Quick SQL functions cheat sheet for beginners

Aggregate Functions

COUNT(*): Counts rows.

SUM(column): Total sum.

AVG(column): Average value.

MAX(column): Maximum value.

MIN(column): Minimum value.


String Functions

CONCAT(a, b, …): Concatenates strings.

SUBSTRING(s, start, length): Extracts part of a string.

UPPER(s) / LOWER(s): Converts string case.

TRIM(s): Removes leading/trailing spaces.


Date & Time Functions

CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.

EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).

DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.


Numeric Functions

ROUND(num, decimals): Rounds to a specified decimal.

CEIL(num) / FLOOR(num): Rounds up/down.

ABS(num): Absolute value.

MOD(a, b): Returns the remainder.


Control Flow Functions

CASE: Conditional logic.

COALESCE(val1, val2, …): Returns the first non-null value.


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#dataanalytics

Essential SQL Topics for Data Analysts

SQL for Data Analysts Free Resources -> https://t.iss.one/sqlanalyst

- Basic Queries: SELECT, FROM, WHERE clauses.
- Sorting and Filtering: ORDER BY, GROUP BY, HAVING.
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
- Aggregation Functions: COUNT, SUM, AVG, MIN, MAX.
- Subqueries: Embedding queries within queries.
- Data Modification: INSERT, UPDATE, DELETE.
- Indexes: Optimizing query performance.
- Normalization: Ensuring efficient database design.
- Views: Creating virtual tables for simplified queries.
- Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many.

Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include:

- ROW_NUMBER(): Assigns a unique number to each row based on a specified order.
- RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently.
- LAG() and LEAD(): Access data from preceding or following rows within a partition.
- SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows.

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Learn SQL from basic to advanced level in 30 days

Week 1: SQL Basics

Day 1: Introduction to SQL and Relational Databases

Overview of SQL Syntax

Setting up a Database (MySQL, PostgreSQL, or SQL Server)


Day 2: Data Types (Numeric, String, Date, etc.)

Writing Basic SQL Queries:

SELECT, FROM

Day 3: WHERE Clause for Filtering Data

Using Logical Operators:

AND, OR, NOT

Day 4: Sorting Data: ORDER BY

Limiting Results: LIMIT and OFFSET

Understanding DISTINCT

Day 5: Aggregate Functions:

COUNT, SUM, AVG, MIN, MAX


Day 6: Grouping Data: GROUP BY and HAVING

Combining Filters with Aggregations


Day 7: Review Week 1 Topics with Hands-On Practice

Solve SQL Exercises on platforms like HackerRank, LeetCode, or W3Schools


Week 2: Intermediate SQL

Day 8: SQL JOINS:

INNER JOIN, LEFT JOIN

Day 9: SQL JOINS Continued: RIGHT JOIN, FULL OUTER JOIN, SELF JOIN

Day 10: Working with NULL Values

Using Conditional Logic with CASE Statements

Day 11: Subqueries: Simple Subqueries (Single-row and Multi-row)

Correlated Subqueries

Day 12: String Functions:

CONCAT, SUBSTRING, LENGTH, REPLACE

Day 13: Date and Time Functions: NOW, CURDATE, DATEDIFF, DATEADD

Day 14: Combining Results: UNION, UNION ALL, INTERSECT, EXCEPT

Review Week 2 Topics and Practice

Week 3: Advanced SQL

Day 15: Common Table Expressions (CTEs)

WITH Clauses and Recursive Queries

Day 16: Window Functions:

ROW_NUMBER, RANK, DENSE_RANK, NTILE

Day 17: More Window Functions:

LEAD, LAG, FIRST_VALUE, LAST_VALUE


Day 18: Creating and Managing Views

Temporary Tables and Table Variables

Day 19: Transactions and ACID Properties

Working with Indexes for Query Optimization

Day 20: Error Handling in SQL

Writing Dynamic SQL Queries


Day 21: Review Week 3 Topics with Complex Query Practice

Solve Intermediate to Advanced SQL Challenges



Week 4: Database Management and Advanced Applications

Day 22: Database Design and Normalization:

1NF, 2NF, 3NF


Day 23: Constraints in SQL:
PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, DEFAULT


Day 24: Creating and Managing Indexes

Understanding Query Execution Plans

Day 25: Backup and Restore Strategies in SQL

Role-Based Permissions

Day 26: Pivoting and Unpivoting Data

Working with JSON and XML in SQL

Day 27: Writing Stored Procedures and Functions

Automating Processes with Triggers

Day 28: Integrating SQL with Other Tools (e.g., Python, Power BI, Tableau)

SQL in Big Data: Introduction to NoSQL

Day 29: Query Performance Tuning:

Tips and Tricks to Optimize SQL Queries


Day 30: Final Review of All Topics

Attempt SQL Projects or Case Studies (e.g., analyzing sales data, building a reporting dashboard)

Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series 👍♥️

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SQL table interview questions:

1. What is a DUAL table and why do we need it?
- it is a special table which gets created automatically when we install Oracle database. It can be used to select pseudo columns, perform calculations and also as sequence generator etc.

2. How many columns and rows are present in DUAL table?
- one column & one row by default.

3. Can we insert more rows in to DUAL table?
- Yes.

4. What's the easiest way to backup a table / how can we create a table based on existing table?
- CREATE TABLE SALES_COPY AS SELECT * FROM SALES.

5. Can we drop all the columns from a table?
- No.

6. What is the difference between count(1) and count(*)?
- Both are same. Both consume same amount of resources, Both perform same operation

Essential Python Libraries to build your career in Data Science 📊👇

1. NumPy:
- Efficient numerical operations and array manipulation.

2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).

3. Matplotlib:
- 2D plotting library for creating visualizations.

4. Seaborn:
- Statistical data visualization built on top of Matplotlib.

5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.

6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.

7. PyTorch:
- Deep learning library, particularly popular for neural network research.

8. SciPy:
- Library for scientific and technical computing.

9. Statsmodels:
- Statistical modeling and econometrics in Python.

10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).

11. Gensim:
- Topic modeling and document similarity analysis.

12. Keras:
- High-level neural networks API, running on top of TensorFlow.

13. Plotly:
- Interactive graphing library for making interactive plots.

14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.

15. OpenCV:
- Library for computer vision tasks.

As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.

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Best Resources to learn Python & Data Science 👇👇

Python Tutorial

Data Science Course by Kaggle

Machine Learning Course by Google

Best Data Science & Machine Learning Resources

Interview Process for Data Science Role at Amazon

Python Interview Resources

Join @free4unow_backup for more free courses

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ENJOY LEARNING👍👍

SQL Cheatsheet ✅

SQL interview questions with answers 😄👇

1. Question: What is SQL?

Answer: SQL (Structured Query Language) is a programming language designed for managing and manipulating relational databases. It is used to query, insert, update, and delete data in databases.

2. Question: Differentiate between SQL and MySQL.

Answer: SQL is a language for managing relational databases, while MySQL is an open-source relational database management system (RDBMS) that uses SQL as its language.

3. Question: Explain the difference between INNER JOIN and LEFT JOIN.

Answer: INNER JOIN returns rows when there is a match in both tables, while LEFT JOIN returns all rows from the left table and the matched rows from the right table, filling in with NULLs for non-matching rows.

4. Question: How do you remove duplicate records from a table?

Answer: Use the DISTINCT keyword in a SELECT statement to retrieve unique records. For example: SELECT DISTINCT column1, column2 FROM table;

5. Question: What is a subquery in SQL?

Answer: A subquery is a query nested inside another query. It can be used to retrieve data that will be used in the main query as a condition to further restrict the data to be retrieved.

6. Question: Explain the purpose of the GROUP BY clause.

Answer: The GROUP BY clause is used to group rows that have the same values in specified columns into summary rows, like when using aggregate functions such as COUNT, SUM, AVG, etc.

7. Question: How can you add a new record to a table?

Answer: Use the INSERT INTO statement. For example: INSERT INTO table_name (column1, column2) VALUES (value1, value2);

8. Question: What is the purpose of the HAVING clause?

Answer: The HAVING clause is used in combination with the GROUP BY clause to filter the results of aggregate functions based on a specified condition.

9. Question: Explain the concept of normalization in databases.

Answer: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down tables into smaller, related tables.

10. Question: How do you update data in a table in SQL?

Answer: Use the UPDATE statement to modify existing records in a table. For example: UPDATE table_name SET column1 = value1 WHERE condition;

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🔰 SQL Roadmap for Beginners 2025
├── 🗃 Introduction to Databases & SQL
├── 📄 SQL vs NoSQL (Just Basics)
├── 🧱 Database Concepts (Tables, Rows, Columns, Keys)
├── 🔍 Basic SQL Queries (SELECT, WHERE)
├── ✏️ Filtering & Sorting Data (ORDER BY, LIMIT)
├── 🔢 SQL Operators (IN, BETWEEN, LIKE, AND, OR)
├── 📊 Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
├── 👥 GROUP BY & HAVING Clauses
├── 🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF)
├── 📦 Subqueries & Nested Queries
├── 🏷 Aliases & Case Statements
├── 🧾 Views & Indexes (Basics)
├── 🧠 Common Table Expressions (CTEs)
├── 🔄 Window Functions (ROW_NUMBER, RANK, PARTITION BY)
├── ⚙️ Data Manipulation (INSERT, UPDATE, DELETE)
├── 🧱 Data Definition (CREATE, ALTER, DROP)
├── 🔐 Constraints & Relationships (PK, FK, UNIQUE, CHECK)
├── 🧪 Real-world SQL Scenarios & Challenges

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#sql

If you want to Excel at using the most used database language in the world, learn these powerful SQL features:

• Wildcards (%, _) – Flexible pattern matching
• Window Functions – ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()
• Common Table Expressions (CTEs) – WITH for better readability
• Recursive Queries – Handle hierarchical data
• STRING Functions – LEFT(), RIGHT(), LEN(), TRIM(), UPPER(), LOWER()
• Date Functions – DATEDIFF(), DATEADD(), FORMAT()
• Pivot & Unpivot – Transform row data into columns
• Aggregate Functions – SUM(), AVG(), COUNT(), MIN(), MAX()
• Joins & Self Joins – Master INNER, LEFT, RIGHT, FULL, SELF JOIN
• Indexing – Speed up queries with CREATE INDEX

Like it if you need a complete tutorial on all these topics! 👍❤️

#sql

SQL Interview Questions

1. How would you find duplicate records in SQL?
2.What are various types of SQL joins?
3.What is a trigger in SQL?
4.What are different DDL,DML commands in SQL?
5.What is difference between Delete, Drop and Truncate?
6.What is difference between Union and Union all?
7.Which command give Unique values?
8. What is the difference between Where and Having Clause?
9.Give the execution of keywords in SQL?
10. What is difference between IN and BETWEEN Operator?
11. What is primary and Foreign key?
12. What is an aggregate Functions?
13. What is the difference between Rank and Dense Rank?
14. List the ACID Properties and explain what they are?
15. What is the difference between % and _ in like operator?
16. What does CTE stands for?
17. What is database?what is DBMS?What is RDMS?
18.What is Alias in SQL?
19. What is Normalisation?Describe various form?
20. How do you sort the results of a query?
21. Explain the types of Window functions?
22. What is limit and offset?
23. What is candidate key?
24. Describe various types of Alter command?
25. What is Cartesian product?

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Guys, Big Announcement!

I’m launching a Complete SQL Learning Series — designed for everyone — whether you're a beginner, intermediate, or someone preparing for data interviews.

This is a complete step-by-step journey — from scratch to advanced — filled with practical examples, relatable scenarios, and short quizzes after each topic to solidify your learning.

Here’s the 5-Week Plan:

Week 1: SQL Fundamentals (No Prior Knowledge Needed)

- What is SQL? Real-world Use Cases

- Databases vs Tables

- SELECT Queries — The Heart of SQL

- Filtering Data with WHERE

- Sorting with ORDER BY

- Using DISTINCT and LIMIT

- Basic Arithmetic and Column Aliases

Week 2: Aggregations & Grouping

- COUNT, SUM, AVG, MIN, MAX — When and How

- GROUP BY — The Right Way

- HAVING vs WHERE

- Dealing with NULLs in Aggregations

- CASE Statements for Conditional Logic

*Week 3: Mastering JOINS & Relationships*

- Understanding Table Relationships (1-to-1, 1-to-Many)

- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN

- Practical Examples with Two or More Tables

- SELF JOIN & CROSS JOIN — What, When & Why

- Common Join Mistakes & Fixes

Week 4: Advanced SQL Concepts

- Subqueries: Writing Queries Inside Queries

- CTEs (WITH Clause): Cleaner & More Readable SQL

- Window Functions: RANK, DENSE_RANK, ROW_NUMBER

- Using PARTITION BY and ORDER BY

- EXISTS vs IN: Performance and Use Cases


Week 5: Real-World Scenarios & Interview-Ready SQL

- Using SQL to Solve Real Business Problems

- SQL for Sales, Marketing, HR & Product Analytics

- Writing Clean, Efficient & Complex Queries

- Most Common SQL Interview Questions like:

“Find the second highest salary”

“Detect duplicates in a table”

“Calculate running totals”

“Identify top N products per category”

- Practice Challenges Based on Real Interviews

React with ❤️ if you're ready for this series

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SQL Joins – Essential Concepts 🚀

1️⃣ What Are SQL Joins?

SQL Joins are used to combine rows from two or more tables based on a related column.

2️⃣ Types of Joins

INNER JOIN: Returns only matching rows from both tables.
SELECT * FROM TableA INNER JOIN TableB ON TableA.id = TableB.id;

LEFT JOIN (LEFT OUTER JOIN): Returns all rows from the left table and matching rows from the right table.
SELECT * FROM TableA LEFT JOIN TableB ON TableA.id = TableB.id;

RIGHT JOIN (RIGHT OUTER JOIN): Returns all rows from the right table and matching rows from the left table.
SELECT * FROM TableA RIGHT JOIN TableB ON TableA.id = TableB.id;

FULL JOIN (FULL OUTER JOIN): Returns all rows when there is a match in either table.
SELECT * FROM TableA FULL JOIN TableB ON TableA.id = TableB.id;


3️⃣ Self Join

A table joins with itself to compare rows.
SELECT A.name, B.name FROM Employees A JOIN Employees B ON A.manager_id = B.id;

4️⃣ Cross Join

Returns the Cartesian product of both tables (every row from Table A pairs with every row from Table B).
SELECT * FROM TableA CROSS JOIN TableB;

5️⃣ Joins with Multiple Conditions

Using multiple columns for matching.
SELECT * FROM TableA INNER JOIN TableB ON TableA.id = TableB.id AND TableA.type = TableB.type;

6️⃣ Using Aliases in Joins

Shortens table names for better readability.
SELECT A.name, B.salary FROM Employees A INNER JOIN Salaries B ON A.id = B.emp_id;

7️⃣ Handling NULLs in Joins

Use COALESCE(column, default_value) to replace NULL values.

IS NULL to filter unmatched rows in LEFT or RIGHT JOINs.


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SQL Cheatsheet ✅

𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗦𝗤𝗟 𝗹𝗲𝘀𝘀𝗼𝗻 𝘆𝗼𝘂’𝗹𝗹 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝘁𝗼𝗱𝗮𝘆:

Master the core SQL statements—they are the building blocks of every powerful query you'll write.

-> SELECT retrieves data efficiently and accurately. Remember, clarity starts with understanding the result set you need.

-> WHERE filters data to show only the insights that matter. Precision is key.

-> CREATE, INSERT, UPDATE, DELETE allow you to mold your database like an artist—design it, fill it, improve it, or even clean it up.

In a world where everyone wants to take, give knowledge back.

Become an alchemist of your life. Learn, share, and build solutions.

Always follow best practices in SQL to avoid mistakes like missing WHERE in an UPDATE or DELETE. These oversights can cause chaos!

Without WHERE, you risk updating or deleting entire datasets unintentionally. That's a costly mistake.

But with proper syntax and habits, your databases will be secure, efficient, and insightful.

SQL is not just a skill—it's a mindset of precision, logic, and innovation.

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#sql

SQL Roadmap
👇👇
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075

Complete SQL Topics for Data Analysts 😄👇

1. Introduction to SQL:
- Basic syntax and structure
- Understanding databases and tables

2. Querying Data:
- SELECT statement
- Filtering data using WHERE clause
- Sorting data with ORDER BY

3. Joins:
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Combining data from multiple tables

4. Aggregation Functions:
- GROUP BY
- Aggregate functions like COUNT, SUM, AVG, MAX, MIN

5. Subqueries:
- Using subqueries in SELECT, WHERE, and HAVING clauses

6. Data Modification:
- INSERT, UPDATE, DELETE statements
- Transactions and Rollback

7. Data Types and Constraints:
- Understanding various data types (e.g., INT, VARCHAR)
- Using constraints (e.g., PRIMARY KEY, FOREIGN KEY)

8. Indexes:
- Creating and managing indexes for performance optimization

9. Views:
- Creating and using views for simplified querying

10. Stored Procedures and Functions:
- Writing and executing stored procedures
- Creating and using functions

11. Normalization:
- Understanding database normalization concepts

12. Data Import and Export:
- Importing and exporting data using SQL

13. Window Functions:
- ROW_NUMBER(), RANK(), DENSE_RANK(), and others

14. Advanced Filtering:
- Using CASE statements for conditional logic

15. Advanced Join Techniques:
- Self-joins and other advanced join scenarios

16. Analytical Functions:
- LAG(), LEAD(), OVER() for advanced analytics

17. Working with Dates and Times:
- Date and time functions and formatting

18. Performance Tuning:
- Query optimization strategies

19. Security:
- Understanding SQL injection and best practices for security

20. Handling NULL Values:
- Dealing with NULL values in queries

Ensure hands-on practice on these topics to strengthen your SQL skills.

Since SQL is one of the most essential skill for data analysts, I have decided to teach each topic daily in this channel for free. Like this post if you want me to continue this SQL series 👍♥️

Share with credits: https://t.iss.one/sqlspecialist

Hope it helps :)