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🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 4 — LIMIT

LIMIT is used to control how many rows a SQL query returns.

It is especially useful when you want to see only the first few records, such as:

• Top 5 highest salaries

• Top 10 products by sales

• First 20 customers

• Latest 10 transactions

🔹 Basic Syntax

SELECT *
FROM employees
LIMIT 5;

This returns only 5 rows from the result.

🔹 LIMIT with ORDER BY

This is where LIMIT becomes especially powerful.

Suppose you want the 5 highest-paid employees:

SELECT employee_name, salary
FROM employees
ORDER BY salary DESC
LIMIT 5;

The logical idea is:

Filter → Sort → Take required number of rows

For example:

Salary
120000
110000
95000
90000
85000

Only the top 5 rows are returned.

🔹 LIMIT Without ORDER BY

You can write:

SELECT *
FROM employees
LIMIT 5;

But without ORDER BY, you should not assume these are the "first" or "top" 5 meaningful records.

The database does not guarantee a particular order unless you explicitly specify one.

So for analysis, this is usually better:

SELECT *
FROM employees
ORDER BY salary DESC
LIMIT 5;

🔹 LIMIT with WHERE

You can combine WHERE, ORDER BY, and LIMIT.

Example:

SELECT employee_name, salary
FROM employees
WHERE department = 'IT'
ORDER BY salary DESC
LIMIT 3;

This finds the 3 highest-paid employees in the IT department.

🔹 LIMIT for Top-N Analysis

Top-N queries are extremely common in data analysis.

Top 10 products by sales

SELECT product_name, sales
FROM products
ORDER BY sales DESC
LIMIT 10;

5 most recent transactions

SELECT transaction_id, transaction_date, amount
FROM transactions
ORDER BY transaction_date DESC
LIMIT 5;

3 cheapest products

SELECT product_name, price
FROM products
ORDER BY price ASC
LIMIT 3;

🔹 LIMIT with OFFSET

OFFSET allows you to skip a certain number of rows before returning results.

Example:

SELECT *
FROM employees
LIMIT 5 OFFSET 10;

This means:

Skip 10 rows

↓

Return the next 5 rows

This is commonly used for pagination.

For example:

• Page 1 → LIMIT 10 OFFSET 0

• Page 2 → LIMIT 10 OFFSET 10

• Page 3 → LIMIT 10 OFFSET 20

Syntax can vary between SQL databases, so always check the specific database you are using.

🔹 LIMIT vs TOP vs FETCH

Different SQL databases may use different syntax.

MySQL/PostgreSQL/SQLite commonly use:

LIMIT 5;

SQL Server commonly uses:

SELECT TOP 5 *
FROM employees;

Standard SQL also supports:

FETCH FIRST 5 ROWS ONLY;

The concept is the same: restrict the number of returned rows.

🔹 Real-World Data Science Example

Suppose an analyst wants to investigate the 10 largest customer transactions.

SELECT customer_id,
       transaction_id,
       amount
FROM transactions
ORDER BY amount DESC
LIMIT 10;

This can help identify:

• High-value transactions

• Potential anomalies

• Important customers

• Transactions requiring further investigation

🔹 Common Mistakes

Mistake 1 — Using LIMIT without sorting

SELECT *
FROM products
LIMIT 10;

This does not necessarily mean "10 best products."

For that:

ORDER BY sales DESC
LIMIT 10;

Mistake 2 — Using ASC when you need the highest values

Wrong:

ORDER BY salary ASC
LIMIT 5;

This returns the lowest salaries.

For the highest salaries:

ORDER BY salary DESC
LIMIT 5;

Mistake 3 — Forgetting that LIMIT applies after sorting

For a top-N analysis, think:

ORDER BY → LIMIT

not simply:

LIMIT → assume top records

🔹 Interview Questions

1. What is LIMIT?

LIMIT restricts the number of rows returned by a query.

2. How do you find the top 10 salaries?

SELECT *
FROM employees
ORDER BY salary DESC
LIMIT 10;

3. Can LIMIT be used with WHERE?

Yes.

SELECT *
FROM employees
WHERE department = 'Finance'
LIMIT 5;

4. Why is ORDER BY important when using LIMIT?

Because without sorting, the returned rows are not necessarily the highest, lowest, newest, or oldest records you want.

5. What is OFFSET used for?

OFFSET skips a specified number of rows before returning the requested rows, commonly for pagination.

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🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 6 — GROUP BY

GROUP BY is used to group rows that have the same value in one or more columns.

It is mainly used when you want to perform calculations for each group.

For example:

• Total sales by region

• Average salary by department

• Number of customers by city

• Total orders by product

• Maximum transaction by category

🔹 Basic Syntax

SELECT column_name, aggregate_function(column_name)
FROM table_name
GROUP BY column_name;

Common aggregate functions:

• COUNT() → Count rows

• SUM() → Calculate total

• AVG() → Calculate average

• MIN() → Find minimum

• MAX() → Find maximum

🔹 Simple Example

Suppose we have:

department   salary
IT           60000
IT           80000
HR           50000
HR           70000
Finance      90000

Query:

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department;

Result:

department   avg_salary
IT           70000
HR           60000
Finance      90000

Instead of calculating one overall average, SQL calculates an average for each department.

🔹 GROUP BY with COUNT()

To count employees in each department:

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

Result:

department   employee_count
IT           2
HR           2
Finance      1

🔹 GROUP BY with SUM()

Suppose you have sales data:

region   sales
North    10000
North    15000
South    20000
South    12000

Query:

SELECT region, SUM(sales) AS total_sales
FROM sales
GROUP BY region;

Result:

North   25000
South   32000

This is extremely common in business analytics.

🔹 GROUP BY with MAX()

Find the highest salary in each department:

SELECT department, MAX(salary) AS highest_salary
FROM employees
GROUP BY department;

🔹 GROUP BY with MIN()

Find the lowest salary in each department:

SELECT department, MIN(salary) AS lowest_salary
FROM employees
GROUP BY department;

🔹 GROUP BY with Multiple Columns

You can group by multiple columns.

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

Here SQL creates groups based on:

department + job_title

For example:

IT        Analyst       5
IT        Developer     8
HR        Analyst       3
Finance   Manager       4

🔹 GROUP BY with WHERE

WHERE filters rows before grouping.

SELECT department, AVG(salary) AS avg_salary
FROM employees
WHERE salary > 50000
GROUP BY department;

🔹 GROUP BY with ORDER BY

You can sort grouped results.

For example, find departments with the highest total salary first:

SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department
ORDER BY total_salary DESC;

This is very useful for ranking groups.

🔹 GROUP BY vs DISTINCT

These two can sometimes look similar.

DISTINCT

SELECT DISTINCT department
FROM employees;

Used to find unique departments.

GROUP BY

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

Used when you want to perform calculations for each department.

Simple rule:

DISTINCT → Give me unique values

GROUP BY → Give me groups so I can calculate something

🔹 Real-World Data Science Example

Imagine an e-commerce dataset.

You want to know the total revenue generated by each product category.

SELECT category,
       SUM(quantity * price) AS total_revenue
FROM sales
GROUP BY category
ORDER BY total_revenue DESC;

This gives you a category-level summary that can be used for:

• Revenue analysis

• Product performance

• Business reporting

• Feature creation

• Exploratory data analysis

🔹 Common Mistake

Consider:

SELECT department, employee_name, AVG(salary)
FROM employees
GROUP BY department;

This can cause an error in many SQL databases because employee_name is neither:

• included in GROUP BY, nor

• used inside an aggregate function.

A valid query would be:

SELECT department, AVG(salary)
FROM employees
GROUP BY department;

🔹 Important Rule

When using GROUP BY, selected columns generally need to be either:

1. Included in the GROUP BY, or

2. Wrapped inside an aggregate function.

Example:

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

department → grouped column

COUNT(*) → aggregate

🔹 Interview Questions

1. What is GROUP BY?

GROUP BY groups rows with the same values so aggregate calculations can be performed for each group.

2. Which aggregate functions are commonly used with GROUP BY?

COUNT(), SUM(), AVG(), MIN(), and MAX().

3. What is the difference between WHERE and GROUP BY?

WHERE filters individual rows before grouping.

GROUP BY creates groups from the filtered rows.

4. Can GROUP BY use multiple columns?

Yes.

GROUP BY department, job_title;

5. Can GROUP BY be used without aggregate functions?

Yes, although DISTINCT is often clearer when the goal is simply to return unique combinations.

🎯 Practice Questions

1. Find the number of employees in each department.

2. Find the average salary for each department.

3. Find the total sales for each region.

4. Find the highest salary in each department.

5. Find the total revenue for each product category and sort categories from highest to lowest revenue.

🎯 Key Takeaways

• GROUP BY creates groups of similar rows.

• It is commonly used with aggregate functions.

• COUNT() counts.

• SUM() totals.

• AVG() calculates averages.

• MIN() finds the minimum.

• MAX() finds the maximum.

• WHERE filters rows before grouping.

• ORDER BY can sort grouped results.

• GROUP BY is one of the most important SQL concepts for Data Science and Analytics.

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🚀 Data Science Roadmap 2026

📍 Phase 3: SQL for Data Science

📖 Topic 7 — HAVING

HAVING is used to filter grouped results.

The most important thing to remember:

WHERE → filters rows

HAVING → filters groups

🔹 Why Do We Need HAVING?

Suppose you want to find departments that have more than 5 employees.

First, you need to group employees by department:

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

But if you want only departments with more than 5 employees, use HAVING:

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

The grouping happens first, and then HAVING filters the groups.

🔹 Basic Syntax

SELECT column_name, aggregate_function(column_name)
FROM table_name
GROUP BY column_name
HAVING condition;

🔹 HAVING with COUNT()

Find departments having more than 10 employees:

SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 10;

🔹 HAVING with SUM()

Find regions where total sales exceed 100000:

SELECT region, SUM(sales) AS total_sales
FROM sales
GROUP BY region
HAVING SUM(sales) > 100000;

🔹 HAVING with AVG()

Find departments where the average salary is greater than 70000:

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department
HAVING AVG(salary) > 70000;

🔹 HAVING with MAX()

Find departments where the highest salary is greater than 150000:

SELECT department, MAX(salary) AS highest_salary
FROM employees
GROUP BY department
HAVING MAX(salary) > 150000;

🔹 WHERE vs HAVING

This is one of the most frequently asked SQL interview questions.

WHERE Filters individual rows.

SELECT *
FROM employees
WHERE salary > 50000;

It removes individual records that don't meet the condition.

HAVING

Filters groups after GROUP BY.

SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department
HAVING AVG(salary) > 70000;

It removes entire groups that don't meet the condition.

🔹 Using WHERE and HAVING Together

You can use both.

Suppose you want departments where:

• Only employees earning more than 50000 are considered

• The department must have at least 5 such employees

SELECT department, COUNT(*) AS employee_count
FROM employees
WHERE salary > 50000
GROUP BY department
HAVING COUNT(*) >= 5;

Conceptually:

All employees
     ↓
WHERE
     ↓
Employees with salary > 50000
     ↓
GROUP BY
     ↓
Create department groups
     ↓
HAVING
     ↓
Keep groups with at least 5 employees

🔹 HAVING with ORDER BY

You can also sort the filtered groups.

SELECT department, SUM(salary) AS total_salary
FROM employees
GROUP BY department
HAVING SUM(salary) > 500000
ORDER BY total_salary DESC;

This finds departments whose total salary exceeds 500000 and displays the highest totals first.

🔹 HAVING Without GROUP BY

Some SQL databases allow HAVING without an explicit GROUP BY, treating the entire result as a single group.

For example:

SELECT COUNT(*) AS employee_count
FROM employees
HAVING COUNT(*) > 100;

This returns the result only if there are more than 100 employees.

For beginners, however, focus primarily on the common pattern:

GROUP BY → HAVING

🔹 Common Mistake

❌ Incorrect:

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

Aggregate functions such as COUNT() generally cannot be used in WHERE.

✅ Correct:

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

🔹 Real-World Data Science Example

Suppose you're analyzing customer transactions.

You want customers who have made at least 10 transactions:

SELECT customer_id,
       COUNT(*) AS transaction_count
FROM transactions
GROUP BY customer_id
HAVING COUNT(*) >= 10;

This can help identify:

• Frequent customers

• High-engagement users

• Active accounts

• Customers suitable for segmentation

Another example:

Find products generating more than 50000 in revenue:

SELECT product_id,
       SUM(quantity * price) AS revenue
FROM sales
GROUP BY product_id
HAVING SUM(quantity * price) > 50000
ORDER BY revenue DESC;

🔹 Interview Questions

What is HAVING?

HAVING filters groups created by GROUP BY.


What is the difference between WHERE and HAVING?

WHERE filters rows before grouping.

HAVING filters groups after grouping.


Can HAVING be used with aggregate functions?

Yes.

HAVING COUNT(*) > 5


Can WHERE and HAVING be used together?

Yes.

WHERE salary > 50000
GROUP BY department
HAVING COUNT(*) > 5

Why can't we normally use COUNT() in WHERE?

Because WHERE operates on individual rows before the aggregation is performed. HAVING is designed to filter aggregated groups.

🎯 Practice Questions

1. Find departments with more than 10 employees.

2. Find customers who have made at least 5 transactions.

3. Find regions where total sales exceed 100000.

4. Find departments where average salary is greater than 75000.

5. Find products whose total revenue exceeds 50000 and sort them from highest to lowest revenue.

🎯 Key Takeaways

• WHERE → Filters rows.

• GROUP BY → Creates groups.

• HAVING → Filters groups.

• WHERE is applied before grouping.

• HAVING is applied after grouping.

• HAVING is commonly used with COUNT(), SUM(), AVG(), MIN(), and MAX().

• WHERE + GROUP BY + HAVING is one of the most important SQL patterns for analytics.

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