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Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

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Everything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_data

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Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books (@programming_guide) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 56 114 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 374-o'rinni va Hindiston mintaqasida 6 527-o'rinni egallagan.

๐Ÿ“Š Auditoriya koโ€˜rsatkichlari va dinamika

ะฝะตะฒั–ะดะพะผะพ sanasidan buyon loyiha tez oโ€˜sib, 56 114 obunachiga ega boโ€˜ldi.

10 Iyun, 2026 dagi oxirgi maโ€™lumotlarga koโ€˜ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 89 ga, soโ€˜nggi 24 soatda esa 1 ga oโ€˜zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya oโ€˜rtacha 2.65% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.87% ini tashkil etuvchi reaksiyalarni toโ€˜playdi.
  • Post qamrovi: Har bir post oโ€˜rtacha 1 485 marta koโ€˜riladi; birinchi sutkada odatda 488 ta koโ€˜rish yigโ€˜iladi.
  • Reaksiyalar va oโ€˜zaro taโ€™sir: Auditoriya faol: har bir postga oโ€˜rtacha 4 ta reaksiya keladi.
  • Tematik yoโ€˜nalishlar: Kontent algorithm, structure, stack, javascript, programming kabi asosiy mavzularga jamlangan.

๐Ÿ“ Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโ€™riflaydi:
โ€œEverything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_dataโ€

Yuqori yangilanish chastotasi (oxirgi maโ€™lumot 11 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโ€˜lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโ€™sir nuqtasiga aylantirishini koโ€˜rsatadi.

56 114
Obunachilar
+124 soatlar
+317 kunlar
+8930 kunlar
Postlar arxiv
Programming Languages & What Theyโ€™re Really Good At Python ๐Ÿ โ€“ Data analysis, automation, AI/ML Java โ˜• โ€“ Android apps, enterprise software JavaScript โšก โ€“ Interactive websites, full-stack apps C++ โš™๏ธ โ€“ Game development, system-level software C# ๐ŸŽฎ โ€“ Unity games, Windows apps R ๐Ÿ“Š โ€“ Statistical analysis, data visualization Go ๐Ÿš€ โ€“ Fast APIs, cloud-native apps PHP ๐Ÿ˜ โ€“ WordPress, backend for websites Swift ๐ŸŽ โ€“ iOS/macOS apps Kotlin ๐Ÿ“ฑ โ€“ Modern Android development

๐Ÿณ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—จ๐—ฝ๐—ด๐—ฟ๐—ฎ๐—ฑ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐˜๐—ฎ๐—ป๐—ฑ ๐—ข๐˜‚๐˜๐Ÿ˜ ๐Ÿš€ Want to Make
๐Ÿณ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—จ๐—ฝ๐—ด๐—ฟ๐—ฎ๐—ฑ๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐˜๐—ฎ๐—ป๐—ฑ ๐—ข๐˜‚๐˜๐Ÿ˜ ๐Ÿš€ Want to Make Your Resume Stand Out in 2025?โœจ๏ธ If youโ€™re aiming to boost your chances in job interviews or want to upgrade your resume with powerful, in-demand skills โ€” start with these 7 free online courses๐Ÿ‘จโ€๐Ÿ’ป๐Ÿ“Œ ๐‹๐ข๐ง๐ค๐Ÿ‘‡:- https://pdlink.in/3SJ91OV Empower yourself and take your career to the next level! โœ…

๐Ÿ– 8 Super useful HTML tips & tricks that every Developer should know about
+8
๐Ÿ– 8 Super useful HTML tips & tricks that every Developer should know about

Top 5 Websites Every Developer Should Bookmark ๐Ÿ”–๐Ÿ’ก 1. DevDocs โ€“ All-in-one fast documentation โšก๏ธ 2. CanIUse โ€“ Check browser support like a pro ๐ŸŒ 3. Roadmap โ€“ Visual guides to grow your dev career ๐Ÿ—บ๏ธ 4. JSONLint โ€“ Instantly validate & format JSON ๐Ÿงน 5. Frontend Mentor โ€“ Practice real-world frontend challenges ๐ŸŽฏ React โค๏ธ for more like this

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Common Programming Interview Questions     How do you reverse a string?     How do you determine if a string is a palindrome?     How do you calculate the number of numerical digits in a string?     How do you find the count for the occurrence of a particular character in a string?     How do you find the non-matching characters in a string?     How do you find out if the two given strings are anagrams?     How do you calculate the number of vowels and consonants in a string?     How do you total all of the matching integer elements in an array?     How do you reverse an array?     How do you find the maximum element in an array?     How do you sort an array of integers in ascending order?     How do you print a Fibonacci sequence using recursion?     How do you calculate the sum of two integers?     How do you find the average of numbers in a list?     How do you check if an integer is even or odd?     How do you find the middle element of a linked list?     How do you remove a loop in a linked list?     How do you merge two sorted linked lists?     How do you implement binary search to find an element in a sorted array?     How do you print a binary tree in vertical order? Conceptual Coding Interview Questions     What is a data structure?     What is an array?     What is a linked list?     What is the difference between an array and a linked list?     What is LIFO?     What is FIFO?     What is a stack?     What are binary trees?     What are binary search trees?     What is object-oriented programming?     What is the purpose of a loop in programming?     What is a conditional statement?     What is debugging?     What is recursion?     What are the differences between linear and non-linear data structures? General Coding Interview Questions     What programming languages do you have experience working with?     Describe a time you faced a challenge in a project you were working on and how you overcame it.     Walk me through a project youโ€™re currently or have recently worked on.     Give an example of a project you worked on where you had to learn a new programming language or technology. How did you go about learning it?     How do you ensure your code is readable by other developers?     What are your interests outside of programming?     How do you keep your skills sharp and up to date?     How do you collaborate on projects with non-technical team members?     Tell me about a time when you had to explain a complex technical concept to a non-technical team member.     How do you get started on a new coding project? Best Programming Resources: https://topmate.io/coding/886839 Join for more: https://t.me/programming_guide ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜ ๐˜„๐—ถ๐˜๐—ต ๐—›๐—ฎ๐—ฟ๐˜ƒ๐—ฎ๐—ฟ๐—ฑ ๐—จ๐—ป๐—ถ๐˜ƒ๐—ฒ๐—ฟ๐˜€๐—ถ๐˜๐˜†๐Ÿ˜ ๐ŸŽฏ Want to break into Data
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Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science: 1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs. 2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data. 3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships. 4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding. 5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data. 6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering. 7. Data Visualization: The graphical representation of data to communicate insights and findings effectively. 8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score. 9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models. 10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis. These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data. Join for more: https://t.me/datasciencefun ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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Repost from Data Analytics
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SQL (Structured Query Language) is a standard programming language used to manage and manipulate relational databases. Here are some key concepts to understand the basics of SQL: 1. Database: A database is a structured collection of data organized in tables, which consist of rows and columns. 2. Table: A table is a collection of related data organized in rows and columns. Each row represents a record, and each column represents a specific attribute or field. 3. Query: A SQL query is a request for data or information from a database. Queries are used to retrieve, insert, update, or delete data in a database. 4. CRUD Operations: CRUD stands for Create, Read, Update, and Delete. These are the basic operations performed on data in a database using SQL:    - Create (INSERT): Adds new records to a table.    - Read (SELECT): Retrieves data from one or more tables.    - Update (UPDATE): Modifies existing records in a table.    - Delete (DELETE): Removes records from a table. 5. Data Types: SQL supports various data types to define the type of data that can be stored in each column of a table, such as integer, text, date, and decimal. 6. Constraints: Constraints are rules enforced on data columns to ensure data integrity and consistency. Common constraints include:    - Primary Key: Uniquely identifies each record in a table.    - Foreign Key: Establishes a relationship between two tables.    - Unique: Ensures that all values in a column are unique.    - Not Null: Specifies that a column cannot contain NULL values. 7. Joins: Joins are used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN (or LEFT OUTER JOIN), RIGHT JOIN (or RIGHT OUTER JOIN), and FULL JOIN (or FULL OUTER JOIN). 8. Aggregate Functions: SQL provides aggregate functions to perform calculations on sets of values. Common aggregate functions include SUM, AVG, COUNT, MIN, and MAX. 9. Group By: The GROUP BY clause is used to group rows that have the same values into summary rows. It is often used with aggregate functions to perform calculations on grouped data. 10. Order By: The ORDER BY clause is used to sort the result set of a query based on one or more columns in ascending or descending order. Understanding these basic concepts of SQL will help you write queries to interact with databases effectively. Practice writing SQL queries and experimenting with different commands to become proficient in using SQL for database management and manipulation. SQL Learning Series: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1075

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Python Cheatsheet ๐Ÿš€ 1๏ธโƒฃ Variables & Data Types x = 10 (Integer) y = 3.14 (Float) name = "Python" (String) is_valid = True (Boolean) items = [1, 2, 3] (List) data = (1, 2, 3) (Tuple) person = {"name": "Alice", "age": 25} (Dictionary) 2๏ธโƒฃ Operators Arithmetic: +, -, *, /, //, %, ** Comparison: ==, !=, >, <, >=, <= Logical: and, or, not Membership: in, not in 3๏ธโƒฃ Control Flow If-Else: if age > 18: print("Adult") elif age == 18: print("Just turned 18") else: print("Minor") Loops: for i in range(5): print(i) while x < 10: x += 1 4๏ธโƒฃ Functions Defining & Calling: def greet(name): return f"Hello, {name}" print(greet("Alice")) Lambda Functions: add = lambda x, y: x + y 5๏ธโƒฃ Lists & Dictionary Operations Append: items.append(4) Remove: items.remove(2) List Comprehension: [x**2 for x in range(5)] Dictionary Access: person["name"] 6๏ธโƒฃ File Handling Read File: with open("file.txt", "r") as f: content = f.read() Write File: with open("file.txt", "w") as f: f.write("Hello, World!") 7๏ธโƒฃ Exception Handling try: result = 10 / 0 except ZeroDivisionError: print("Cannot divide by zero!") finally: print("Done") 8๏ธโƒฃ Modules & Packages Importing: import math print(math.sqrt(25)) Creating a Module (mymodule.py): def add(x, y): return x + y Usage: from mymodule import add 9๏ธโƒฃ Object-Oriented Programming (OOP) Defining a Class: class Person: def init(self, name, age): self.name = name self.age = age def greet(self): return f"Hello, my name is {self.name}" Creating an Object: p = Person("Alice", 25) ๐Ÿ”Ÿ Useful Libraries NumPy: import numpy as np Pandas: import pandas as pd Matplotlib: import matplotlib.pyplot as plt Requests: import requests Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L ENJOY LEARNING ๐Ÿ‘๐Ÿ‘

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These are top 5 data structures and algorithms projects, allowing you to dive deep into the world of DSA ๐Ÿ’ช๐Ÿป โ€ขProject 1: Snakes Game (Arrays) The Snakes Game project is a classic implementation of the popular game Snake. This project allows you to understand the concepts of arrays, loops, and conditional statements. You can further enhance the game by incorporating additional features such as score tracking and power-ups. โ€ขProject 2: Cash Flow Minimizer (Graphs/ Multisets/Heaps) The Cash Flow Minimizer project involves solving a cash flow optimization problem using graphs, multisets, and heaps. Given a set of transactions among a group of people, the objective is to minimize the total number of transactions required to settle all debts โ€ขProject 3: Sudoku Solver (Backtracking) The Sudoku Solver project aims to solve the popular Sudoku puzzle using backtracking. This project allows you to understand the backtracking algorithm, which is widely used in solving constraint satisfaction problems. โ€ขProject 4: File Zipper (Greedy Huffman Encoder) The File Zipper project focuses on implementing a file compression utility using the Greedy Huffman encoding algorithm. This project provides a practical application of the greedy algorithm and helps you understand the trade-offs between compression ratio and execution time. โ€ขProject 5: Map Navigator (Dijkstraโ€™s Algorithm) The Map Navigator project aims to develop a navigation system using Dijkstraโ€™s algorithm. It involves finding the shortest path between two locations on a map, considering factors such as distance and traffic. You can check these amazing resources for DSA Preparation Join for more: https://t.me/crackingthecodinginterview All the best ๐Ÿ‘๐Ÿ‘

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๐Ÿฑ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ๐—ณ๐˜‚๐—น ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ ๐—œ๐—ฑ๐—ฒ๐—ฎ๐˜€ ๐˜๐—ผ ๐—•๐—ผ๐—ผ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ฃ๐—ผ๐—ฟ๐˜๐—ณ๐—ผ๐—น๐—ถ๐—ผ๐Ÿ˜ Want
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Essential Programming Languages to Learn Data Science ๐Ÿ‘‡๐Ÿ‘‡ 1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn). 2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization. 3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases. 4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems. 5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications. 6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations. 7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks. Free Resources to master data analytics concepts ๐Ÿ‘‡๐Ÿ‘‡ Data Analysis with R Intro to Data Science Practical Python Programming SQL for Data Analysis Java Essential Concepts Machine Learning with Python Data Science Project Ideas Learning SQL FREE Book Join @free4unow_backup for more free resources. ENJOY LEARNING๐Ÿ‘๐Ÿ‘

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