Python Interviews
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Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free For collaborations: @coderfun
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شبکه:📈 تحلیل کانال تلگرام Python Interviews
کانال Python Interviews (@pythoninterviews) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 28 950 مشترک است و جایگاه 4 529 را در دسته فناوری و برنامهها و رتبه 14 248 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 28 950 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 25 اوت, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 122 و در ۲۴ ساعت گذشته برابر 4 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 1.84% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.63% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 534 بازدید دریافت میکند. در اولین روز معمولاً 182 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند |--, link:-, learning, sql, analytic تمرکز دارد.
📝 توضیح و سیاست محتوایی
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“Join this channel to learn python for web development, data science, artificial intelligence and machine learning with quizzes, projects and amazing resources for free
For collaborations: @coderfun”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 26 اوت, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
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پستهای کانال
✅ Data Science Interview Prep Guide 📊🧠
Whether you're a fresher or career-switcher, here’s how to prep step-by-step:
1️⃣ Understand the Role
Data scientists solve problems using data. Core responsibilities:
• Data cleaning & analysis
• Building predictive models
• Communicating insights
• Working with business/product teams
2️⃣ Core Skills Needed
✔️ Python (NumPy, Pandas, Matplotlib, Scikit-learn)
✔️ SQL
✔️ Statistics & probability
✔️ Machine Learning basics
✔️ Data storytelling & visualization (Power BI / Tableau / Seaborn)
3️⃣ Key Interview Areas
A. Python & Coding
• Write code to clean and analyze data
• Solve logic problems (e.g., reverse a list, group data by key)
• List vs Dict vs DataFrame usage
B. Statistics & Probability
• Hypothesis testing
• p-values, confidence intervals
• Normal distribution, sampling
C. Machine Learning Concepts
• Supervised vs unsupervised learning
• Overfitting, regularization, cross-validation
• Algorithms: Linear Regression, Decision Trees, KNN, SVM
D. SQL
• Joins, GROUP BY, subqueries
• Window functions
• Data aggregation and filtering
E. Business & Communication
• Explain model results to non-tech stakeholders
• What metrics would you track for [business case]?
• Tell me about a time you used data to influence a decision
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✅ Do projects like:
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• LeetCode (SQL, Python)
• HackerRank
• StrataScratch (SQL case studies)
• Kaggle (competitions & notebooks)
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| 3 | To be GOOD in Data Science you need to learn:
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| 5 | Quick Python Cheat Sheet for Beginners 🐍✍️
Python is widely used for data analysis, automation, and AI—perfect for beginners starting their coding journey.
Aggregation Functions 📊
• sum(list) → Adds all values
👉 sum([1,2,3]) = 6
• len(list) → Counts total elements
👉 len([1,2,3]) = 3
• max(list) → Highest value
👉 max([4,7,2]) = 7
• min(list) → Lowest value
👉 min([4,7,2]) = 2
• sum(list)/len(list) → Average
👉 sum([10,20])/2 = 15
Lookup / Searching 🔍
• in → Check existence
👉 5 in [1,2,5] = True
• list.index(value) → Position of value
👉 [10,20,30].index(20) = 1
• Dictionary lookup
👉 data = {"name": "John", "age": 25} data["name"] # John
Logical Operations 🧠
• if condition: → Decision making
👉 if x > 10: print("High") else: print("Low")
• and → All conditions true
• or → Any condition true
• not → Reverse condition
Text (String) Functions 🔤
• len(text) → Length
👉 len("hello") = 5
• text.lower() → Lowercase
• text.upper() → Uppercase
• text.strip() → Remove spaces
👉 " hi ".strip() = "hi"
• text.replace(old, new)
👉 "hi".replace("h","H") = "Hi"
• String concatenation
👉 "Hello " + "World"
Date Time Functions 📅
• from datetime import datetime
• datetime.now() → Current date time
• Extract values:
now = datetime.now() now.year now.month now.day
Math Functions ➗
• import math
• math.sqrt(x) → Square root
• math.ceil(x) → Round up
• math.floor(x) → Round down
• abs(x) → Absolute value
Conditional Aggregation (Like Excel SUMIF) ⚡
• Using list comprehension
nums = [10, 20, 30, 40] sum(x for x in nums if x > 20) # 70
• Count condition
len([x for x in nums if x > 20]) # 2
Pro Tip for Data Analysts 💡
👉 For real-world work, use libraries: pandas & numpy
Example:
import pandas as pd df["salary"].mean()
Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
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| 10 | Top linked list questions to practice:
1. 🔄 Reverse a Linked List
2. 🔁 Detect a Cycle in a Linked List
3. 🤝 Find the Merge Point of Two Linked Lists
4. 🚫 Remove N-th Node From End of List
5. 🔗 Merge Two Sorted Linked Lists
6. 🖼️ Check if a Linked List is a Palindrome
7. 🚨 Remove Duplicates from a Sorted List
8. 🎯 Find the Middle of a Linked List
9. 🔄 Rotate a Linked List
10. 📑 Implement a Doubly Linked List
11. 📊 Implement a Circular Linked List
12. 🛠️ Add Two Numbers Represented by Linked Lists
13. 🧹 Remove Linked List Elements
14. 🧩 Partition List around a value
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| 11 |  🔰 Take Screenshots using Python | 1 141 |
| 12 | Master Python the Right Way – Without Procrastination. 🐍✨
When I first started learning Python, I quickly realized:
You can't master a programming language just by reading syntax or watching tutorials. 📚🚫
Real growth happens when you practice, build, and solve problems on your own. 🛠💻
That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠
What is this collection about? 🤔
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✔️ Pattern-based exercises to strengthen core fundamentals
✔️ Problem-solving programs that sharpen logical thinking
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You don't just learn "how to code", you start learning "how to think like a programmer". 🧠⚡️
This is perfect for: 🎯
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