Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books
前往频道在 Telegram
Everything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_data
显示更多📈 Telegram 频道 Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books 的分析概览
频道 Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books (@programming_guide) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 55 782 名订阅者,在 技术与应用 类别中位列第 2 360,并在 印度 地区排名第 6 076 位。
📊 受众指标与增长动态
自 невідомо 创建以来,项目保持高速增长,吸引了 55 782 名订阅者。
根据 31 十月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -218,过去 24 小时变化为 -11,整体触达仍然可观。
- 认证状态: 未认证
- 互动率 (ER): 平均受众互动率为 1.99%。内容发布后 24 小时内通常能获得 0.58% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 1 109 次浏览,首日通常累积 321 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 2。
- 主题关注点: 内容集中在 algorithm, structure, stack, javascript, programming 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台:
“Everything about programming for beginners
* Python programming
* Java programming
* App development
* Machine Learning
* Data Science
Managed by: @love_data”
凭借高频更新(最新数据采集于 01 十一月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。
55 782
订阅者
-1124 小时
-397 天
-21830 天
帖子存档
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Python CheatSheet 📚 ✅
1. Basic Syntax
- Print Statement:
print("Hello, World!")
- Comments: # This is a comment
2. Data Types
- Integer: x = 10
- Float: y = 10.5
- String: name = "Alice"
- List: fruits = ["apple", "banana", "cherry"]
- Tuple: coordinates = (10, 20)
- Dictionary: person = {"name": "Alice", "age": 25}
3. Control Structures
- If Statement:
if x > 10:
print("x is greater than 10")
- For Loop:
for fruit in fruits:
print(fruit)
- While Loop:
while x < 5:
x += 1
4. Functions
- Define Function:
def greet(name):
return f"Hello, {name}!"
- Lambda Function: add = lambda a, b: a + b
5. Exception Handling
- Try-Except Block:
try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero.")
6. File I/O
- Read File:
with open('file.txt', 'r') as file:
content = file.read()
- Write File:
with open('file.txt', 'w') as file:
file.write("Hello, World!")
7. List Comprehensions
- Basic Example: squared = [x**2 for x in range(10)]
- Conditional Comprehension: even_squares = [x**2 for x in range(10) if x % 2 == 0]
8. Modules and Packages
- Import Module: import math
- Import Specific Function: from math import sqrt
9. Common Libraries
- NumPy: import numpy as np
- Pandas: import pandas as pd
- Matplotlib: import matplotlib.pyplot as plt
10. Object-Oriented Programming
- Define Class:
class Dog:
def __init__(self, name):
self.name = name
def bark(self):
return "Woof!"
11. Virtual Environments
- Create Environment: python -m venv myenv
- Activate Environment:
- Windows: myenv\Scripts\activate
- macOS/Linux: source myenv/bin/activate
12. Common Commands
- Run Script: python script.py
- Install Package: pip install package_name
- List Installed Packages: pip list
This Python checklist serves as a quick reference for essential syntax, functions, and best practices to enhance your coding efficiency!
Checklist for Data Analyst: https://dataanalytics.beehiiv.com/p/data
Here you can find essential Python Interview Resources👇
https://t.me/DataSimplifier
Like for more resources like this 👍 ♥️
Share with credits: https://t.me/sqlspecialist
Hope it helps :)𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗔𝗿𝗲 𝗠𝗼𝘀𝘁 𝗗𝗲𝗺𝗮𝗻𝗱𝗶𝗻𝗴 𝗖𝗮𝗿𝗲𝗲𝗿𝘀 𝗜𝗻 𝟮𝟬𝟮𝟱 😍
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Complete roadmap to learn Python and Data Structures & Algorithms (DSA) in 2 months
### Week 1: Introduction to Python
Day 1-2: Basics of Python
- Python setup (installation and IDE setup)
- Basic syntax, variables, and data types
- Operators and expressions
Day 3-4: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
Day 5-6: Functions and Modules
- Function definitions, parameters, and return values
- Built-in functions and importing modules
Day 7: Practice Day
- Solve basic problems on platforms like HackerRank or LeetCode
### Week 2: Advanced Python Concepts
Day 8-9: Data Structures in Python
- Lists, tuples, sets, and dictionaries
- List comprehensions and generator expressions
Day 10-11: Strings and File I/O
- String manipulation and methods
- Reading from and writing to files
Day 12-13: Object-Oriented Programming (OOP)
- Classes and objects
- Inheritance, polymorphism, encapsulation
Day 14: Practice Day
- Solve intermediate problems on coding platforms
### Week 3: Introduction to Data Structures
Day 15-16: Arrays and Linked Lists
- Understanding arrays and their operations
- Singly and doubly linked lists
Day 17-18: Stacks and Queues
- Implementation and applications of stacks
- Implementation and applications of queues
Day 19-20: Recursion
- Basics of recursion and solving problems using recursion
- Recursive vs iterative solutions
Day 21: Practice Day
- Solve problems related to arrays, linked lists, stacks, and queues
### Week 4: Fundamental Algorithms
Day 22-23: Sorting Algorithms
- Bubble sort, selection sort, insertion sort
- Merge sort and quicksort
Day 24-25: Searching Algorithms
- Linear search and binary search
- Applications and complexity analysis
Day 26-27: Hashing
- Hash tables and hash functions
- Collision resolution techniques
Day 28: Practice Day
- Solve problems on sorting, searching, and hashing
### Week 5: Advanced Data Structures
Day 29-30: Trees
- Binary trees, binary search trees (BST)
- Tree traversals (in-order, pre-order, post-order)
Day 31-32: Heaps and Priority Queues
- Understanding heaps (min-heap, max-heap)
- Implementing priority queues using heaps
Day 33-34: Graphs
- Representation of graphs (adjacency matrix, adjacency list)
- Depth-first search (DFS) and breadth-first search (BFS)
Day 35: Practice Day
- Solve problems on trees, heaps, and graphs
### Week 6: Advanced Algorithms
Day 36-37: Dynamic Programming
- Introduction to dynamic programming
- Solving common DP problems (e.g., Fibonacci, knapsack)
Day 38-39: Greedy Algorithms
- Understanding greedy strategy
- Solving problems using greedy algorithms
Day 40-41: Graph Algorithms
- Dijkstra’s algorithm for shortest path
- Kruskal’s and Prim’s algorithms for minimum spanning tree
Day 42: Practice Day
- Solve problems on dynamic programming, greedy algorithms, and advanced graph algorithms
### Week 7: Problem Solving and Optimization
Day 43-44: Problem-Solving Techniques
- Backtracking, bit manipulation, and combinatorial problems
Day 45-46: Practice Competitive Programming
- Participate in contests on platforms like Codeforces or CodeChef
Day 47-48: Mock Interviews and Coding Challenges
- Simulate technical interviews
- Focus on time management and optimization
Day 49: Review and Revise
- Go through notes and previously solved problems
- Identify weak areas and work on them
### Week 8: Final Stretch and Project
Day 50-52: Build a Project
- Use your knowledge to build a substantial project in Python involving DSA concepts
Day 53-54: Code Review and Testing
- Refactor your project code
- Write tests for your project
Day 55-56: Final Practice
- Solve problems from previous contests or new challenging problems
Day 57-58: Documentation and Presentation
- Document your project and prepare a presentation or a detailed report
Day 59-60: Reflection and Future Plan
- Reflect on what you've learned
- Plan your next steps (advanced topics, more projects, etc.)
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Repost from SQL Programming Resources
𝗣𝗿𝗲𝗽𝗮𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗮𝗻 𝗔𝗺𝗮𝘇𝗼𝗻 𝗗𝗮𝘁𝗮 𝗥𝗼𝗹𝗲? 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗧𝗼𝗽 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀😍
💼 Why SQL Is Crucial for Amazon Interviews🗣
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❯ VS Code ➟ Lightweight, Powerful Code Editor
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❯ Docker ➟ App Containerization
❯ Kubernetes ➟ Scaling & Orchestrating Containers
❯ Git ➟ Version Control, Team Collaboration
❯ GitHub/GitLab ➟ Hosting Code Repos, CI/CD
❯ Figma ➟ UI/UX Design, Prototyping
❯ Jira ➟ Agile Project Management
❯ Slack/Discord ➟ Team Communication
❯ Notion ➟ Docs, Notes, Knowledge Base
❯ Trello ➟ Task Management
❯ Zsh + Oh My Zsh ➟ Advanced Terminal Experience
❯ Linux Terminal ➟ DevOps, Shell Scripting
❯ Homebrew (macOS) ➟ Package Manager
❯ Anaconda ➟ Python & Data Science Environments
❯ Pandas ➟ Data Manipulation in Python
❯ NumPy ➟ Numerical Computation
❯ Jupyter Notebooks ➟ Interactive Python Coding
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❯ Firebase ➟ Backend as a Service
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Repost from Data Science & Machine Learning
🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍
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How to master Python from scratch🚀
1. Setup and Basics 🏁
- Install Python 🖥️: Download Python and set it up.
- Hello, World! 🌍: Write your first Hello World program.
2. Basic Syntax 📜
- Variables and Data Types 📊: Learn about strings, integers, floats, and booleans.
- Control Structures 🔄: Understand if-else statements, for loops, and while loops.
- Functions 🛠️: Write reusable blocks of code.
3. Data Structures 📂
- Lists 📋: Manage collections of items.
- Dictionaries 📖: Store key-value pairs.
- Tuples 📦: Work with immutable sequences.
- Sets 🔢: Handle collections of unique items.
4. Modules and Packages 📦
- Standard Library 📚: Explore built-in modules.
- Third-Party Packages 🌐: Install and use packages with pip.
5. File Handling 📁
- Read and Write Files 📝
- CSV and JSON 📑
6. Object-Oriented Programming 🧩
- Classes and Objects 🏛️
- Inheritance and Polymorphism 👨👩👧
7. Web Development 🌐
- Flask 🍼: Start with a micro web framework.
- Django 🦄: Dive into a full-fledged web framework.
8. Data Science and Machine Learning 🧠
- NumPy 📊: Numerical operations.
- Pandas 🐼: Data manipulation and analysis.
- Matplotlib 📈 and Seaborn 📊: Data visualization.
- Scikit-learn 🤖: Machine learning.
9. Automation and Scripting 🤖
- Automate Tasks 🛠️: Use Python to automate repetitive tasks.
- APIs 🌐: Interact with web services.
10. Testing and Debugging 🐞
- Unit Testing 🧪: Write tests for your code.
- Debugging 🔍: Learn to debug efficiently.
11. Advanced Topics 🚀
- Concurrency and Parallelism 🕒
- Decorators 🌀 and Generators ⚙️
- Web Scraping 🕸️: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects 💡
- Calculator 🧮
- To-Do List App 📋
- Weather App ☀️
- Personal Blog 📝
13. Community and Collaboration 🤝
- Contribute to Open Source 🌍
- Join Coding Communities 💬
- Participate in Hackathons 🏆
14. Keep Learning and Improving 📈
- Read Books 📖: Like "Automate the Boring Stuff with Python".
- Watch Tutorials 🎥: Follow video courses and tutorials.
- Solve Challenges 🧩: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge 📢
- Write Blogs ✍️
- Create Video Tutorials 📹
- Mentor Others 👨🏫
I have curated the best interview resources to crack Python Interviews 👇👇
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this 👍❤️
𝗪𝗼𝗿𝗸 𝗙𝗿𝗼𝗺 𝗛𝗼𝗺𝗲 𝗝𝗼𝗯 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗮𝗻 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗕𝗿𝗮𝗻𝗱!😍
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