𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 | 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄😍

- Infosys
- Genpact
- IBM
- Virtusa
- S&P Global

Job Location:- Across India

Qualification:- Graduate/Post Graduate

Salary Range :- 5 To 21LPA

𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :- 

https://bit.ly/44qMX2k

Select your experience & Complete The Registration Process

 Once your profile shortlisted , you will get call letter from recruiters

Here are some interview questions for both freshers and experienced applying for a data analyst #SQL

Analyst role:

#ForFreshers:
1. What is SQL, and why is it important in data analysis?
2. Explain the difference between a database and a table.
3. What are the basic SQL commands for data retrieval?
4. How do you retrieve all records from a table named "Employees"?
5. What is a primary key, and why is it important in a database?
6. What is a foreign key, and how is it used in SQL?
7. Describe the difference between SQL JOIN and SQL UNION.
8. How do you write a SQL query to find the second-highest salary in a table?
9. What is the purpose of the GROUP BY clause in SQL?
10. Can you explain the concept of normalization in SQL databases?
11. What are the common aggregate functions in SQL, and how are they used?

ForExperiencedCandidates:

1. Describe a scenario where you had to optimize a slow-running SQL query. How did you approach it?
2. Explain the differences between SQL Server, MySQL, and Oracle databases.
3. Can you describe the process of creating an index in a SQL database and its impact on query performance?
4. How do you handle data quality issues when performing data analysis with SQL?
5. What is a subquery, and when would you use it in SQL? Give an example of a complex SQL query you've written to extract specific insights from a database.
6. How do you handle NULL values in SQL, and what are the challenges associated with them?
7. Explain the ACID properties of a database and their importance.
8. What are stored procedures and triggers in SQL, and when would you use them?
9. Describe your experience with ETL (Extract, Transform, Load) processes using SQL.
10. Can you explain the concept of query optimization in SQL, and what techniques have you used for optimization?

Enjoy Learning 👍👍

SQL Cheatsheet 📝

This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.

1. Database Basics
- CREATE DATABASE db_name;
- USE db_name;

2. Tables
- Create Table: CREATE TABLE table_name (col1 datatype, col2 datatype);
- Drop Table: DROP TABLE table_name;
- Alter Table: ALTER TABLE table_name ADD column_name datatype;

3. Insert Data
- INSERT INTO table_name (col1, col2) VALUES (val1, val2);

4. Select Queries
- Basic Select: SELECT * FROM table_name;
- Select Specific Columns: SELECT col1, col2 FROM table_name;
- Select with Condition: SELECT * FROM table_name WHERE condition;

5. Update Data
- UPDATE table_name SET col1 = value1 WHERE condition;

6. Delete Data
- DELETE FROM table_name WHERE condition;

7. Joins
- Inner Join: SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;
- Left Join: SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;
- Right Join: SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;

8. Aggregations
- Count: SELECT COUNT(*) FROM table_name;
- Sum: SELECT SUM(col) FROM table_name;
- Group By: SELECT col, COUNT(*) FROM table_name GROUP BY col;

9. Sorting & Limiting
- Order By: SELECT * FROM table_name ORDER BY col ASC|DESC;
- Limit Results: SELECT * FROM table_name LIMIT n;

10. Indexes
- Create Index: CREATE INDEX idx_name ON table_name (col);
- Drop Index: DROP INDEX idx_name;

11. Subqueries
- SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);

12. Views
- Create View: CREATE VIEW view_name AS SELECT * FROM table_name;
- Drop View: DROP VIEW view_name;

Here you can find SQL Interview Resources👇
https://t.iss.one/DataSimplifier

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

Hope it helps :)

𝟯𝟬+ 𝗙𝗥𝗘𝗘 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

India's Biggest AI Challenge (13th To 15th July )

, Earn Free certificates & Boost your resume!

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 

https://pdlink.in/3Gx7lW7

Enroll For FREE & Become an AI Champion🏆

Operating System RoadMap
|
|-- Kernel
| |-- Memory Management
| | |-- Paging
| | |-- Segmentation
| | |-- Virtual Memory
| |
| |-- Process Management
| | |-- Process Scheduling
| | |-- Inter-Process Communication (IPC)
| | |-- Threads
| |
| |-- File System
| | |-- File I/O
| | |-- Directory Structure
| | |-- File Permissions
| |
| |-- Device Drivers
| | |-- Communication with Hardware
| | |-- Input/Output (I/O)
| |
| |-- System Calls
| |-- Interface to Kernel Functionality
| |-- Examples: open(), read(), write(), etc.
|
|-- Memory Management
| |-- RAM
| | |-- Stack
| | |-- Heap
| | |-- Data Segment
| | |-- Code Segment
| |
| |-- Cache
| | |-- L1, L2, L3 Caches
| |
| |-- Virtual Memory
| |-- Page Table
| |-- Page Replacement Algorithms
| |-- Swapping
|
|-- File System
| |-- File Organization
| |-- File Allocation Table (FAT)
| |-- Inodes
| |-- File Access Methods
|
|-- Networking
| |-- TCP/IP
| |-- Protocols
| |-- Network Stack
| |-- Routing
| |-- Firewalls
|
|-- Security
| |-- Authentication
| |-- Authorization
| |-- Encryption
| |-- Access Control Lists (ACL)
|
|-- Process Management
| |-- PCB (Process Control Block)
| |-- Context Switching
| |-- Deadlocks
| |-- Synchronization
| |-- Mutual Exclusion
|
|-- Device Management
| |-- I/O Buffering
| |-- Device Controllers
| |-- Interrupt Handling
| |-- DMA (Direct Memory Access)
|
|-- User Interface
| |-- Graphical User Interface (GUI)
| |-- Command Line Interface (CLI)
| |-- Windowing Systems
|
|-- Shell
| |-- Command Interpreter
| |-- Scripting
| |-- Job Control
|
|-- System Utilities
| |-- Task Manager
| |-- Disk Cleanup
| |-- System Monitor
| |-- Backup and Restore
|
|-- Boot Process
| |-- BIOS/UEFI
| |-- Boot Loader
| |-- Kernel Initialization
| |-- Init Process
|
|-- System Libraries
| |-- Standard C Library
| |-- POSIX Library
| |-- WinAPI (for Windows)
|
|-- System Calls
| |-- File System Calls
| |-- Process Control Calls
| |-- Memory Management Calls
| |-- Communication Calls
|
|-- Error Handling
| |-- Error Codes
| |-- Logging
| |-- Recovery Strategies
|
|-- Distributed Systems
| |-- Clustering
| |-- Load Balancing
| |-- Distributed File Systems
|
|-- Cloud Computing
| |-- Virtualization
| |-- Infrastructure as a Service (IaaS)
| |-- Platform as a Service (PaaS)
| |-- Software as a Service (SaaS)
|
└-- Comments
|-- // Single-line comment
└-- /* Multi-line comment */

Join for more: https://t.iss.one/programming_guide

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍

Whether you’re a student, job seeker, or just hungry to upskill — these 5 beginner-friendly courses are your golden ticket🎟️

No fluff. No fees. Just career-boosting knowledge and certificates that make your resume pop✨️

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/42vL6br

Enjoy Learning ✅️

Here is the list of latest trending tech stacks in 2025👇👇

1. Frontend Development:
- React.js: Known for its component-based architecture and strong community support.
- Vue.js: Valued for its simplicity and flexibility in building user interfaces.
- Angular: Still widely used, especially in enterprise applications.

2. Backend Development:
- Node.js: Popular for building scalable and fast network applications using JavaScript.
- Django: Preferred for its rapid development capabilities and robust security features.
- Spring Boot: Widely used in Java-based applications for its ease of use and integration capabilities.

3. Mobile Development:
- Flutter: Known for building natively compiled applications for mobile, web, and desktop from a single codebase.
- React Native: Continues to be popular for building cross-platform applications with native capabilities.

4. Cloud Computing and DevOps:
- AWS (Amazon Web Services), Azure, Google Cloud: Leading cloud service providers offering extensive services for computing, storage, and networking.
- Docker and Kubernetes: Essential for containerization and orchestration of applications in a cloud-native environment.
- Terraform: Infrastructure as code tool for managing and provisioning cloud infrastructure.

5. Data Science and Machine Learning:
- Python: Dominant language for data science and machine learning, with libraries like NumPy, Pandas, and Scikit-learn.
- TensorFlow and PyTorch: Leading frameworks for building and training machine learning models.
- Apache Spark: Used for big data processing and analytics.

6. Cybersecurity:
- SIEM Tools (Security Information and Event Management): Such as Splunk and ELK Stack, crucial for monitoring and managing security incidents.
- Zero Trust Architecture: A security model that eliminates the idea of trust based on network location.

7. Blockchain and Cryptocurrency:
- Ethereum: A blockchain platform supporting smart contracts and decentralized applications.
- Hyperledger Fabric: Framework for developing permissioned, blockchain-based applications.

8. Artificial Intelligence (AI) and Natural Language Processing (NLP):
- GPT (Generative Pre-trained Transformer) Models: Such as GPT-4, used for various natural language understanding tasks.
- Computer Vision: Frameworks like OpenCV for image and video processing tasks.

9. Edge Computing and IoT (Internet of Things):
- Edge Computing: Technologies that bring computation and data storage closer to the location where it is needed.
- IoT Platforms: Such as AWS IoT, Azure IoT Hub, offering capabilities for managing and securing IoT devices and data.

Best Resources to help you with the journey 👇👇

Javascript Roadmap
https://t.iss.one/javascript_courses/309

Best Programming Resources: https://topmate.io/coding/886839

Web Development Resources
https://t.iss.one/webdevcoursefree

Latest Jobs & Internships
https://t.iss.one/getjobss

Cryptocurrency Basics
https://t.iss.one/Bitcoin_Crypto_Web/236

Python Resources
https://t.iss.one/pythonanalyst

Data Science Resources
https://t.iss.one/datasciencefree

Best DSA Resources
https://topmate.io/coding/886874

Udemy Free Courses with Certificate
https://t.iss.one/udemy_free_courses_with_certi

Join @free4unow_backup for more free resources.

ENJOY LEARNING 👍👍

🎓 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 - 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Unlock the power of data and launch your tech career with this FREE industry-relevant certification!

📘 What You’ll Learn:

- Introduction to Data Science & Analytics
- Database Management Essentials
- Big Data Applications in Real World
- Data Science for Absolute Beginners
- Evolution & Impact of Big Data Analytics

𝐋𝐢𝐧𝐤 👇:- 

https://pdlink.in/4l3nFx0

🚀 Start Learning Now – 100% Free!

📜 Get Certified & Boost Your Career!

Data analytics offers excellent job prospects in 2025, with numerous opportunities across various industries.

Job Market Overview
Data analyst jobs are experiencing rapid growth, with an expected expansion in multiple sectors.

- High Demand Roles:
- Data Scientist
- Business Intelligence Analyst
- Financial Analyst
- Marketing Analyst
- Healthcare Data Analyst

Skills Required
Top skills for success in data analytics include:

- Technical Skills:
- Python and R programming
- SQL database management
- Data manipulation and cleaning
- Statistical analysis
- Power BI or Tableau
- Machine learning basics

Salary Expectations
Average salaries vary by role:
- Data Scientist: ~$122,738 per year
- Data Analyst: Around INR 6L per annum
- Entry-level Data Analyst: ~$83,011 annually[2]

Job Search Strategies

- Utilize job portals like LinkedIn, Indeed & telegram
- Attend industry conferences and webinars
- Network with professionals
- Check company career pages
- Consider recruitment agencies specializing in tech roles

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier

Like this post for if you want me to continue the interview series 👍♥️

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

Hope it helps :)

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗧𝗵𝗮𝘁 𝗚𝗲𝘁𝘀 𝗬𝗼𝘂 𝗛𝗶𝗿𝗲𝗱?😍

If you’re just starting out in data analytics and wondering how to stand out — real-world projects are the key📊

No recruiter is impressed by “just theory.” What they want to see? Actionable proof of your skills👨‍💻📌

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4ezeIc9

Show recruiters that you don’t just “know” tools — you use them to solve problems✅️

Complete SQL road map
👇👇

1.Intro to SQL
• Definition
• Purpose
• Relational DBs
• DBMS

2.Basic SQL Syntax
• SELECT
• FROM
• WHERE
• ORDER BY
• GROUP BY

3. Data Types
• Integer
• Floating-Point
• Character
• Date
• VARCHAR
• TEXT
• BLOB
• BOOLEAN

4.Sub languages
• DML
• DDL
• DQL
• DCL
• TCL

5. Data Manipulation
• INSERT
• UPDATE
• DELETE

6. Data Definition
• CREATE
• ALTER
• DROP
• Indexes

7.Query Filtering and Sorting
• WHERE
• AND
• OR Conditions
• Ascending
• Descending

8. Data Aggregation
• SUM
• AVG
• COUNT
• MIN
• MAX

9.Joins and Relationships
• INNER JOIN
• LEFT JOIN
• RIGHT JOIN
• Self-Joins
• Cross Joins
• FULL OUTER JOIN

10.Subqueries
• Subqueries used in
• Filtering data
• Aggregating data
• Joining tables
• Correlated Subqueries

11.Views
• Creating
• Modifying
• Dropping Views

12.Transactions
• ACID Properties
• COMMIT
• ROLLBACK
• SAVEPOINT
• ROLLBACK TO SAVEPOINT

13.Stored Procedures
• CREATE PROCEDURE
• ALTER PROCEDURE
• DROP PROCEDURE
• EXECUTE PROCEDURE
• User-Defined Functions (UDFs)

14.Triggers
• Trigger Events
• Trigger Execution and Syntax

15. Security and Permissions
• CREATE USER
• GRANT
• REVOKE
• ALTER USER
• DROP USER

16.Optimizations
• Indexing Strategies
• Query Optimization

17.Normalization
• 1NF(Normal Form)
• 2NF
• 3NF
• BCNF

18.Backup and Recovery
• Database Backups
• Point-in-Time Recovery

19.NoSQL Databases
• MongoDB
• Cassandra etc...
• Key differences

20. Data Integrity
• Primary Key
• Foreign Key

21.Advanced SQL Queries
• Window Functions
• Common Table Expressions (CTEs)

22.Full-Text Search
• Full-Text Indexes
• Search Optimization

23. Data Import and Export
• Importing Data
• Exporting Data (CSV, JSON)
• Using SQL Dump Files

24.Database Design
• Entity-Relationship Diagrams
• Normalization Techniques

25.Advanced Indexing
• Composite Indexes
• Covering Indexes

26.Database Transactions
• Savepoints
• Nested Transactions
• Two-Phase Commit Protocol

27.Performance Tuning
• Query Profiling and Analysis
• Query Cache Optimization

------------------ END -------------------

Some good resources to learn SQL

1.Tutorial & Courses
• Learn SQL: https://bit.ly/3FxxKPz
• Udacity: imp.i115008.net/AoAg7K

2. YouTube Channel's
• FreeCodeCamp:rb.gy/pprz73
• Programming with Mosh: rb.gy/g62hpe

3. Books
• SQL in a Nutshell: https://t.iss.one/DataAnalystInterview/158

4. SQL Interview Questions
https://t.iss.one/sqlanalyst/72?single

Join @free4unow_backup for more free resourses

ENJOY LEARNING 👍👍

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗪𝗲𝗯𝗶𝗻𝗮𝗿 | 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍 

A Guide to a Career in Data Science : Tools, Skills, and Career Fundamentals

- Learn how How MAANG Companies Use Data Science in Their Daily Business

- Get a step-by-step guide on how to start building the expertise companies are hiring for.

Eligibility :- Students,Freshers & Woking Professionals 

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-

https://pdlink.in/3TwjLjZ

(Limited Slots ..HurryUp🏃‍♂️ ) 

𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞:-  July 11, 2025 , at 7 PM

How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

Join for more: https://t.iss.one/machinelearning_deeplearning

Like for more ❤️

All the best 👍👍

SQL Basics for Data Analysts

SQL (Structured Query Language) is used to retrieve, manipulate, and analyze data stored in databases.

1️⃣ Understanding Databases & Tables

Databases store structured data in tables.

Tables contain rows (records) and columns (fields).

Each column has a specific data type (INTEGER, VARCHAR, DATE, etc.).

2️⃣ Basic SQL Commands

Let's start with some fundamental queries:

🔹 SELECT – Retrieve Data

SELECT * FROM employees; -- Fetch all columns from 'employees' table SELECT name, salary FROM employees; -- Fetch specific columns 

🔹 WHERE – Filter Data

SELECT * FROM employees WHERE department = 'Sales'; -- Filter by department SELECT * FROM employees WHERE salary > 50000; -- Filter by salary 

🔹 ORDER BY – Sort Data

SELECT * FROM employees ORDER BY salary DESC; -- Sort by salary (highest first) SELECT name, hire_date FROM employees ORDER BY hire_date ASC; -- Sort by hire date (oldest first) 

🔹 LIMIT – Restrict Number of Results

SELECT * FROM employees LIMIT 5; -- Fetch only 5 rows SELECT * FROM employees WHERE department = 'HR' LIMIT 10; -- Fetch first 10 HR employees 

🔹 DISTINCT – Remove Duplicates

SELECT DISTINCT department FROM employees; -- Show unique departments 

Mini Task for You: Try to write an SQL query to fetch the top 3 highest-paid employees from an "employees" table.

You can find free SQL Resources here
👇👇
https://t.iss.one/mysqldata

Like this post if you want me to continue covering all the topics! 👍❤️

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

Hope it helps :)

#sql

𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗝𝘂𝘀𝘁 𝗥𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻’𝘁 𝗠𝗶𝘀𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

🚨 Harvard just dropped 5 FREE online tech courses — no fees, no catches!📌

Whether you’re just starting out or upskilling for a tech career, this is your chance to learn from one of the world’s top universities — for FREE. 🌍

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4eA368I

💡Learn at your own pace, earn certificates, and boost your resume✅️

Amazon Data Analyst Interview Questions for 1-3 years of experience role :-

A. SQL:

1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location

Write an SQL query to find the name of the employee with the highest salary in each location.

2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date

Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.

B. Python:

1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).

2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)

2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.

C. Leadership or Situational Questions:

(Based on the leadership principle of Bias for Action)

- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?

(Based on the leadership principle of Dive Deep)

- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?

(Based on the leadership principle of Customer Obsession)

- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?

D. Excel:

Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.

I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://t.iss.one/DataSimplifier

Like if it helps :)

𝗟𝗲𝗮𝗿𝗻 𝗚𝗲𝗻𝗔𝗜 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 , 𝗘𝗮𝗿𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 & 𝗠𝗮𝗸𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗹𝗹𝗲𝗴𝗲 𝗜𝗻𝗱𝗶𝗮’𝘀 𝗔𝗜 𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻😍

Join the #GreatLearningAIChallenge | 🗓️ 13th–15th July

🎁 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗚𝗲𝘁:-
✅ Certificates worth ₹40,000 – Absolutely FREE
✅ Internship Opportunity at Great Learning
✅ Top 10 students from winning colleges get Third Wave Coffee vouchers ☕

🏆 More participants = Higher rank for your college!

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-

https://pdlink.in/4ksaynS

Get your classmates to join & win BIG together!🎓

Most people learn SQL just enough to pull some data. But if you really understand it, you can analyze massive datasets without touching Excel or Python.

Here are 8 game-changing SQL concepts that will make you a data pro:

👇

1. Stop pulling raw data. Start pulling insights.

The biggest mistake? Running a query that gives you everything and then filtering it later.

Good analysts don’t pull raw data. They shape the data before it even reaches them.

2. “SELECT ” is a rookie move.

Pulling all columns is lazy and slow.

A pro only selects what they need.
✔️ Fewer columns = Faster queries
✔️ Less noise = Clearer insights

The more precise your query, the less time you waste cleaning data.

3. GROUP BY is your best friend.

You don’t need 100,000 rows of transactions. What you need is:
✔️ Sales per region
✔️ Average order size per customer
✔️ Number of signups per month

Grouping turns chaotic data into useful summaries.

4. Joins = Connecting the dots.

Your most important data is split across multiple tables.

Want to know how much each customer spent? You need to join:
✔️ Customer info
✔️ Order history
✔️ Payments

Joins = unlocking hidden insights.

5. Window functions will blow your mind.

They let you:
✔️ Rank customers by total purchases
✔️ Calculate rolling averages
✔️ Compare each row to the overall trend

It’s like pivot tables, but way more powerful.

6. CTEs will save you from spaghetti SQL.

Instead of writing a 50-line nested query, break it into steps.

CTEs (Common Table Expressions) make your SQL:
✔️ Easier to read
✔️ Easier to debug
✔️ Reusable

Good SQL is clean SQL.

7. Indexes = Speed.

If your queries take forever, your database is probably doing unnecessary work.

Indexes help databases find data faster.

If you work with large datasets, this is a game changer.

SQL isn’t just about pulling data. It’s about analyzing, transforming, and optimizing it.

Master these 7 concepts, and you’ll never look at SQL the same way again.

Join us on WhatsApp: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v