foreign key.

SELECT * FROM Employees WHERE Department = 'IT' OR Department = 'Finance'; Both departments will be included. 1️⃣9️⃣ IN When checking multiple values, IN makes your query cleaner. Instead of: WHERE Department = 'IT' OR Department = 'Finance' OR Department = 'HR' you can write: WHERE Department IN ('IT', 'Finance', 'HR'); This is easier to read and maintain. 2️⃣0️⃣ NOT IN You can exclude multiple values. SELECT * FROM Employees WHERE Department NOT IN ('HR', 'Finance'); This returns employees who aren't in those departments. 2️⃣1️⃣ BETWEEN BETWEEN checks whether a value falls within a range. For example: SELECT * FROM Employees WHERE Salary BETWEEN 50000 AND 80000; This returns salaries within the specified range. For numeric data, this is often useful for: • Salary ranges • Sales ranges • Age ranges • Scores • Transaction values 2️⃣2️⃣ LIKE LIKE is used for pattern matching. Suppose you want employees whose names start with J. SELECT * FROM Employees WHERE Name LIKE 'J%'; % means:

Any number of characters.

So this could match: • John • James • Jennifer 2️⃣3️⃣ LIKE with Wildcards • Starts with J LIKE 'J%' • Ends with n LIKE '%n' • Contains "oh" LIKE '%oh%' Wildcards are extremely useful when searching text data. 2️⃣4️⃣ DISTINCT DISTINCT removes duplicate values from the result. Suppose your employee table contains: • IT • HR • IT • Finance • HR • IT Use: SELECT DISTINCT Department FROM Employees; Result: IT HR Finance This is useful for discovering categories in a dataset. 2️⃣5️⃣ ORDER BY ORDER BY sorts your results. Suppose you want employees with the highest salary first. SELECT * FROM Employees ORDER BY Salary DESC; DESC means: Descending Highest → Lowest 2️⃣6️⃣ ASC ASC means ascending. SELECT * FROM Employees ORDER BY Salary ASC; Lowest → Highest Ascending is generally the default sort direction. 2️⃣7️⃣ LIMIT / TOP The syntax depends on the database system. In systems such as PostgreSQL and MySQL: SELECT * FROM Employees ORDER BY Salary DESC LIMIT 5; This returns the top 5 employees by salary. In SQL Server, you would commonly use: SELECT TOP 5 * FROM Employees ORDER BY Salary DESC; This is an important point:

SQL is a language, but different database systems have slightly different syntax.

2️⃣8️⃣ Aliases Aliases give columns or tables temporary names within a query. For example: SELECT Name AS Employee_Name, Salary AS Annual_Salary FROM Employees; The result displays: Employee_Name Annual_Salary John 75,000 Sarah 60,000 Aliases make results easier to understand. 2️⃣9️⃣ SQL Comments You can add comments to explain your queries. For example: -- Get employees earning more than 70,000 SELECT Name, Salary FROM Employees WHERE Salary > 70000; Comments don't affect the query result. They're useful when queries become complex. 🧪 Practical Interview Challenge Suppose you have: Employees ID Name Department Salary 101 John IT 75,000 102 Sarah HR 60,000 103 Mike Finance 82,000 104 David IT 90,000 105 Alice HR 65,000 Q1. Retrieve all employees. SELECT * FROM Employees; Q2. Retrieve only names and salaries. SELECT Name, Salary FROM Employees; Q3. Find employees earning more than ₹70,000. SELECT * FROM Employees WHERE Salary > 70000; Q4. Find IT employees. SELECT * FROM Employees WHERE Department = 'IT'; Q5. Find IT or Finance employees. SELECT * FROM Employees WHERE Department IN ('IT', 'Finance'); Q6. Sort employees by salary from highest to lowest. SELECT * FROM Employees ORDER BY Salary DESC; Q7. Find the top 3 highest-paid employees. PostgreSQL/MySQL: SELECT * FROM Employees ORDER BY Salary DESC LIMIT 3; SQL Server: SELECT TOP 3 * FROM Employees ORDER BY Salary DESC; Q8. List unique departments. SELECT DISTINCT Department FROM Employees; 🏆 Double Tap ❤️ For More ----- 1.59 ₽ · /balance_help

This allows us to connect orders to customers. 8️⃣ Understanding Relationships The relationship is: Customers Customer_ID ↓ Orders One customer can have multiple orders. For example: John ↓ Order 5001 Order 5003 Order 5010 This is a: One-to-Many relationship It's one of the most important database concepts for Data Analysts. 9️⃣ What Is a Relational Database? A relational database stores data in related tables. For example: Customers ↓ Orders ↓ Order Details ↓ Products Instead of storing the customer's name repeatedly in every order, the database can store: Customer_ID and retrieve the customer information through relationships. This helps reduce unnecessary duplication. 🔟 What Is SQL Syntax? SQL queries generally consist of keywords and expressions. For example: SELECT * FROM Customers; This asks:

Return all columns from the Customers table.

Let me break it down. SELECT: Specifies what you want to retrieve. FROM: Specifies the table. Customers: The table you're querying. 1️⃣1️⃣ SELECT SELECT is one of the first SQL commands you need to learn. Suppose you have: Employees Employee_ID Name Department Salary 101 John IT 75,000 102 Sarah HR 60,000 103 Mike Finance 82,000 To retrieve all columns: SELECT * FROM Employees; 1️⃣2️⃣ Selecting Specific Columns You don't always need every column. Suppose you only want: Name and Department Use: SELECT Name, Department FROM Employees; Result: Name Department John IT Sarah HR Mike Finance This is generally better than using SELECT * when you only need specific fields. 1️⃣3️⃣ Why Avoid SELECT * in Production Queries? You may see beginners writing: SELECT * FROM Employees; all the time. It's useful while learning and exploring data. But in production queries, explicitly selecting the required columns is often better because: • It makes the query clearer • It avoids retrieving unnecessary data • It can reduce data transfer • It makes downstream dependencies more predictable For example: SELECT Employee_ID, Name, Salary FROM Employees; is more intentional. 1️⃣4️⃣ WHERE WHERE filters records. Suppose you want employees from IT. SELECT * FROM Employees WHERE Department = 'IT'; Result: Employee_ID Name Department Salary 101 John IT 75,000 The database only returns records satisfying the condition. 1️⃣5️⃣ Filtering Numeric Values Suppose you want employees earning more than ₹70,000. SELECT * FROM Employees WHERE Salary > 70000; Result: Employee_ID Name Department Salary 101 John IT 75,000 103 Mike Finance 82,000 1️⃣6️⃣ Comparison Operators You should know these operators: Operator Meaning = Equal to <> Not equal to

Greater than < Less than = Greater than or equal <= Less than or equal

Examples: WHERE Salary >= 80000 WHERE Department <> 'HR' 1️⃣7️⃣ AND AND requires all conditions to be true. Suppose you want: IT employees earning more than ₹70,000. SELECT * FROM Employees WHERE Department = 'IT' AND Salary > 70000; The record must satisfy both conditions. Think: IT AND Salary > 70,000 1️⃣8️⃣ OR OR requires at least one condition to be true. Suppose you want: IT or Finance employees.

🚀 Data Analyst Roadmap — Part 12 🗄️ SQL — Level 1: SQL Fundamentals & Databases You've completed the major Excel section of the roadmap. Now we're moving to one of the most important skills for a Data Analyst: SQL If Excel helps you analyze spreadsheet-based data, SQL helps you work directly with data stored in databases. A Data Analyst should be able to use SQL to: • Retrieve data • Filter records • Sort results • Summarize information • Join tables • Find trends • Calculate KPIs • Investigate business problems 1️⃣ What Is SQL? SQL stands for: Structured Query Language It's a language used to communicate with relational databases. For example, suppose a company stores millions of sales records in a database. Instead of opening a huge spreadsheet, you can ask the database:

"Give me all sales from the North region."

Or:

"What was total revenue last month?"

Or:

"Which 10 products generated the most revenue?"

SQL allows you to ask these questions directly. 2️⃣ Why Is SQL Important for Data Analysts? Imagine a company has: 50 million transactions. Excel isn't the right tool for storing and querying all that information. The data may be stored in a database such as: • PostgreSQL • MySQL • Microsoft SQL Server • Oracle Database • Snowflake • BigQuery As a Data Analyst, you may connect to the database and use SQL to extract the data you need. A typical workflow looks like: Database ↓ SQL Query ↓ Required Data ↓ Analysis ↓ Dashboard / Report ↓ Business Decision 3️⃣ What Is a Database? A database is a system used to store and manage data. For example, an e-commerce company might have: • Customers • Products • Orders • Payments • Employees Each represents a different type of information. Instead of putting everything into one enormous table, relational databases typically organize related information into separate tables. 4️⃣ What Is a Table? A table is a structured collection of data organized into: Rows + Columns For example: Customers Customer_ID Customer_Name City 101 John Mumbai 102 Sarah Pune 103 Mike Delhi Each row represents one customer. Each column represents an attribute. This should look familiar from Excel. 5️⃣ Rows vs Columns Just like Excel: Row Represents a record. Example: 101 | John | Mumbai represents one customer. Column Represents an attribute. For example: • Customer_ID • Customer_Name • City A useful rule:

One row = one record One column = one attribute

6️⃣ What Is a Primary Key? A Primary Key uniquely identifies each record in a table. For example: Customer_ID Customer_Name 101 John 102 Sarah 103 Mike Here: Customer_ID can be the primary key. Each customer should have a unique ID. 101 → John 102 → Sarah 103 → Mike You shouldn't have two different customers with the same primary key. 7️⃣ What Is a Foreign Key? A Foreign Key is a column used to establish a relationship between tables. Suppose: Customers Customer_ID Customer_Name 101 John 102 Sarah Orders Order_ID Customer_ID Sales 5001 101 50,000 5002 102 70,000 5003 101 30,000 Here: Customers.Customer_ID is the primary key. Orders.Customer_ID can be a foreign key.

A missing salary doesn't mean Salary = 0, it means it wasn't provided. 1️⃣4️⃣ Replace Values Standardize inconsistent entries: North, NORTH, north, N → North Use: Replace Values 1️⃣5️⃣ Trim and Clean Text Transform " John Smith " → "John Smith" Trim whitespace, Clean non-printing characters, Change case 1️⃣6️⃣ Split Columns John-Smith → First Name: John, Last Name: Smith Use: Split Column → By Delimiter → "-" 1️⃣7️⃣ Merge Columns John + Smith → John Smith Use: Merge Columns with space separator 2️⃣0️⃣ Add Custom Columns Sales: 100,000, Cost: 70,000 → Profit = Sales - Cost = 30,000 Profit Margin = Profit / Sales 2️⃣1️⃣ Conditional Columns IF Sales >= 100000 THEN "High" ELSE IF Sales >= 50000 THEN "Medium" ELSE "Low" Similar to Excel's IF() 2️⃣2️⃣ Merge Queries (The most important concept) Sales: Product ID, Sales Products: Product ID, Product, Category Use: Merge Queries → Match Product ID → This is like a JOIN in SQL. SQL: SELECT * FROM Sales LEFT JOIN Products ON Sales.ProductID = Products.ProductID; 2️⃣3️⃣ Append Queries Merge = Add columns by matching keys Append = Add rows by stacking Jan (1001, 1002) + Feb (1003, 1004) → 1001, 1002, 1003, 1004 2️⃣4️⃣ Group By Region: North 50K, North 70K → Group by Region, Sum Sales → North 120K Similar to SQL GROUP BY 2️⃣5️⃣ Pivot and Unpivot This is critical for reports designed for humans: Before: Region | Jan | Feb | Mar North | 50K | 60K | 70K After Unpivot: Region | Month | Sales North | Jan | 50K North | Feb | 60K This structure is much better for analysis. 2️⃣6️⃣ Applied Steps = Your Superpower Source → Changed Type → Removed Columns → Trimmed Text → Removed Duplicates → Filtered Rows → Added Profit → Merged Products When new data arrives, just Refresh. 🧪 Practical Interview Challenge Messy file with: Duplicate Order IDs, Extra spaces, Sales as text, Missing regions, Product info in another file Strong approach: 1. Import into Power Query 2. Set correct data types 3. Trim and clean text 4. Investigate duplicates 5. Handle missing regions per business rules 6. Merge Product lookup table 7. Add Profit column 8. Filter invalid records 9. Review Applied Steps 10. Load cleaned dataset 🏆 Key Lesson Instead of: > "How do I clean this file?" Think: > "How do I build a repeatable process that cleans this type of data every time?" That's the difference between manually manipulating spreadsheets and building a professional analytics workflow. Remember: Merge = Add columns by matching data Append = Add rows Group By = Summarize Unpivot = Convert columns into rows Applied Steps = Record your process Refresh = Run the process again Double Tap ❤️ For Part-11 ----- 1.46 ₽ · /balance_help

🚀 Data Analyst Roadmap — Part 10 🧹 Excel — Level 9: Power Query for Data Cleaning & Transformation So far, you've learned how to analyze data using Excel formulas and PivotTables. But there's a major problem with real-world data:

The data is often messy.

You might receive a monthly Excel file with: • Duplicate records • Missing values • Incorrect data types • Extra spaces • Inconsistent names • Multiple files • Unnecessary columns • Data spread across different tables Cleaning this manually every time is slow and error-prone. That's where Power Query comes in. 1️⃣ What Is Power Query? Power Query is a data preparation and transformation tool available in Excel and Power BI. It allows you to: Connect → Extract → Transform → Load - This is commonly called ETL. Extract: Get data from a source. Transform: Clean and reshape the data. Load: Bring the prepared data into Excel for analysis. The biggest advantage is repeatability. Instead of cleaning the same file manually every month, you create a transformation process once and refresh it. 2️⃣ Why Should a Data Analyst Learn Power Query? Imagine your company sends you this file every month: January.xlsx, February.xlsx, March.xlsx, April.xlsx... Every file contains 50,000 rows, extra spaces, duplicates, incorrect date formats. Without Power Query, you repeat the same cleaning every month. With Power Query: Refresh → Transformations run again 3️⃣ Where Do You Find Power Query? In modern Excel: Data → Get & Transform Data Options: From Table/Range, From Workbook, From Text/CSV, From Folder, From Web, From Database 4️⃣ Understand the Power Query Workflow Data Source → Connect → Power Query Editor → Clean → Transform → Validate → Load → Excel / Data Model → Analysis Power Query records the transformation steps. 5️⃣ Import Data from Excel & CSV Excel: Data → Get Data → From File → From Excel Workbook → Select sheet → Open in Power Query Editor CSV: Data → From Text/CSV → Preview delimiter, headers, data types → Transform Data 6️⃣ Power Query Editor Left side: Queries Middle: Data preview Right side: Applied Steps Example Applied Steps: Source → Changed Type → Removed Columns → Filtered Rows → Removed Duplicates → Renamed Columns → Added Custom Column 7️⃣ Changing Data Types Correct data types are critical. Order ID → Whole Number, Order Date → Date, Sales → Decimal Number, Customer → Text Use the data-type icon to change it. 8️⃣ Remove Duplicates If Order ID should be unique, select the column and use: Remove Rows → Remove Duplicates 🔟 Important: Understand What a Duplicate Means Don't automatically delete duplicates. Ask: > Is this actually a duplicate? Two records with same customer but different orders = Not a duplicate. Same order appearing twice = Duplicate. 1️⃣1️⃣ Remove & Rename Columns Remove unnecessary columns: Home → Remove Columns Rename for clarity: CustNm → Customer Name, SlsAmt → Sales 1️⃣2️⃣ Filter Rows Filtering in Power Query becomes part of the reusable query. Example: Keep only orders from 2026, or North region, or Sales > 0 1️⃣3️⃣ Handle Missing Values Never blindly replace missing values with zero.

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Add slicer for Region → Clickable North/South/East/West → PivotTable updates. Easier for non-technical users. 1️⃣7️⃣ Multiple Slicers Add Region, Category, Year slicers → User selects Region: North, Category: Electronics, Year: 2026 → Shows only relevant info. Foundation of interactive dashboard. 1️⃣8️⃣ PivotCharts A chart connected to a PivotTable. 📈 Line Chart for sales by month, 📊 Column Chart for sales by region. Automatically responds to filters and slicers. 1️⃣9️⃣ Choosing the Right Chart Compare categories → Bar/Column Chart Show trends over time → Line Chart Show contribution → Bar or Pie/Donut for small categories Analyze relationships → Scatter Plot 2️⃣0️⃣ Drill Down Year → Quarter → Month → Day. Move from high-level view to detailed view. 2️⃣1️⃣ Drill Through to Source Data Double-click a value to see underlying records contributing to that value. Useful for investigating unexpected numbers. 2️⃣2️⃣ Refreshing PivotTables PivotTables don't auto-update. Right-click → Refresh or Data → Refresh All. Using Excel Table as source makes refresh easier. 2️⃣3️⃣ PivotTable Best Practice Source data should have: ✅ Headers ✅ No blank rows ✅ Consistent data types ✅ One record per row ✅ One field per column ✅ No manually inserted totals. 🧪 Practical Interview Challenge Q1. Total sales by region → Region → Rows, Sales → Values Q2. Average profit by category → Category → Rows, Profit → Values → Average Q3. Monthly sales trend → Order Date → Rows, Sales → Values, Group by Months Q4. Top 10 products by sales → Product → Rows, Sales → Values, Value Filters → Top 10 Q5. Interactive regional report → PivotTable + PivotChart + Region Slicer 🎯 Mini Project: Build an Excel Sales Analysis Dashboard KPIs: Total Sales, Total Profit, Total Orders, Average Order Value Analysis: 📊 Sales by Region, 📈 Monthly Trend, 📊 Sales by Category, 🏆 Top 10 Products, 📊 Profit by Region Interactive Controls: Slicers for Region, Category, Year Double Tap ❤️ For Part-10 ----- 1.44 ₽ · /balance_help

🚀 Data Analyst Roadmap — Part 9 📊 Excel — Level 8: PivotTables, PivotCharts & Interactive Analysis Now that you understand Excel formulas and dynamic functions, it's time to learn one of the most important Excel features for Data Analysts: PivotTables. A PivotTable allows you to take a large dataset and quickly summarize it without writing complicated formulas. For example, imagine you have 50,000 sales transactions. Your manager asks: "Show me total sales by region, product category, and month." Doing this manually would take a lot of time. With a PivotTable, you can summarize the data in seconds. 1️⃣ What Is a PivotTable? A PivotTable is an Excel tool that lets you summarize, group, compare and analyze large datasets. Raw data example: Order ID | Date | Region | Product | Sales | Profit 1001 | Jan | North | Laptop | 80,000 | 12,000 1002 | Jan | South | Mouse | 2,000 | 500 Instead of manually calculating totals, create a PivotTable. 2️⃣ Creating a PivotTable First select your dataset. Then: Insert → PivotTable → Usually select New Worksheet → OK. You'll see four main areas: Rows, Columns, Values, Filters. These four areas are the foundation. 3️⃣ Understand the Rows Area Rows determines what you want to group by. Drag Region → Rows → You get North, South, West grouped. 4️⃣ Understand the Values Area Values contains the calculation. Drag Sales → Values → Sum of Sales. Region | Total Sales → North 155,000, South 92,000, West 5,000. Now you've answered: "How much did each region sell?" 5️⃣ Understand the Columns Area Allows you to compare categories horizontally. Region → Rows, Product → Columns, Sales → Values → You get Region x Product matrix. 6️⃣ Understand the Filters Area Lets you filter entire PivotTable. Region → Rows, Sales → Values, Year → Filters → Select 2026 to see only 2026 results. 7️⃣ The Four PivotTable Areas Rows → What do I want to group by? Columns → What do I want to compare across? Values → What calculation do I want? Filters → What do I want to filter? 8️⃣ Change the Calculation Right-click value → Value Field Settings → Choose Sum, Count, Average, Max, Min, etc. e.g., "What is average sales per order?" → Change to Average. 9️⃣ Sum vs Count in PivotTables Sum of Sales = 100,000, Count = 3, Average = 33,333.33. Always make sure aggregation matches business question. 🔟 Show Values as % of Total Right-click Sales values → Show Values As → % of Grand Total → North 50%, South 30%, West 20%. Useful for contribution analysis. 1️⃣1️⃣ Group Dates in PivotTables Right-click a date → Group → Years, Quarters, Months, Days. Makes time-based analysis easier. 1️⃣2️⃣ Analyze Monthly Sales Order Date → Rows, Sales → Values, Group by Months → Jan 120K, Feb 145K, Mar 170K etc. 1️⃣3️⃣ Analyze Sales by Region and Month Rows → Region, Columns → Month, Values → Sales → Matrix to identify best/worst region and trends. 1️⃣4️⃣ Sorting PivotTable Results Sort Largest → Smallest to make best performers stand out. 1️⃣5️⃣ Top 10 Analysis Use Value Filters → Top 10 to show top 10 customers/products/regions. 1️⃣6️⃣ Slicers Slicers make PivotTables interactive.

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For example: "25/08/2026" may be stored as text. Then functions such as: =YEAR(A2) may not work as expected. You need to ensure the value is converted into a genuine Excel date before performing calculations. This is a crucial data-cleaning concept. 🧪 Practical Interview Challenge Suppose you have: Employee: John, Joining Date: 15-Jan-2022, End Date: 25-Aug-2026 Sarah, 20-Mar-2021, 25-Aug-2026 Mike, 10-Jul-2023, 25-Aug-2026 Q1. Extract the joining year: =YEAR(B2) Q2. Extract the joining month: =MONTH(B2) Q3. Calculate completed years: =DATEDIF(B2,C2,"Y") Q4. Calculate total days: =C2-B2 Q5. Find month-end for joining month: =EOMONTH(B2,0) Q6. Find six months after joining: =EDATE(B2,6) Q7. Calculate working days: =NETWORKDAYS(B2,C2) 🏆 Key Lesson Dates aren't just values displayed on a spreadsheet. They allow you to analyze time. A Data Analyst should be able to answer: When did it happen? How long did it take? How many working days did it take? Which month did it happen in? Which quarter/year did it happen in? Is it overdue? When will it be due? Once you become comfortable with date functions, you'll be able to build much more useful analysis around trends, aging, SLAs, employee tenure, financial periods and time-based KPIs. Double Tap ❤️ For Part-8 ----- 1.48 ₽ · /balance_help

Six months later: =EDATE(A2,6) Result: 25-Feb-2027 Three months earlier: =EDATE(A2,-3) Result: 25-May-2026 Common uses: Contract expiry, Subscription dates, Loan schedules, Review dates, Employee milestones 1️⃣2️⃣ Date Subtraction One of the simplest but most useful date calculations is: =B2-A2 Suppose: Start Date: 01-Aug-2026, End Date: 10-Aug-2026 - Formula: =B2-A2 Result: 9 days This is useful for calculating: Delivery time, Processing time, Turnaround time, Resolution time, Payment delays 1️⃣3️⃣ Calculate Days Overdue Suppose: Due Date: 20-Aug-2026 You want to know how many days overdue the payment is. You could use: =MAX(0,TODAY()-A2) If today is after the due date, Excel calculates the overdue days. If the payment isn't overdue, it returns: 0 This is useful for invoice and payment analysis. 1️⃣4️⃣ DATEDIF() DATEDIF() calculates the difference between two dates in different units. For example: =DATEDIF(A2,B2,"Y") returns the number of complete years. DATEDIF Units "Y" - Complete years. =DATEDIF(A2,B2,"Y") "M" - Complete months. =DATEDIF(A2,B2,"M") "D" - Total days. =DATEDIF(A2,B2,"D") 1️⃣5️⃣ Employee Tenure Example Suppose: Employee: John, Joining Date: 15-Jan-2022 To calculate completed years as of today: =DATEDIF(B2,TODAY(),"Y") If today is after January 15, 2026, the result would be: 4 years This is commonly used in HR analytics. 1️⃣6️⃣ Calculate Years and Months Together You can combine DATEDIF calculations. =DATEDIF(B2,TODAY(),"Y")&" Years "&DATEDIF(B2,TODAY(),"YM")&" Months" Example result: 4 Years 7 Months - This can be useful in employee reports. 1️⃣7️⃣ NETWORKDAYS() NETWORKDAYS() calculates the number of working days between two dates. It normally excludes: Saturday, Sunday Example: =NETWORKDAYS(A2,B2) This is very useful for: SLA analysis, Employee working days, Project duration, Processing time, Operational reporting 1️⃣8️⃣ NETWORKDAYS() with Holidays Suppose your company holidays are listed in: H2:H10 You can use: =NETWORKDAYS(A2,B2,H2:H10) Now Excel excludes: Weekends, Listed holidays This is extremely useful for real-world business calculations. 1️⃣9️⃣ WORKDAY() WORKDAY() calculates a future or previous working date. Suppose a task starts on: 25-Aug-2026 and should take: 10 working days - Use: =WORKDAY(A2,10) Excel returns the date after 10 working days, excluding weekends. You can also provide holidays: =WORKDAY(A2,10,H2:H10) 2️⃣0️⃣ MONTH-END Reporting Example Suppose you're preparing a monthly sales report. You have: Order Date, Sales - You need to identify the month-end date for every transaction. Use: =EOMONTH(A2,0) You can then use that month-end field for reporting and grouping. 2️⃣1️⃣ Extract Month Name MONTH() gives you a number. But sometimes you want: January instead of: 1 You can use: =TEXT(A2,"mmmm") Result: January For abbreviated month: =TEXT(A2,"mmm") Result: Jan 2️⃣2️⃣ Extract Year-Month For reporting, you may want: 2026-08 - You can use: =TEXT(A2,"yyyy-mm") This is useful for: Monthly trends, Grouping, Reporting, Time-series analysis 2️⃣3️⃣ Important Date Problem: Dates Stored as Text One common real-world problem is that something that looks like a date isn't actually stored as a date.