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Tarmoq:📈 Telegram kanali Coding Free Books | Python | AI analitikasi
Coding Free Books | Python | AI (@codingwithsagar) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 31 165 obunachidan iborat bo'lib, Taʼlim toifasida 6 125-o'rinni va Hindiston mintaqasida 13 105-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 31 165 obunachiga ega bo‘ldi.
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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Best Channel for Programmers and Hackers
All in one channel to learn
👇
1. Python
2. Ethical Hacking
3. Java
4. App development
5. Machine learning
6. Data structures
7. Algorithms
Promotions: @coderfun”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 26 Avgust, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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| 2 | Here's a good list of cheat sheets for programmers (all free):
Data Science Cheatsheet
https://github.com/aaronwangy/Data-Science-Cheatsheet
SQL Cheatsheet
sqltutorial.org/sql-cheat-sheet
t.me/sqlspecialist/827
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
Java Programming Cheatsheet
https://introcs.cs.princeton.edu/java/11cheatsheet/
Javascript Cheatsheet
quickref.me/javascript.html
t.me/javascript_courses/532
Data Analytics Cheatsheets
https://dataanalytics.beehiiv.com/p/data
Python Cheat sheet
quickref.me/python.html
https://t.me/pythondevelopersindia/314
GIT and Machine Learning Cheatsheet
https://t.me/datasciencefun/714
HTML Cheatsheet
https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf
htmlcheatsheet.com
CSS Cheatsheet
htmlcheatsheet.com/css
jQuery Cheatsheet
t.me/webdevelopmentbook/90
Data Visualization
t.me/datasciencefun/698
ENJOY LEARNING👍👍 | 1 904 |
| 3 | Top free Data Science resources
1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html
2. Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/
3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse
4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI
5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY
6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM
7. CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/
8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf
9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx
10. Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE
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| 4 |  If you’re a student, graduate, or someone looking for a career switch, read this.
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| 5 | Quick Recap of Essential Python Concepts 😄👇
Python is a versatile and beginner-friendly programming language widely used in data science, web development, and automation. Here's a quick overview of some fundamental concepts:
1. Variables:
* Variables are used to store data values. They are assigned using the = operator. Example: x = 10, name = "Alice"
2. Data Types:
* Python has several built-in data types:
* Integer (int): Whole numbers (e.g., 1, -5).
* Float (float): Decimal numbers (e.g., 3.14, -2.5).
* String (str): Textual data (e.g., "Hello", 'Python').
* Boolean (bool): True or False values.
* List: Ordered collection of items (e.g., [1, 2, "apple"]).
* Tuple: Ordered, immutable collection (e.g., (1, 2, "apple")).
* Dictionary: Key-value pairs (e.g., {"name": "Alice", "age": 30}).
3. Operators:
* Python supports various operators for performing operations:
* Arithmetic Operators: +, -, *, /, // (floor division), % (modulus), * (exponentiation).
* Comparison Operators: ==, !=, >, <, >=, <=.
* Logical Operators: and, or, not.
* Assignment Operators: =, +=, -=, *=, /=, etc.
4. Control Flow:
* Control flow statements determine the order in which code is executed:
* if, elif, else: Conditional execution.
* for loop: Iterating over a sequence (list, string, etc.).
* while loop: Repeating a block of code as long as a condition is true.
5. Functions:
* Functions are reusable blocks of code defined using the def keyword.
def greet(name):
print("Hello, " + name + "!")
greet("Bob") # Output: Hello, Bob!
6. Lists:
* Lists are ordered, mutable (changeable) collections.
* Create: my_list = [1, 2, 3, "a"]
* Access: my_list[0] (first element)
* Modify: my_list.append(4), my_list.remove(2)
7. Dictionaries:
* Dictionaries store key-value pairs.
* Create: my_dict = {"name": "Alice", "age": 30}
* Access: my_dict["name"] (gets "Alice")
* Modify: my_dict["city"] = "New York"
8. Loops:
* For Loops:
my_list = [1, 2, 3]
for item in my_list:
print(item)
* While Loops:
count = 0
while count < 5:
print(count)
count += 1
9. String Manipulation:
* Slicing: my_string[1:4] (extracts a portion of the string)
* Concatenation: "Hello" + " " + "World"
* Useful Methods: .upper(), .lower(), .strip(), .replace(), .split()
10. Modules and Libraries:
* import statement is used to include code from external modules (libraries).
* Example:
import math
print(math.sqrt(16)) # Output: 4.0
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| 6 | Confused about which field to dive into—Front-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain?
Here's a concise breakdown of each, designed to clarify your options:
### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.
Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer
### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.
Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator
### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.
Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher
### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.
Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst
### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.
Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.
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| 13 | 💻 Software Engineer Roadmap 🚀
📂 Computer Fundamentals
∟📂 Operating Systems (Processes, Threads, Memory, Scheduling)
∟📂 Networking Basics (HTTP/HTTPS, TCP/IP, DNS, APIs)
∟📂 DBMS (SQL, Indexing, Normalization, Transactions)
∟📂 Git & Version Control (GitHub workflow)
📂 Programming Fundamentals
∟📂 Language (Python / JavaScript / Java / C++)
∟📂 Variables, Loops, Functions
∟📂 OOP (Class, Object, Inheritance, Polymorphism)
∟📂 Error Handling & Debugging
📂 Data Structures & Algorithms
∟📂 Arrays, Strings, HashMap
∟📂 Stack, Queue, Linked List
∟📂 Trees, Graphs (Basics)
∟📂 Recursion & Backtracking
∟📂 Patterns (Sliding Window, Two Pointers, Binary Search, DFS/BFS)
∟📂 Dynamic Programming (Basic)
📂 Development (Choose One Path)
∟📂 Web Development 🌐
∟ Frontend (HTML, CSS, JavaScript, React)
∟ Backend (Node.js / Django / FastAPI)
∟ Database (MongoDB / PostgreSQL)
∟ REST APIs + Authentication
∟📂 Backend / Systems ⚙️
∟ APIs & Microservices
∟ Databases (SQL + NoSQL)
∟ Caching (Redis)
∟ Message Queues (Kafka/RabbitMQ Basics)
∟📂 AI / Data 🤖
∟ Python (NumPy, Pandas)
∟ Machine Learning Basics
∟ APIs + AI Integration
∟ LLMs / RAG / AI Apps
📂 Tools & Development Skills
∟📂 Git & GitHub
∟📂 Linux Basics
∟📂 VS Code / IDE
∟📂 Postman (API Testing)
∟📂 Docker (Basics)
📂 System Design (Basics → Advanced)
∟📂 Scalability (Load Balancing, Caching)
∟📂 Database Design
∟📂 API Design
∟📂 Real-world Systems (URL Shortener, Chat App)
📂 Projects (Very Important 🔥)
∟📂 Beginner (Calculator, CLI Apps)
∟📂 Intermediate (CRUD App, Auth System)
∟📂 Advanced (Full Stack App / SaaS / AI Tool)
∟📂 Deploy Projects (Vercel / AWS / Render)
📂 Interview Preparation
∟📂 DSA Practice (LeetCode)
∟📂 Core Subjects Revision (OS, DBMS, CN)
∟📂 Mock Interviews
📂 Portfolio & Resume
∟📂 GitHub Projects
∟📂 Personal Portfolio Website
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∟📂 Apply Daily (Internships + Jobs)
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| 16 |  There's a floating-point number in Python and you need to output it as a percentage - use the % format in the f-string
x = .023
print(f'{x:.2%}') # 2.30%
x = .02375
print(f'{x:.2%}') # 2.38% -- rounded off!
x = 1.02375
print(f'{x:.2%}') # 102.38%
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| 17 | ✅ Step-by-Step Approach to Learn Programming 💻🚀
➊ Pick a Programming Language
Start with beginner-friendly languages that are widely used and have lots of resources.
✔ Python – Great for beginners, versatile (web, data, automation)
✔ JavaScript – Perfect for web development
✔ C++ / Java – Ideal if you're targeting DSA or competitive programming
Goal: Be comfortable with syntax, writing small programs, and using an IDE.
➋ Learn Basic Programming Concepts
Understand the foundational building blocks of coding:
✔ Variables, data types
✔ Input/output
✔ Loops (for, while)
✔ Conditional statements (if/else)
✔ Functions and scope
✔ Error handling
Tip: Use visual platforms like W3Schools, freeCodeCamp, or Sololearn.
➌ Understand Data Structures & Algorithms (DSA)
✔ Arrays, Strings
✔ Linked Lists, Stacks, Queues
✔ Hash Maps, Sets
✔ Trees, Graphs
✔ Sorting & Searching
✔ Recursion, Greedy, Backtracking
✔ Dynamic Programming
Use GeeksforGeeks, NeetCode, or Striver's DSA Sheet.
➍ Practice Problem Solving Daily
✔ LeetCode (real interview Qs)
✔ HackerRank (step-by-step)
✔ Codeforces / AtCoder (competitive)
Goal: Focus on logic, not just solutions.
➎ Build Mini Projects
✔ Calculator
✔ To-do list app
✔ Weather app (using APIs)
✔ Quiz app
✔ Rock-paper-scissors game
Projects solidify your concepts.
➏ Learn Git & GitHub
✔ Initialize a repo
✔ Commit & push code
✔ Branch and merge
✔ Host projects on GitHub
Must-have for collaboration.
➐ Learn Web Development Basics
✔ HTML – Structure
✔ CSS – Styling
✔ JavaScript – Interactivity
Then explore:
✔ React.js
✔ Node.js + Express
✔ MongoDB / MySQL
➑ Choose Your Career Path
✔ Web Dev (Frontend, Backend, Full Stack)
✔ App Dev (Flutter, Android)
✔ Data Science / ML
✔ DevOps / Cloud (AWS, Docker)
➒ Work on Real Projects & Internships
✔ Build a portfolio
✔ Clone real apps (Netflix UI, Amazon clone)
✔ Join hackathons
✔ Freelance or open source
✔ Apply for internships
➓ Stay Updated & Keep Improving
✔ Follow GitHub trends
✔ Dev YouTube channels (Fireship, etc.)
✔ Tech blogs (Dev.to, Medium)
✔ Communities (Discord, Reddit, X)
🎯 Remember:
• Consistency > Intensity
• Learn by building
• Debugging is learning
• Track progress weekly
Useful WhatsApp Channels to Learn Programming Languages
Python Programming: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
JavaScript: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
C++ Programming: https://whatsapp.com/channel/0029VbBAimF4dTnJLn3Vkd3M
Java Programming: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
👍 React ♥️ for more | 2 067 |
| 18 |  CVE | Cyber Vulnerabilities Exchange
Group dedicated to sharing and discussing CVEs, zero-days, critical vulnerabilities, exploits, PoCs, and technical analyses of offensive and defensive security.
What you'll find here:
• Newly disclosed CVEs
• Public and private exploits
• Technical analysis and bypasses
• Offensive/defensive security
• Penetration testing and red team discussions
Technical, direct, and straightforward content.
Channel=> https://t.me/cve0day
Think. Break. Secure. | 0 |
| 19 | +3 💻 Collection of cheat sheets on SQL
I've gathered for you short and understandable cheat sheets on the main topics:
▶️ Basics of the SQL language;
▶️ JOINs with clear examples;
▶️ Window functions;
▶️ SQL for data analysis.
An excellent set to refresh your knowledge before a job interview or quickly recall the syntax.
tags: #sql #useful
https://t.me/DataAnalyticsX | 0 |
| 20 | Master DSA 🚀
DSA MASTER TREE
│
├── 1. Foundations
│ ├── What is Data Structure
│ ├── What is Algorithm
│ ├── Time Complexity
│ │ ├── Big-O
│ │ ├── Big-Ω
│ │ └── Big-Θ
│ ├── Space Complexity
│ └── Recurrence Relations
│
├── 2. Mathematical Basics
│ ├── Logarithms
│ ├── Modular Arithmetic
│ ├── Prime Numbers
│ ├── GCD / LCM
│ └── Sieve of Eratosthenes
│
├── 3. Arrays
│ ├── Traversal
│ ├── Searching
│ │ ├── Linear Search
│ │ └── Binary Search
│ ├── Prefix Sum
│ ├── Sliding Window
│ ├── Two Pointers
│ ├── Kadane’s Algorithm
│ └── Matrix / 2D Arrays
│
├── 4. Strings
│ ├── String Manipulation
│ ├── Pattern Matching
│ │ ├── Naive
│ │ ├── KMP
│ │ ├── Rabin-Karp
│ │ └── Z Algorithm
│ ├── Palindrome Problems
│ ├── String Hashing
│ └── Trie
│
├── 5. Linked Lists
│ ├── Singly Linked List
│ ├── Doubly Linked List
│ ├── Circular Linked List
│ ├── Reverse Linked List
│ ├── Cycle Detection (Floyd)
│ └── Merge Lists
│
├── 6. Stack
│ ├── Stack Implementation
│ ├── Balanced Parentheses
│ ├── Next Greater Element
│ ├── Monotonic Stack
│ └── Min Stack
│
├── 7. Queue
│ ├── Queue Implementation
│ ├── Circular Queue
│ ├── Deque
│ ├── Priority Queue
│ └── Monotonic Queue
│
├── 8. Hashing
│ ├── Hash Tables
│ ├── Collision Handling
│ │ ├── Chaining
│ │ └── Open Addressing
│ ├── Load Factor
│ └── Rehashing
│
├── 9. Trees
│ ├── Binary Tree
│ │ ├── Traversals
│ │ │ ├── Inorder
│ │ │ ├── Preorder
│ │ │ └── Postorder
│ │ ├── Height / Depth
│ │ └── Diameter
│ ├── Binary Search Tree
│ ├── AVL Tree
│ ├── Red-Black Tree
│ ├── Segment Tree
│ ├── Fenwick Tree
│ └── Heap
│ ├── Min Heap
│ └── Max Heap
│
├── 10. Graphs
│ ├── Graph Representation
│ │ ├── Adjacency Matrix
│ │ └── Adjacency List
│ ├── BFS
│ ├── DFS
│ ├── Topological Sort
│ ├── Cycle Detection
│ ├── Shortest Path
│ │ ├── Dijkstra
│ │ ├── Bellman-Ford
│ │ └── Floyd-Warshall
│ ├── Minimum Spanning Tree
│ │ ├── Kruskal
│ │ └── Prim
│ └── Disjoint Set (Union-Find)
│
├── 11. Recursion & Backtracking
│ ├── Recursion Basics
│ ├── Subsets
│ ├── Permutations
│ ├── N-Queens
│ └── Sudoku Solver
│
├── 12. Greedy Algorithms
│ ├── Activity Selection
│ ├── Huffman Coding
│ ├── Fractional Knapsack
│ └── Job Scheduling
│
├── 13. Dynamic Programming
│ ├── Memoization
│ ├── Tabulation
│ ├── 1D DP
│ ├── 2D DP
│ ├── Knapsack Variants
│ ├── Longest Common Subsequence
│ ├── Longest Increasing Subsequence
│ └── Matrix Chain Multiplication
│
├── 14. Bit Manipulation
│ ├── Bitwise Operators
│ ├── Set / Clear Bits
│ ├── Count Set Bits
│ └── XOR Tricks
│
├── 15. Advanced DSA
│ ├── Sparse Table
│ ├── Heavy-Light Decomposition
│ ├── Treap
│ ├── Splay Tree
│ └── Skip List
│
└── 16. Interview Patterns
├── Two Pointer Pattern
├── Sliding Window Pattern
├── Binary Search Pattern
├── BFS / DFS Pattern
├── Greedy Choice Pattern
└── DP Pattern Recognition | 0 |
