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Package : 10-16 Lakh

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🖥 Website To Learn Programming & Data Analytics

1. Learn HTML :- html.com
2. Learn CSS :- css-tricks.com
3. Learn Tailwind CSS :- tailwindcss.com
4. Learn JavaScript :- imp.i115008.net/mgGagX
5. Learn Bootstrap :- getbootstrap.com
6. Learn DSA :- t.me/dsabooks
7. Learn Git :- git-scm.com
8. Learn React :- react-tutorial.app
9. Learn API :- rapidapi.com/learn
10. Learn Python :- t.me/pythondevelopersindia
11. Learn SQL :- t.me/sqlspecialist
12. Learn Web3 :- learnweb3.io
13. Learn JQuery :- learn.jquery.com
14. Learn ExpressJS :- expressjs.com
15. Learn NodeJS :- nodejs.dev/learn
16. Learn MongoDB :- learn.mongodb.com
17. Learn PHP :- phptherightway.com/
18. Learn Golang :- learn-golang.org/
19. Learn Power BI :- t.me/powerbi_analyst
20. Learn Data Analytics:- datasimplifier.com

ENJOY LEARNING 👍👍

Data Science Question and Answer!!

Hope this helps !!

Do react for more post like these!! ➡️➡️📖

🐍 PySpark vs Pandas
1. Data Handling
✅Pandas:

Best for small to medium-sized datasets.
Works on a single machine (in-memory processing).
Suitable for datasets that fit in memory.
✅PySpark:

Designed for large-scale data processing.
Can handle big datasets that don’t fit in memory (distributed processing).
Works across multiple machines (clusters).
2. Performance
✅Pandas:

Faster for small datasets (single-machine operations).
May slow down with very large datasets.
✅PySpark:

Faster for large datasets (distributed computing).
Optimized for parallel processing.
3. Ease of Use
✅Pandas:

Simple and easy to use for data manipulation and analysis.
Rich set of functions and operations.
✅PySpark:

More complex and requires setup (cluster, Spark context).
Similar operations to Pandas, but for distributed data.

Hope this helps !!

Do react for more post like these!! ➡️➡️📖

What is Apache Spark and Where to learn them?


Apache Spark is a powerful distributed data processing framework used for big data and machine learning tasks. Here are some excellent resources to learn Apache Spark, catering to various levels of expertise:

1. Follow - Apache Spark Official Documentation

- Great starting point with detailed tutorials and guides.
- Covers installation, core concepts, and APIs for Scala, Python (PySpark), Java, and R.

2. YouTube Tutorials

- Free video tutorials by channels like Simplilearn or Data Engineering Simplified.

3. Coursera and edX Courses

- Coursera: Big Data Analysis with Scala and Spark (offered by École Polytechnique Fédérale de Lausanne).
- edX: Introduction to Big Data with Apache Spark (offered by UC Berkeley).

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