Codehub
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📈 Аналитический обзор Telegram-канала Codehub
Канал Codehub (@pythonadvisorai) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 32 524 подписчиков, занимая 4 014 место в категории Технологии и приложения и 1 025 место в регионе Малайзия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 32 524 подписчиков.
Согласно последним данным от 25 августа, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило -446, а за последние 24 часа — -9, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.42%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 0 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Free Programming resources.”
Благодаря высокой частоте обновлений (последние данные получены 26 августа, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
32 524
Подписчики
-924 часа
-857 дней
-44630 день
Архив постов
32 520
Repost from N/a
Top 50 Data Analyst Interview Questions (2025) 🎯📊
1. What does a data analyst do?
2. Difference between data analyst, data scientist, and data engineer.
3. What are the key skills every data analyst must have?
4. Explain the data analysis process.
5. What is data wrangling or data cleaning?
6. How do you handle missing values?
7. What is the difference between structured and unstructured data?
8. How do you remove duplicates in a dataset?
9. What are the most common data types in Python or SQL?
10. What is the difference between INNER JOIN and LEFT JOIN?
11. Explain the concept of normalization in databases.
12. What are measures of central tendency?
13. What is standard deviation and why is it important?
14. Difference between variance and covariance.
15. What are outliers and how do you treat them?
16. What is hypothesis testing?
17. Explain p-value in simple terms.
18. What is correlation vs. causation?
19. How do you explain insights from a dashboard to non-technical stakeholders?
20. What tools do you use for data visualization?
21. Difference between Tableau and Power BI.
22. What is a pivot table?
23. How do you build a dashboard from scratch?
49. What do you do if data contradicts business intuition?
50. What are your favorite analytics tools and why?
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💡 25 VS Code Extensions Every Dev Should Try 💻
✅ Prettier
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✅ ChatGPT - CodeGPT
🔥 React “❤️” if you use VS Code daily!
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***
✅ *Python Logic Building Interview Question* 🧠🐍
You have a list of numbers:
nums = [3, 5, 7, 9, 12, 17, 20, 21]*Question:* Find and print all numbers in the list that are prime. *Expected Output:*
[3, 5, 7, 17]*Python Code:*
def is_prime(n):
if n < 2:
return False
for i in range(2, int(n**0.5) + 1):
if n % i == 0:
return False
return True
prime_nums = [n for n in nums if is_prime(n)]
print(prime_nums)
is_prime() logic
– Uses list comprehension for concise filtering
– Prints list of all prime numbers
💬 *Tap ❤️ for more logic-building questions!*32 520
✅ *Python Scenario-Based Interview Question* 🧠🐍
You have a list:
numbers = [1, 2, 3, 2, 4, 1, 5, 2]*Question:* Find the number that appears most frequently in the list. *Expected Output:*
2*Python Code:*
from collections import Counter
most_common_num = Counter(numbers).most_common(1)[0][0]
print(most_common_num)
Counter() counts occurrences of each element
– most_common(1) returns the most frequent item
– Access [0][0] to get just the number
💬 *Tap ❤️ for more bite-sized Python tips!*
***
Would you like the next one to be slightly more advanced (e.g., involving strings or list comprehensions)?32 520
*Free Data Analytics Webinar + Certificate by HCL GUVI: an HCL Group company.* 💻🚀
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32 520
🐍 *How to Learn Python Programming in 2025 – Step by Step* 💻✨
✅ *Tip 1: Start with the Basics*
Learn Python fundamentals:
• Variables & Data Types (int, float, str, list, dict)
• Loops (`for`,
while`) & Conditionals (`if, `else`)
• Functions & Modules
✅ *Tip 2: Practice Small Programs*
Build mini-projects to reinforce concepts:
• Calculator
• To-do app
• Dice roller
• Guess-the-number game
✅ *Tip 3: Understand Data Structures*
• Lists, Tuples, Sets, Dictionaries
• How to manipulate, search, and iterate
✅ *Tip 4: Learn File Handling & Libraries*
• Read/write files (`open`, `with`)
• Explore libraries: math, random, datetime, os
✅ *Tip 5: Work with Data*
• Learn pandas for data analysis
• Use matplotlib & seaborn for visualization
✅ *Tip 6: Object-Oriented Programming (OOP)*
• Classes, Objects, Inheritance, Encapsulation
✅ *Tip 7: Practice Coding Challenges*
• Platforms: LeetCode, HackerRank, Codewars
• Focus on loops, strings, arrays, and logic
✅ *Tip 8: Build Real Projects*
• Portfolio website backend
• Chatbot with NLTK or Rasa
• Simple game with pygame
• Data analysis dashboards
✅ *Tip 9: Learn Web & APIs*
• Flask / Django basics
• Requesting & handling APIs (`requests`)
✅ *Tip 10: Consistency is Key*
Practice Python daily. Review your old code and improve logic, readability, and efficiency.
💬 *Tap ❤️ if this helped you!*
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32 520
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Learn Python, SQL, Excel, Power BI, & more! 📈
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For *Working Professionals*, Job Aspirants and *Final Year Students* 🚀
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32 520
*Free Data Analytics Webinar + Certificate by Guvi & HCL!* 💻🚀
Learn Python, SQL, Excel, Power BI, & more! 📈
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Only *500 seats* left! 😳
For *Working Professionals*, Job Aspirants and *Final Year Students* 🚀
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32 520
Get started with Python quickly using this easy cheatsheet! 🚀
From comments and operators to data structures, loops, and file handling, this guide covers the most useful Python basics, making coding faster and simpler for beginners and pros alike. Save this post and level up your Python journey!
For more tips, follow and share with friends interested in learning Python!
Check out WhatsApp channels for more updates. 👇
https://whatsapp.com/channel/0029Vb6r6218kyyQgBDNjm26
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✅Python Interview Questions with Answers 🧑💻👩💻
1️⃣ Write a function to remove outliers from a list using IQR.
import numpy as np
def remove_outliers(data):
q1 = np.percentile(data, 25)
q3 = np.percentile(data, 75)
iqr = q3 - q1
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr
return [x for x in data if lower <= x <= upper]nested = [[1, 2], [3, 4],]
flat = [item for sublist in nested for item in sublist]import pandas as pd
df = pd.read_csv('data.csv')
null_rows = df.isnull().any(axis=1).sum()
print("Rows with nulls:", null_rows)32 520
🚀 Welcome to PythonAdvisor — your ultimate hub for mastering Python programming and AI technologies!
👨💻 Whether you’re a beginner or an advanced developer, join our community for:
• Daily Python tutorials and coding tips
• Latest AI insights and projects
• Interactive quizzes and challenges
• Support from fellow learners and experts
🔥 Start your programming journey with PythonAdvisor today. Subscribe now and unlock the power of coding!
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32 520
✅Python Interview Questions with Answers 🧑💻👩💻
1️⃣ Write a function to remove outliers from a list using IQR.
import numpy as np
def remove_outliers(data):
q1 = np.percentile(data, 25)
q3 = np.percentile(data, 75)
iqr = q3 - q1
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr
return [x for x in data if lower <= x <= upper]nested = [[1, 2], [3, 4],]
flat = [item for sublist in nested for item in sublist]import pandas as pd
df = pd.read_csv('data.csv')
null_rows = df.isnull().any(axis=1).sum()
print("Rows with nulls:", null_rows)32 520
🚀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗜/𝗟𝗟𝗠 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝘁𝗲𝗰𝗵 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗳𝗼𝗿: fine-tune large language models and deploy them to production at scale.
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🔥 Master These 30 Algorithms to Boost Your Coding Skills! 🚀
- Binary Search 🕵️♂️
- Quick Sort ⚡
- Merge Sort 🌊
- Heap Sort 🏰
- BFS 🌐
- DFS 🌲
- Dijkstra’s Shortest Path 🚗
- Bellman-Ford 🚦
- Floyd-Warshall 🌉
- Kruskal’s Minimum Spanning Tree 🌳
- Prim’s Algorithm 🌿
- KMP Pattern Matching 🔍
- Rabin-Karp Search 🧮
- Dynamic Programming 🧠
- Kadane’s Max Subarray Sum 💥
- Floyd’s Cycle Detection 🔄
- Topological Sort 🗂️
- Backtracking 🎯
- Binary Tree Traversals 🌳
- Segment Tree 📊
- Union-Find Disjoint Set 🔗
- Greedy Algorithms 🎯
- Bit Manipulation 💡
- Sliding Window ⏱️
- Two Pointers 🔀
- Hashing 🔑
- Recursion 🔁
- Divide & Conquer ⚔️
- Graph Coloring 🎨
- A* Search 🗺️
Master these, ace interviews, and become a problem-solving pro! 💪🔥
***
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32 520
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