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1. Python Programming:
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2. Coding Resources:
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3. Coding Projects:
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4. Coding Interviews:
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5. Java Programming:
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6. Javascript:
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7. Web Development:
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8. Artificial Intelligence:
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9. Data Science:
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10. Machine Learning:
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11. SQL:
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12. GitHub:
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🔰 Python List Slicing

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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Python Project Ideas: https://t.me/dsabooks/85

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Best Data Science & Machine Learning Resources

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Python Interview Resources

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Python Basics Arrays & Loops 🐍

Essential you need to start strong 💪

If you work with Python, remember a simple rule: do not modify a list while iterating over it. 🐍🛑 This can lead to unexpected results because the iterator does not track structural changes.

Here is an example that looks logical but works incorrectly: 🤔

items = [1, 2, 2, 3, 4]
for item in items:
    if item == 2:
        items.remove(item)
print(items)
# Output: [1, 2, 3, 4]

It seems that all 2s should disappear, but one remains. ❓ Why?

After removing an element, the list shifts, but the loop moves on — as a result, some values are simply skipped. 🔄🚫

How to do it correctly — iterate over a copy: ✅

for item in items[:]:
    if item == 2:
          items.remove(item)
print(items)
# Output: [1, 3, 4]

Even better — use list comprehension: 🚀

items = [x for x in items if x != 2]

Conclusion: 🏁 do not modify a collection during iteration. This can lead to skipped elements, duplication, or even errors during execution. 🛠️🚧

#Python #Coding #Programming #Debugging #TechTips #PythonTips

🔰 Python functions

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐞𝐥𝐭 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐟𝐢𝐫𝐬𝐭, 𝐛𝐮𝐭 𝐭𝐡𝐞𝐬𝐞 𝟗 𝐬𝐭𝐞𝐩𝐬 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠!
.
.
1️⃣ 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: Started with foundational Python concepts like variables, loops, functions, and conditional statements.

2️⃣ 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐝 𝐄𝐚𝐬𝐲 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Focused on beginner-friendly problems on platforms like LeetCode and HackerRank to build confidence.

3️⃣ 𝐅𝐨𝐥𝐥𝐨𝐰𝐞𝐝 𝐏𝐲𝐭𝐡𝐨𝐧-𝐒𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: Studied essential problem-solving techniques for Python, like list comprehensions, dictionary manipulations, and lambda functions.

4️⃣ 𝐋𝐞𝐚𝐫𝐧𝐞𝐝 𝐊𝐞𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Explored popular libraries like Pandas, NumPy, and Matplotlib for data manipulation, analysis, and visualization.

5️⃣ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Built small projects like a to-do app, calculator, or data visualization dashboard to apply concepts.

6️⃣ 𝐖𝐚𝐭𝐜𝐡𝐞𝐝 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥𝐬: Followed creators like CodeWithHarry and Shradha Khapra for in-depth Python tutorials.

7️⃣ 𝐃𝐞𝐛𝐮𝐠𝐠𝐞𝐝 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲: Made it a habit to debug and analyze code to understand errors and optimize solutions.

8️⃣ 𝐉𝐨𝐢𝐧𝐞𝐝 𝐌𝐨𝐜𝐤 𝐂𝐨𝐝𝐢𝐧𝐠 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Participated in coding challenges to simulate real-world problem-solving scenarios.

9️⃣ 𝐒𝐭𝐚𝐲𝐞𝐝 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭: Practiced daily, worked on diverse problems, and never skipped Python for more than a day.

I have curated the best interview resources to crack Python Interviews 👇👇
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#Python

7 GitHub repos to master AI engineering in 2026 👇


1/ Awesome Artificial Intelligence:
https://github.com/owainlewis/awesome-artificial-intelligence

2/ Awesome LLM Apps:
https://github.com/Shubhamsaboo/awesome-llm-apps

3/ 100 Days of ML Code:
https://github.com/avik-jain/100-Days-of-ML-Code

4/ System Prompts and AI Tools:
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools

5/ AI Agents for Beginners:
https://github.com/microsoft/ai-agents-for-beginners

6/ Microsoft Gen AI for Beginners:
https://github.com/microsoft/ai-for-beginners

7/ Learn Agentic AI:
https://github.com/panaversity/learn-agentic-ai

15 Best Project Ideas for Python : 🐍

🚀 Beginner Level:
1. Simple Calculator
2. To-Do List
3. Number Guessing Game
4. Dice Rolling Simulator
5. Word Counter

🌟 Intermediate Level:
6. Weather App
7. URL Shortener
8. Movie Recommender System
9. Chatbot
10. Image Caption Generator

🌌 Advanced Level:
11. Stock Market Analysis
12. Autonomous Drone Control
13. Music Genre Classification
14. Real-Time Object Detection
15. Natural Language Processing (NLP) Sentiment Analysis

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Here are some tricky🧩 SQL interview questions!

1. Find the second-highest salary in a table without using LIMIT or TOP.

2. Write a SQL query to find all employees who earn more than their managers.

3. Find the duplicate rows in a table without using GROUP BY.

4. Write a SQL query to find the top 10% of earners in a table.

5. Find the cumulative sum of a column in a table.

6. Write a SQL query to find all employees who have never taken a leave.

7. Find the difference between the current row and the next row in a table.

8. Write a SQL query to find all departments with more than one employee.

9. Find the maximum value of a column for each group without using GROUP BY.

10. Write a SQL query to find all employees who have taken more than 3 leaves in a month.

These questions are designed to test your SQL skills, including your ability to write efficient queries, think creatively, and solve complex problems.

Here are the answers to these questions:

1. SELECT MAX(salary) FROM table WHERE salary NOT IN (SELECT MAX(salary) FROM table)

2. SELECT e1.* FROM employees e1 JOIN employees e2 ON e1.manager_id = (link unavailable) WHERE e1.salary > e2.salary

3. SELECT * FROM table WHERE rowid IN (SELECT rowid FROM table GROUP BY column HAVING COUNT(*) > 1)

4. SELECT * FROM table WHERE salary > (SELECT PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY salary) FROM table)

5. SELECT column, SUM(column) OVER (ORDER BY rowid) FROM table

6. SELECT * FROM employees WHERE id NOT IN (SELECT employee_id FROM leaves)

7. SELECT *, column - LEAD(column) OVER (ORDER BY rowid) FROM table

8. SELECT department FROM employees GROUP BY department HAVING COUNT(*) > 1

9. SELECT MAX(column) FROM table WHERE column NOT IN (SELECT MAX(column) FROM table GROUP BY group_column)

Here you can find essential SQL Interview Resources👇
https://t.me/mysqldata

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🔰 Comprehensions in python with example

✅ Complete Roadmap to Learn Python Programming 🐍💻

Week 1: Python Basics
• Install Python and VS Code
• Learn variables, data types, input, output
• Practice arithmetic and string operations
• Write 10 small programs
Example: Calculator, temperature converter

Week 2: Control Flow
• Learn if, else, elif
• Learn for and while loops
• Use break and continue
• Solve 20 logic problems
Example: Number guessing game

Week 3: Data Structures
• Lists, tuples, sets, dictionaries
• Indexing, slicing, methods
• Loop through collections
• Solve real problems
Example: Student marks analysis

Week 4: Functions and Modules
• Define functions
• Use parameters and return values
• Learn lambda functions
• Import built-in modules
Example: Reusable math utility

Week 5: Strings and File Handling
• String methods and formatting
• Read and write files
• Handle CSV and text files
• Build small file-based programs
Example: Log file analyzer

Week 6: Error Handling and Debugging
• Learn try, except, finally
• Understand common errors
• Use print and debugger
• Fix broken programs
Example: Robust input validator

Week 7: Object-Oriented Programming
• Classes and objects
• Constructors and methods
• Inheritance and encapsulation
• Build simple class-based apps
Example: Bank account system

Week 8: Standard Libraries
• datetime, math, random
• os and sys basics
• Work with JSON
• Write utility scripts
Example: Automated folder organizer

Week 9: Working with External Packages
• Learn pip and virtual environments
• Use requests library
• Basic API calls
• Handle API responses
Example: Weather app using API

Week 10: Data Handling Basics
• Intro to NumPy
• Intro to Pandas
• Read CSV and Excel files
• Basic data cleaning
Example: Sales data summary

Week 11: Mini Projects
• Build 2 small projects
• Focus on logic and structure
• Write clean, readable code
Examples:
• To-do list app
• Expense tracker

Week 12: Final Project and Revision
• Build one end-to-end project
• Revise core concepts
• Practice interview-style questions
Example projects:
• Simple automation tool
• Data analysis mini project

Daily Rule for You:
• Code at least 60 minutes
• Solve 5 problems daily
• Rewrite old code weekly

Double Tap ♥️ For Detailed Explanation

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🔰 Comprehensions in python with example