Data Science & Machine Learning

🚀 Data Science Roadmap 2026 📍 Phase 3: SQL for Data Science 📖 Topic 3 — ORDER BY ORDER BY is used to sort the rows returned by a SQL query. It helps you arrange data in a meaningful order, such as: • Highest salary to lowest salary • Lowest price to highest price • Newest date to oldest date • A–Z or Z–A • Highest sales to lowest sales 🔹 Basic Syntax

SELECT column1, column2
FROM table_name
ORDER BY column_name;

By default, SQL sorts in ascending order.

SELECT *
FROM employees
ORDER BY salary;

This generally returns employees from the lowest salary to the highest salary. 🔹 ASC — Ascending Order ASC means ascending.

SELECT *
FROM employees
ORDER BY salary ASC;

For numbers: 10000, 25000, 40000, 75000, 100000 For text: Amazon, Apple, Google, Microsoft For dates: 2024-01-01, 2024-05-15, 2024-12-20, 2025-03-10 ASC is usually the default, so ORDER BY salary; is equivalent to ORDER BY salary ASC; 🔹 DESC — Descending Order DESC means descending.

SELECT *
FROM employees
ORDER BY salary DESC;

Example: 100000, 75000, 40000, 25000, 10000 This is especially useful when you want to find: • Highest-paid employees • Top-selling products • Most expensive products • Most recent transactions • Highest-performing regions 🔹 Sorting Text You can sort text columns alphabetically.

SELECT employee_name, department
FROM employees
ORDER BY employee_name ASC;

SELECT employee_name, department
FROM employees
ORDER BY employee_name DESC;

🔹 Sorting Dates To find the most recent transactions:

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

To see transactions from oldest to newest:

SELECT transaction_id, transaction_date, amount
FROM transactions
ORDER BY transaction_date ASC;

🔹 Sorting by Multiple Columns This is very important. Suppose you want to sort employees: 1. By department 2. Within each department, by salary from highest to lowest

SELECT employee_name, department, salary
FROM employees
ORDER BY department ASC, salary DESC;

SQL first sorts by department. When multiple rows have the same department, it then uses salary to determine their order. Example: Finance 90000 Finance 70000 Finance 50000 HR 85000 HR 60000 IT 120000 IT 95000 🔹 ORDER BY with Calculations You can also sort using an expression.

SELECT product_name, quantity, price,
       quantity * price AS total_value
FROM products
ORDER BY quantity * price DESC;

You can also use the alias in many SQL databases:

SELECT product_name,
       quantity * price AS total_value
FROM products
ORDER BY total_value DESC;

🔹 ORDER BY with DISTINCT

SELECT DISTINCT department
FROM employees
ORDER BY department ASC;

This returns each department once and sorts them alphabetically. 🔹 ORDER BY and NULL Values NULL represents a missing or unknown value. The position of NULL values when using ORDER BY can vary between SQL databases. Some databases also support explicit control such as:

ORDER BY salary ASC NULLS LAST;

or

ORDER BY salary DESC NULLS FIRST;

Always check the syntax supported by your SQL database. 🔹 ORDER BY with WHERE WHERE filters the rows first, and ORDER BY sorts the resulting rows.

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

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❌ Mistake 2: Forgetting quotes around text Incorrect:

WHERE city = Pune;

Correct:

WHERE city = 'Pune';

❌ Mistake 3: Using AND when you mean OR Incorrect if you want either city:

WHERE city = 'Pune'
AND city = 'Mumbai';

A single city value cannot normally be both at the same time. Correct:

WHERE city = 'Pune'
OR city = 'Mumbai';

Or:

WHERE city IN ('Pune', 'Mumbai');

❌ Mistake 4: Forgetting parentheses For complex conditions, use parentheses:

WHERE
    (city = 'Pune' OR city = 'Mumbai')
    AND age > 30;

❌ Mistake 5: Assuming BETWEEN excludes the boundaries BETWEEN is generally inclusive. 🔹 34. Interview Questions 💡 What is the purpose of WHERE? WHERE filters rows based on a condition. 💡 What is the difference between WHERE and SELECT? SELECT → Determines what columns/expressions appear in the result. WHERE → Determines which rows are included. 💡 How do you check for NULL? Use: IS NULL or: IS NOT NULL 💡 What is the difference between IN and OR? IN provides a concise way to test whether a value matches any value in a list. 💡 Is BETWEEN inclusive? Yes, BETWEEN generally includes both boundary values. 🎯 Practice Questions Q1. Write a query to retrieve employees whose salary is greater than 50,000. Q2. Write a query to retrieve customers from Pune or Mumbai. Q3. Write a query to retrieve products priced between 1,000 and 5,000. Q4. Write a query to retrieve customers whose phone number is missing. Q5. Write a query to retrieve orders where the status is Success and the amount is greater than 10,000. 🎯 Key Takeaways ✅ WHERE is used to filter rows. ✅ = checks equality. ✅ <> and != can be used for not equal. ✅ AND requires all specified conditions to be true. ✅ OR requires at least one condition to be true. ✅ IN is useful for matching multiple values. ✅ BETWEEN is useful for ranges and is generally inclusive. ✅ LIKE is used for pattern matching. ✅ % represents a sequence of characters. ✅ _ represents one character. ✅ Use IS NULL and IS NOT NULL for NULL values. ✅ Parentheses make complex AND/OR logic clearer and safer. 🧭 Double Tap ❤️ For More ----- 3 ₽ · /balance_help

But IN makes this much cleaner:

SELECT *
FROM customers
WHERE city IN ('Pune', 'Mumbai', 'Delhi');

IN checks whether a value belongs to a specified list. 🔹 23. NOT IN You can also exclude multiple values.

SELECT *
FROM customers
WHERE city NOT IN ('Pune', 'Mumbai');

This returns customers whose city isn't Pune or Mumbai. 🔹 24. LIKE LIKE is used for pattern matching. Suppose we want names beginning with A.

SELECT *
FROM customers
WHERE name LIKE 'A%';

Here: % → Any sequence of characters So this could match: Alice Amit Ananya 🔹 25. LIKE with % Example:

SELECT *
FROM customers
WHERE name LIKE '%an%';

This searches for names containing the sequence an. The exact behavior can depend on database collation and case-sensitivity settings. 🔹 26. LIKE with _ The underscore _ generally represents exactly one character. Example:

SELECT *
FROM products
WHERE product_code LIKE 'A_1';

This could match: A11 AB1 AX1 But not: A123 A1 because _ represents one character. 🔹 27. NULL Values One of the most important concepts in SQL filtering is NULL. NULL generally means:

Missing, unknown, or unavailable value.

It does not mean: Zero Empty string False For example: customer_id name phone 101 Alice 9999999999 102 Bob NULL Bob's phone number is missing or unknown. 🔹 28. Checking for NULL You should not normally write:

WHERE phone = NULL

Instead, use:

SELECT *
FROM customers
WHERE phone IS NULL;

To find records where the value exists:

SELECT *
FROM customers
WHERE phone IS NOT NULL;

This is extremely important in Data Analytics. 🔹 29. WHERE and NULL Logic Suppose:

WHERE salary > 50000

What happens when salary is NULL? The condition isn't considered true. The row won't be returned. SQL uses three-valued logic involving: TRUE FALSE UNKNOWN This is one reason NULL handling requires special attention. 🔹 30. WHERE with SELECT WHERE works together with SELECT. Example:

SELECT
    customer_id,
    name,
    city
FROM customers
WHERE city = 'Pune';

The query: 1. Retrieves selected columns 2. From the customers table 3. Keeps only rows satisfying the condition 🔹 31. WHERE in Real-World Data Science Imagine a transaction database containing millions of records. A Data Scientist needs:

Successful transactions above ₹10,000 from January 2026 onward.

A query might look like:

SELECT
    transaction_id,
    customer_id,
    transaction_date,
    amount
FROM transactions
WHERE status = 'Success'
AND amount > 10000
AND transaction_date >= '2026-01-01';

This is much more efficient for analysis than extracting the entire table and filtering everything later in Python. 🔹 32. WHERE Before Python A common Data Science workflow is: Database → SQL → Filter/Transform → Python → Analysis → Model For example:

SELECT
    customer_id,
    amount,
    transaction_date
FROM transactions
WHERE status = 'Success';

Then load the result into Pandas:

import pandas as pd

df = pd.read_sql(query, connection)

SQL handles the database-side filtering, while Python can then handle deeper analysis. 🔹 33. Common Mistakes ❌ Mistake 1: Using = with NULL Incorrect:

WHERE phone = NULL;

Correct:

WHERE phone IS NULL;

SELECT *
FROM customers
WHERE city = 'Pune'
OR city = 'Mumbai';

This returns customers from either Pune or Mumbai. 🔹 14. AND vs OR Consider:

WHERE age > 30
AND city = 'Pune'

A customer must satisfy both conditions. But:

WHERE age > 30
OR city = 'Pune'

A customer only needs to satisfy one or both conditions. This difference is extremely important. 🔹 15. NOT NOT reverses a condition. Example:

SELECT *
FROM customers
WHERE NOT city = 'Pune';

This returns customers who aren't from Pune. You can also commonly write:

SELECT *
FROM customers
WHERE city <> 'Pune';

🔹 16. Combining AND and OR You can combine multiple logical operators. Example:

SELECT *
FROM employees
WHERE department = 'Finance'
AND salary > 60000;

Another example:

SELECT *
FROM employees
WHERE department = 'Finance'
OR department = 'Analytics'
AND salary > 60000;

When conditions become complex, use parentheses to make your intended logic explicit. For example:

SELECT *
FROM employees
WHERE
    (department = 'Finance' OR department = 'Analytics')
    AND salary > 60000;

This means:

Employees from Finance or Analytics who earn more than 60,000.

🔹 17. Why Parentheses Matter Consider:

WHERE city = 'Pune'
OR city = 'Mumbai'
AND age > 30

SQL's logical evaluation rules can make this behave differently from what a beginner might expect. A safer and clearer version is:

WHERE
    (city = 'Pune' OR city = 'Mumbai')
    AND age > 30;

This clearly communicates the intended logic. Best practice:

Use parentheses whenever combining AND and OR in a complex condition.

🔹 18. WHERE with Dates You can also filter dates. Example:

SELECT *
FROM orders
WHERE order_date >= '2026-01-01';

This retrieves orders on or after January 1, 2026. Another example:

SELECT *
FROM orders
WHERE order_date < '2026-07-01';

This retrieves orders before July 1, 2026. Date syntax can vary slightly across database systems, so always consider the SQL dialect you're using. 🔹 19. Filtering a Date Range Suppose you want orders during a particular period. You can use:

SELECT *
FROM orders
WHERE order_date >= '2026-01-01'
AND order_date < '2026-04-01';

This retrieves orders from January through March. Using a half-open range like this is particularly useful when working with timestamps because it avoids accidentally excluding records with time components. 🔹 20. BETWEEN SQL provides BETWEEN for range filtering. Example:

SELECT *
FROM products
WHERE price BETWEEN 1000 AND 5000;

BETWEEN is inclusive of both boundaries in standard SQL. So this includes: 1000 and: 5000 as well as values between them. 🔹 21. BETWEEN with Dates Example:

SELECT *
FROM orders
WHERE order_date BETWEEN '2026-01-01' AND '2026-01-31';

For a date-only column, this can be useful. However, if order_date contains timestamps, using:

order_date >= '2026-01-01'
AND order_date < '2026-02-01'

is often safer because it includes the entire final day regardless of the timestamp. 🔹 22. IN Operator Suppose you want customers from: Pune Mumbai Delhi You could write:

SELECT *
FROM customers
WHERE city = 'Pune'
OR city = 'Mumbai'
OR city = 'Delhi';

🚀 Data Science Roadmap 2026 ** 📍 Phase 3: SQL for Data Science** 📖 Topic 2: SQL Basics — WHERE After learning SELECT, the next essential SQL concept is WHERE. In real-world Data Science, databases can contain millions or billions of records. You usually don't want to retrieve everything. You want to answer questions such as: Which customers are from Mumbai? Which orders are above ₹10,000? Which employees joined after 2023? Which transactions were successful? Which products belong to a particular category? The WHERE clause allows you to filter rows based on conditions. 🔹 1. What Is WHERE? WHERE is used to filter records based on a specified condition. Basic syntax:

SELECT column1, column2
FROM table_name
WHERE condition;

Example:

SELECT *
FROM customers
WHERE city = 'Mumbai';

This returns only customers whose city is Mumbai. 🔹 2. WHERE with Text Values Text values are generally written inside single quotes. Example:

SELECT customer_id, name
FROM customers
WHERE city = 'Pune';

This retrieves customers from Pune. Another example:

SELECT *
FROM employees
WHERE department = 'Finance';

🔹 3. WHERE with Numbers For numeric values, quotes are generally not required. Example:

SELECT *
FROM customers
WHERE age > 30;

This returns customers older than 30. Another example:

SELECT *
FROM orders
WHERE amount > 10000;

This returns orders where the amount is greater than 10,000. 🔹 4. Comparison Operators The most commonly used comparison operators are: Operator Meaning = Equal to <> Not equal to != Not equal to

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

Example:

SELECT *
FROM employees
WHERE salary >= 50000;

This returns employees whose salary is at least 50,000. 🔹 5. Equal To = The = operator checks whether two values are equal.

SELECT *
FROM customers
WHERE city = 'Delhi';

Only records where city equals Delhi are returned. 🔹 6. Not Equal <> You can retrieve records that don't match a value.

SELECT *
FROM customers
WHERE city <> 'Delhi';

This returns customers whose city isn't Delhi. You may also see:

WHERE city != 'Delhi'

Both are commonly supported, although <> is the standard SQL operator. 🔹 7. Greater Than > Example:

SELECT *
FROM orders
WHERE amount > 50000;

Returns orders above 50,000. 🔹 8. Less Than < Example:

SELECT *
FROM products
WHERE price < 1000;

Returns products priced below 1,000. 🔹 9. Greater Than or Equal To >= Example:

SELECT *
FROM employees
WHERE experience >= 5;

This includes employees with exactly 5 years as well as those with more than 5 years. 🔹 10. Less Than or Equal To <= Example:

SELECT *
FROM products
WHERE price <= 500;

This includes products priced exactly at 500. 🔹 11. WHERE with Multiple Conditions Real-world queries often require more than one condition. For this, SQL provides logical operators: • AND • OR • NOT 🔹 12. AND AND means all conditions must be true. Example:

SELECT *
FROM customers
WHERE city = 'Pune'
AND age > 30;

This returns customers who: 1. Are from Pune 2. Are older than 30 Both conditions must be satisfied. 🔹 13. OR OR means at least one condition must be true. Example:

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order_id | customer_id | product | price
1        | 101         | Laptop  | 60000
2        | 102         | Phone   | 30000
3        | 101         | Mouse   | 1000

To retrieve order details:

SELECT
    order_id,
    customer_id,
    product,
    price
FROM orders;

To calculate price after adding a hypothetical 10% increase:

SELECT
    product,
    price,
    price * 1.10 AS increased_price
FROM orders;

🔹 21. SELECT in Real-World Data Science SQL is often the first step in a Data Science workflow. For example: Business Question "Give me all customer transactions from the sales database." You might start with:

SELECT
    customer_id,
    order_date,
    product_id,
    amount
FROM transactions;

Then later: WHERE → Filter data GROUP BY → Aggregate data JOIN → Combine tables ORDER BY → Sort results Window Functions → Advanced analysis Eventually, you may load the SQL result into Pandas:

import pandas as pd
df = pd.read_sql(query, connection)

So SQL and Python often work together. 🔹 22. Common Beginner Mistakes ❌ Mistake 1: Forgetting the FROM clause Incorrect: SELECT name; when you intend to retrieve a column from a table. Correct:

SELECT name
FROM customers;

❌ Mistake 2: Using commas incorrectly Correct:

SELECT name, city, age
FROM customers;

❌ Mistake 3: Confusing column names and values A column: city is different from a text value: 'Pune' We'll explore this more when we learn WHERE. ❌ Mistake 4: Using SELECT * everywhere SELECT * is useful while learning and exploring, but for production queries, selecting only the required columns can be more efficient and clearer. 🔹 23. Interview Questions 💡 What does SELECT do? SELECT specifies the columns or expressions that should appear in the query result. 💡 What does SELECT * mean? It selects all columns from the specified table. 💡 What is an alias? An alias gives a temporary name to a column or expression in the query result. 💡 What does DISTINCT do? It removes duplicate rows from the selected result. 🎯 Practice Questions Q1. Write a query to select all columns from a table called employees. Q2. Write a query to select only employee_id and salary from employees. Q3. Write a query to display salary as monthly_salary. Q4. Write a query to calculate price * quantity as total_amount from an orders table. Q5. Write a query to return unique values from the department column of an employees table. 🎯 Key Takeaways ✅ SQL is used to communicate with relational databases. ✅ SELECT specifies what you want to retrieve. ✅ FROM specifies the table. ✅ * means all columns. ✅ You can select one or multiple columns. ✅ AS creates a temporary alias. ✅ SQL can perform calculations. ✅ DISTINCT returns unique results. ✅ SQL is one of the most important tools for extracting data before analysis and Machine Learning. 🧭 Double Tap ❤️ For More ----- 1.57 ₽ · /balance_help