Capgemini is hiring!
Position: HR Analyst
Qualification: Bachelor’s/ Master’s/ MBA
Salary: 4 - 6 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore; Kolkata, India

📌Apply Now: https://careers.capgemini.com/job/Bangalore-HR-Operational-Excellence-Analyst-A/1134863701/

https://careers.capgemini.com/job/Kolkata-HR-Global-Shared-Services-Analyst-A/1153641201/

✅ Hope this helps !!

Do react for more post like these !! ➡️➡️

Here are some SQL project ideas tailored for data analysis:

🔟 SQL Project Ideas for Data Analysts

1. Sales Database Analysis: Create a database to track sales transactions. Write SQL queries to analyze sales performance by product, region, and time period.

2. Customer Churn Analysis: Build a database with customer data and track churn rates. Use SQL to identify factors contributing to churn and segment customers.

3. E-commerce Order Tracking: Design a database for an e-commerce platform. Write queries to analyze order trends, average order value, and customer purchase history.

4. Employee Performance Metrics: Create a database for employee records and performance reviews. Analyze employee performance trends and identify high performers using SQL.

5. Inventory Management System: Set up a database to track inventory levels. Write SQL queries to monitor stock levels, identify slow-moving items, and generate restock reports.

6. Healthcare Patient Analysis: Build a database to manage patient records and treatments. Use SQL to analyze treatment outcomes, readmission rates, and patient demographics.

7. Social Media Engagement Analysis: Create a database to track user interactions on a social media platform. Write queries to analyze engagement metrics like likes, shares, and comments.

8. Financial Transaction Analysis: Set up a database for financial transactions. Use SQL to identify spending patterns, categorize expenses, and generate monthly financial reports.

9. Website Traffic Analysis: Build a database to track website visitors. Write queries to analyze traffic sources, user behavior, and page performance.

10. Survey Results Analysis: Create a database to store survey responses. Use SQL to analyze responses, identify trends, and visualize findings based on demographic data.

Here you can find essential SQL Interview Resources👇
https://topmate.io/codingdidi

Like this post if you need more 👍❤️

Hope it helps :)

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:
🔗 https://freecoderzone.blogspot.com/2025/01/7000-free-courses.html

📘 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
🔗 https://www.coursera.org/professional-certificates/ibm-data-warehouse-engineer

9️⃣ IBM Cybersecurity Analyst Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-cybersecurity-analyst

🔟 IBM AI Engineering Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ai-engineering

1️⃣1️⃣ IBM DevOps and Software Engineering Professional Certificate
🔗 https://www.coursera.org/professional-certificates/ibm-devops-and-software-engineering


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:)