60 Most Asked Interview Question

Do react if you found this helpful.

Leetcode Questions and Solution.

Do react with 😍, if you found this helpful.

"📊 Data Cleaning with Python 🐍

Credits to the original author for this amazing resource! 🙌
Sharing it with the community to help you master the essential concepts of data cleaning. 🌟

Let’s learn and grow together! 💡

Do React with 🤩, if you found this helpful.

Learning Python has never been this engaging! 🍔🍟🧋

👉 Learn Python ZERO TO HERO
🔥 7000+ Free Courses | Free Access:
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📘 Top Python Learning Resources:

1️⃣ Python for Everybody Specialization
🔗 https://www.coursera.org/specializations/python

2️⃣ Crash Course on Python
🔗 https://www.coursera.org/learn/python-crash-course

3️⃣ Get Started with Python
🔗 https://developer.mozilla.org/en-US/docs/Learn/Server-side/Python/Introduction

4️⃣ Python for Data Science, AI & Development
🔗 https://www.edx.org/course/python-for-data-science-ai-development

5️⃣ Google Data Analytics
🔗 https://www.coursera.org/professional-certificates/google-data-analytics

6️⃣ Google Advanced Data Analytics
🔗 https://www.coursera.org/professional-certificates/google-advanced-data-analytics

7️⃣ IBM Data Science Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-data-science

8️⃣ IBM Data Warehouse Engineer Professional Certificate
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1️⃣1️⃣ IBM DevOps and Software Engineering Professional Certificate
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Let’s make Python learning fun and interactive! 🚀
#Python #LearnPython #Programming #CodingSkills #DataScience

Don't Limit Yourself to Just One Title, "𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭" in Your Job Search!


Don't get caught up in the confines of a single job title! There are countless roles out there that might align perfectly with your skills and interests. Here are a few alternative titles for data analyst roles to broaden your search horizons:


1. QI Analyst
2. Risk Analyst
3. Data Modeler
4. Research Analyst
5. Business Analyst
6. Reporting Analyst
7. Operations Analyst
8. Social Media Analyst
9. Statistical Analyst
10. Statistical Analyst
11. Product Data Analyst
12. Analytics Engineer
13. Supply Chain Analyst
14. Data Mining Engineer
15. Data Science Associate
16. Financial Data Analyst
17. Cybersecurity Analyst
18. Marketing Data Analyst
19. Quantitative Analyst
20. HR Analytics Specialist
21. Decision Support Analyst
22. Machine Learning Analyst
23. Fraud Detection Analyst
24. Healthcare Data Analyst
25. Data Insights Specialist
26. Data Visualization Specialist
27. Customer Insights Analyst
28. Business Intelligence Analyst
29. Predictive Analytics Analyst

Remember, the right opportunity might be hiding behind a different title than you expect. Keep an open mind and explore all avenues in your job search journey!

Also, there might be fewer applicants for these roles as many don't search for titles other than data Analyst or Business Analyst. Maybe you can get more calls or interviews this way.

You don't have to try all the titles, filter out based on your interests and skills!

After all, 𝐉𝐨𝐛 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞!! 😉

like for more❤️

Societe Generale is hiring!
Position: Analyst
Qualifications: Bachelor’s/ Master's Degree
Salary: 4 - 7 LPA (Expected)
Experience: Entry Level
Location: Bangalore, India (Hybrid)

📌Apply Now: https://careers.societegenerale.com/en/job-offers/analyst-24000PV0-en?src=JB-14381

7 Best GitHub Repositories to Break into Data Analytics and Data Science

If you're diving into data science or data analytics, these repositories will give you the edge you need. Check them out:

1️⃣ 100-Days-Of-ML-Code
🔗 https://github.com/Avik-Jain/100-Days-Of-ML-Code
⭐️ Stars: ~42k

2️⃣ awesome-datascience
🔗 https://github.com/academic/awesome-datascience
⭐️ Stars: ~22.7k

3️⃣ Data-Science-For-Beginners
🔗 https://github.com/microsoft/Data-Science-For-Beginners
⭐️ Stars: ~14.5k

4️⃣ data-science-interviews
🔗 https://github.com/alexeygrigorev/data-science-interviews
⭐️ Stars: ~5.8k

5️⃣ Coding and ML System Design
🔗 https://github.com/weeeBox/coding-and-ml-system-design
⭐️ Stars: ~3.5k

6️⃣ Machine Learning Interviews from MAANG
🔗 https://github.com/arunkumarpillai/Machine-Learning-Interviews
⭐️ Stars: ~8.1k

7️⃣ data-science-ipython-notebooks
🔗 https://github.com/donnemartin/data-science-ipython-notebooks
⭐️ Stars: ~27.2k

Explore these amazing resources and take your data science journey to the next level! 🚀
#DataScience #DataAnalytics #GitHub #MachineLearning #CodingSkills

if you're a data analyst.

you need to clean data as your job

This is how you should learn data cleaning for 2025:
✅Learn how to handle missing values
✅Learn data normalization and standardization
✅Learn to remove duplicates
✅Learn how to handle outliers
✅Learn how to merge and join datasets
✅Learn to identify and correct data inconsistencies

Data cleaning is an essential step to make your analysis meaningful.

THOMSON REUTERS is Hiring for DATA ENGINEER

Role:- DATA ENGINEER

Qualifications:- GRADUATION

Experience:- Fresher's and Experienced

Mode:- WORK FROM OFFICE

CTC:- 15 LPA

Location:- BANGALORE, KARNATAKA & HYDERABAD, TELANGANA

Apply Now:- https://careers.thomsonreuters.com/us/en/job/THTTRUUSJREQ185931EXTERNALENUS/Data-Engineer

1. Handling Missing Values
- Kaggle Tutorial: [Handling Missing Values](https://www.kaggle.com/learn/data-cleaning)
- YouTube Video: ["Dealing with Missing Data in Python"](https://www.youtube.com/watch?v=wvsE8jm1GzE) by Data School
- Blog Post: [Complete Guide to Handling Missing Data in Python](https://towardsdatascience.com/complete-guide-to-handling-missing-data-in-python-95c1221fba0e)

---

2. Data Normalization and Standardization
- Blog Post: [Normalization vs. Standardization Explained](https://machinelearningmastery.com/normalize-standardize-machine-learning-data-python/)
- Interactive Course: [Feature Scaling and Normalization (DataCamp)](https://www.datacamp.com/)
- YouTube Video: ["Feature Scaling in Machine Learning"](https://www.youtube.com/watch?v=UvK0B5JZpM8) by StatQuest

---

3. Removing Duplicates
- Official Pandas Documentation: [Pandas drop_duplicates()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html)
- Video: ["Removing Duplicates in Python"](https://www.youtube.com/watch?v=QHRrl4Il2Og) by Corey Schafer
- Blog Post: [How to Remove Duplicates Using Python](https://realpython.com/python-data-cleaning-numpy-pandas/)

---

4. Handling Outliers
- Blog Post: [5 Methods to Deal with Outliers in Data](https://towardsdatascience.com/handling-outliers-in-your-data-7cde6b4d76bb)
- Video: ["Identifying and Handling Outliers in Python"](https://www.youtube.com/watch?v=TN-xVNUcDk8) by Krish Naik
- Jupyter Notebook Example: [Outlier Detection with Python](https://github.com/datascience-projects)

---

5. Merging and Joining Datasets
- Pandas Documentation: [Merging, Joining, and Concatenating](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html)
- Video: ["Pandas Merging and Joining"](https://www.youtube.com/watch?v=g7n1MKo7WgQ) by Corey Schafer
- Interactive Course: [Data Manipulation with Pandas (DataCamp)](https://www.datacamp.com/)

---

6. Identifying and Correcting Data Inconsistencies
- Blog Post: [Python for Data Cleaning](https://towardsdatascience.com/python-for-data-cleaning-a-step-by-step-guide-to-deal-with-data-inconsistencies-c08f06fca8c8)
- Video Tutorial: ["Python for Data Cleaning"](https://www.youtube.com/watch?v=B_L0v1xRb6E)
- Project-Based Learning: [Data Cleaning in Python Mini-Projects](https://github.com/topics/data-cleaning)

---

💡 Pro Tip: Practice real-world data cleaning tasks using open datasets on platforms like:
- [Kaggle Datasets](https://www.kaggle.com/datasets)
- [Data World](https://data.world/)
- [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)

𝐑𝐞𝐯𝐨𝐥𝐮𝐭 - 𝐖𝐨𝐫𝐤 𝐅𝐫𝐨𝐦 𝐇𝐨𝐦𝐞
Position: Business Analyst
Qualification: Bachelor's/ Master's Degree
Salary: 5 - 8 LPA (Expected)
Experience: Freshers/ Experienced
Location: Work From Home (Remote)

📌Apply Now: https://www.revolut.com/careers/position/75445311-c0e0-4b6d-90d4-d4a23226831c/

Atlassian is hiring!
Position: Data Engineer, Analytics & Data Science
Qualifications: Bachelor’s/ Master’s Degree
Salary: 15 - 21 LPA (Expected)
Experience: 1 - 2 (Years)
Location: Bengaluru, India

📌Apply Now: https://careers-apac-atlassian.icims.com/jobs/17667/data-engineer/job?iis=LinkedIn&iisn=LinkedIn_Job_Ad&mobile=false&width=1215&height=500&bga=true&needsRedirect=false&jan1offset=330&jun1offset=330

Like for more ❤️

100 Data science concept

*Python*
✅ High-Level & Easy to Learn – Simple syntax, readable code, and beginner-friendly.

✅ Interpreted Language – No need for compilation; executed line by line.

✅ Dynamically Typed – No need to declare variable types; Python determines them at runtime.

✅ Versatile & Multi-Purpose – Used in Web Development, Data Science, AI, ML, Automation, Cybersecurity, and more.

✅ Extensive Libraries & Frameworks – Supports TensorFlow, NumPy, Pandas, Flask, Django, OpenCV, etc.

✅ Object-Oriented & Functional – Supports both OOP and functional programming paradigms.

✅ Cross-Platform – Runs on Windows, macOS, Linux, and even mobile devices.

✅ Large Community & Support – One of the most widely used languages with extensive documentation and forums.

✅ Automation & Scripting – Ideal for task automation, web scraping, and workflow management.

✅ Strong Integration – Works with C, C++, Java, SQL, and various APIs.

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 👍♥️


Hope it helps:)

🚀 Master Data Visualization & Charts for Data Analysis!

Want to make your data analysis more insightful with stunning visualizations? 📊✨ Learn how to use charts, graphs, and dashboards to uncover hidden patterns and tell compelling data stories!

Turn your raw data into clear, meaningful, and powerful visual insights today! 🔥

Day 1: Introduction to SQL and Relational Databases

Welcome to Day 1 of your SQL learning journey! 🚀

SQL (Structured Query Language) is the language of databases and is widely used for managing and analyzing data. Whether you're aiming for a career in Data Science, Data Analysis, Web Development, or Finance, SQL is an essential skill to master.

---

📌 What is SQL?
SQL stands for Structured Query Language and is used to communicate with databases. It helps you store, retrieve, manipulate, and manage data in a structured way.

Imagine a library database where details of books (title, author, genre, availability) are stored in tables. With SQL, you can:
✅ Find a book by its title
✅ Get a list of all books by a specific author
✅ Check how many books are available in a genre
✅ Add new books to the library collection
✅ Delete outdated records

---

📌 What is a Database?
A database is an organized collection of data that allows easy access, management, and updating.

There are two main types of databases:
1️⃣ Relational Databases (RDBMS): Data is stored in tables (like Excel). Examples: MySQL, PostgreSQL, SQL Server, SQLite.
2️⃣ Non-Relational Databases (NoSQL): Data is stored in key-value pairs, JSON, or documents. Examples: MongoDB, Firebase.

SQL works with Relational Databases (RDBMS).

---
📌 What is a Table?
A table is like a spreadsheet in Excel, with rows and columns.

📌 Example: A simple "Students" table

| Student_ID | Name | Age | Grade |
|------------|-------|-----|--------|
| 1 | Alex | 18 | A |
| 2 | Emma | 19 | B |
| 3 | John | 18 | A |

Each row represents a student, and each column stores a specific type of data (ID, Name, Age, Grade).

---

📌 SQL Commands Overview
SQL has different types of commands to manage data:

🔹 Data Query Language (DQL) – Used for fetching data
🔸 SELECT – Retrieve data from tables

🔹 Data Manipulation Language (DML) – Used for modifying data
🔸 INSERT – Add new data
🔸 UPDATE – Modify existing data
🔸 DELETE – Remove data

🔹 Data Definition Language (DDL) – Used for creating and modifying tables
🔸 CREATE TABLE – Create a new table
🔸 ALTER TABLE – Modify table structure
🔸 DROP TABLE – Delete a table

🔹 Data Control Language (DCL) – Used for user permissions
🔸 GRANT – Give permissions
🔸 REVOKE – Remove permissions

🔹 Transaction Control Language (TCL) – Used for managing transactions
🔸 COMMIT – Save changes permanently
🔸 ROLLBACK – Undo changes

---

📌 Setting Up a Database
To practice SQL, you need a database environment. You can use:

1️⃣ Online SQL Editors – No installation needed
- [SQL Fiddle](http://sqlfiddle.com/)
- [W3Schools SQL Editor](https://www.w3schools.com/sql/trysql.asp?filename=trysql_select_all)

2️⃣ Install a Database Software
- MySQL (Popular for beginners) – [Download](https://www.mysql.com/downloads/)
- PostgreSQL (Advanced features) – [Download](https://www.postgresql.org/download/)
- SQL Server (Used in enterprises) – [Download](https://www.microsoft.com/en-us/sql-server/sql-server-downloads)

---

📌 Writing Your First SQL Query
Let’s retrieve all data from a Students table using the SELECT statement.

SELECT * FROM Students;

Explanation:
- SELECT → Tells SQL to retrieve data
- * → Means "all columns"
- FROM Students → Specifies the table name

---
🎯 Task for Today
1️⃣ Understand the basic SQL commands mentioned above.
2️⃣ Try an online SQL editor and execute SELECT * FROM Students;
3️⃣ Install MySQL or PostgreSQL (optional, but recommended).

---

📌 That’s it for Day 1! Tomorrow, we’ll learn about SQL Data Types and start writing basic queries. 🚀

💡 Like & comment if you're excited to continue this series! 😊❤️

Day 2: SQL Data Types and Writing Basic Queries

Welcome to Day 2 of your SQL learning journey! 🎉

Today, we’ll cover two important topics:
✅ Data Types in SQL – Understanding different types of data in a database
✅ Writing Basic SQL Queries – Learning how to retrieve data using SQL

By the end of this lesson, you’ll be able to create a table and write your first SQL query to fetch data! 🚀

---

📌 What Are Data Types in SQL?
A data type defines the kind of data a column can store. Each column in a table must have a specific data type to ensure that data is stored correctly.

For example, a "Name" column should store text, while an "Age" column should store numbers.

---

📌 Common SQL Data Types (MySQL, PostgreSQL, SQL Server)

1️⃣ Numeric Data Types (Used for storing numbers)
| Data Type | Description | Example |
|-----------|------------|---------|
| INT | Stores whole numbers | 25, 100, -50 |
| DECIMAL(p, s) | Stores decimal numbers with precision | 12.34, 99.99 |
| FLOAT / REAL | Stores floating-point numbers | 1.23, 3.14 |

🔹 Example: If you’re storing student ages, use INT. If you’re storing bank balances, use DECIMAL.

---

2️⃣ String (Text) Data Types
| Data Type | Description | Example |
|-----------|------------|---------|
| CHAR(n) | Fixed-length string (n characters) | 'A', 'USA' |
| VARCHAR(n) | Variable-length string (up to n characters) | 'John Doe', 'Hello World' |
| TEXT | Large text data | 'This is a long description...' |

🔹 Example: A column storing names should be VARCHAR(50), meaning it can hold up to 50 characters.

---

3️⃣ Date and Time Data Types
| Data Type | Description | Example |
|-----------|------------|---------|
| DATE | Stores only the date (YYYY-MM-DD) | '2025-02-02' |
| TIME | Stores only the time (HH:MM:SS) | '14:30:00' |
| DATETIME | Stores both date and time | '2025-02-02 14:30:00' |
| TIMESTAMP | Stores date & time (used for logging) | '2025-02-02 14:30:00' |

🔹 Example: If you’re storing a student’s birth date, use DATE. If you’re storing when a student logged in, use DATETIME.

---

📌 Creating a Table in SQL
Now that we understand data types, let’s create a table for our students.

CREATE TABLE Students (
    Student_ID INT PRIMARY KEY,
    Name VARCHAR(50),
    Age INT,
    Grade CHAR(1),
    Enrollment_Date DATE
);

🔹 Explanation:
- CREATE TABLE Students → Creates a table named Students
- Student_ID INT PRIMARY KEY → Unique ID for each student
- Name VARCHAR(50) → Stores student names (up to 50 characters)
- Age INT → Stores student ages
- Grade CHAR(1) → Stores single-character grades (A, B, C...)
- Enrollment_Date DATE → Stores the date of enrollment

---

📌 Writing Basic SQL Queries
Now, let’s insert some data and retrieve it using SQL queries.

1️⃣ Inserting Data into a Table

INSERT INTO Students (Student_ID, Name, Age, Grade, Enrollment_Date)
VALUES (1, 'John Doe', 18, 'A', '2023-09-01');

🔹 Explanation:
- INSERT INTO Students → Adds a new record to the Students table
- (Student_ID, Name, Age, Grade, Enrollment_Date) → Specifies the columns
- VALUES (1, 'John Doe', 18, 'A', '2023-09-01') → Provides the values

Now, let’s add a few more students:

INSERT INTO Students (Student_ID, Name, Age, Grade, Enrollment_Date) 
VALUES 
(2, 'Emma Smith', 19, 'B', '2022-08-15'),
(3, 'Alex Brown', 20, 'A', '2021-07-10'),
(4, 'Sophia Johnson', 18, 'C', '2023-01-20');

---

2️⃣ Retrieving Data Using `SELECT` Statement
To see all student records, use:

SELECT * FROM Students;

🔹 Explanation:
- SELECT * → Selects all columns
- FROM Students → Specifies the Students table

🔹 Output:
| Student_ID | Name | Age | Grade | Enrollment_Date |
|------------|--------------|-----|-------|----------------|
| 1 | John Doe | 18 | A | 2023-09-01 |
| 2 | Emma Smith | 19 | B | 2022-08-15 |
| 3 | Alex Brown | 20 | A | 2021-07-10 |
| 4 | Sophia Johnson| 18 | C | 2023-01-20 |

---