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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Network:📈 Analytical overview of Telegram channel Python Interviews
Channel Python Interviews (@pythoninterviews) in the English language segment is an active participant. Currently, the community unites 28 950 subscribers, ranking 4 529 in the Technologies & Applications category and 14 248 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 28 950 subscribers.
According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 122 over the last 30 days and by 4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.84%. Within the first 24 hours after publication, content typically collects 0.63% reactions from the total number of subscribers.
- Post reach: On average, each post receives 534 views. Within the first day, a publication typically gains 182 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as |--, link:-, learning, sql, analytic.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 26 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
28 950
Subscribers
+424 hours
+77 days
+12230 days
Data loading in progress...
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Channel Posts
✅ 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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• Kaggle (competitions & notebooks)
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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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This is perfect for: 🎯
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• Building real-world Python projects 🚀
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