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

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India6 177 Technologies & Applications2 292...

📈 Analytical overview of Telegram channel Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

Channel Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books (@programming_guide) in the English language segment is an active participant. Currently, the community unites 56 115 subscribers, ranking 2 292 in the Technologies & Applications category and 6 177 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 56 115 subscribers.

According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -49 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 1.80%. Within the first 24 hours after publication, content typically collects 0.72% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 012 views. Within the first day, a publication typically gains 402 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
Thematic interests: Content is focused on key topics such as algorithm, structure, stack, javascript, programming.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Everything about programming for beginners * Python programming * Java programming * App development * Machine Learning * Data Science Managed by: @love_data”

Thanks to the high frequency of updates (latest data received on 27 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.

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Subscribers

+424 hours

-447 days

-4930 days

1 012

Post views

~ 40224 hours

~ 56248 hours

1.80%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

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✅ Data Science Interview Prep Guide 1️⃣ Core Data Science Concepts • What is Data Science vs Data Analytics vs ML • Descriptive, diagnostic, predictive, prescriptive analytics • Structured vs unstructured data • Data-driven decision making • Business problem framing 2️⃣ Statistics Probability (Non-Negotiable) • Mean, median, variance, standard deviation • Probability distributions (normal, binomial, Poisson) • Hypothesis testing p-values • Confidence intervals • Correlation vs causation • Sampling bias 3️⃣ Data Cleaning EDA • Handling missing values outliers • Data normalization scaling • Feature engineering • Exploratory data analysis (EDA) • Data leakage detection • Data quality validation 4️⃣ Python SQL for Data Science • Python (NumPy, Pandas) • Data manipulation transformations • Vectorization performance optimization • SQL joins, CTEs, window functions • Writing business-ready queries 5️⃣ Machine Learning Essentials • Supervised vs unsupervised learning • Regression vs classification • Model selection baseline models • Overfitting, underfitting • Bias–variance tradeoff • Hyperparameter tuning 6️⃣ Model Evaluation Metrics • Accuracy, precision, recall, F1 • ROC AUC • Confusion matrix • RMSE, MAE, log loss • Metrics for imbalanced data • Linking ML metrics to business KPIs 7️⃣ Real-World Deployment Knowledge • Feature stores • Model deployment (batch vs real-time) • Model monitoring drift • Experiment tracking • Data model versioning • Model explainability (business-friendly) 8️⃣ Must-Have Projects • Customer churn prediction • Fraud detection • Sales or demand forecasting • Recommendation system • End-to-end ML pipeline • Business-focused case study 9️⃣ Common Interview Questions • Walk me through an end-to-end DS project • How do you choose evaluation metrics? • How do you handle imbalanced data? • How do you explain a model to leadership? • How do you improve a failing model? 🔟 Pro Tips ✔️ Always connect answers to business impact ✔️ Explain why, not just how ✔️ Be clear about trade-offs ✔️ Discuss failures learnings ✔️ Show structured thinking Double Tap ♥️ For More

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🚀 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗢𝘄𝗻 𝗔𝗽𝗽 𝘄𝗶𝘁𝗵 𝗔𝗜 — 𝗡𝗢 𝗖𝗢𝗗𝗜𝗡𝗚 𝗡𝗘𝗘𝗗𝗘𝗗! Imagine turning your idea into a real app in minutes 🤯 You just describe your idea, and AI builds the entire app for you (frontend + backend + deployment) 💻⚡ 💡 Perfect for: • Students & Beginners , Creators & Side Hustlers & Anyone with an idea 💭 𝗦𝘁𝗮𝗿𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗵𝗲𝗿𝗲👇:- https://pdlink.in/4e4ILub 💬 Your idea + AI = Your next income source 💸 ⚡ Don’t just scroll… BUILD something today!

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📊 Data Science Essentials: What Every Data Enthusiast Should Know! 1️⃣ Understand Your Data Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights. 2️⃣ Data Cleaning Matters Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively. 3️⃣ Use Descriptive & Inferential Statistics Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation. 4️⃣ Master Data Visualization Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable. 5️⃣ Learn SQL for Efficient Data Extraction Write optimized queries (SELECT, JOIN, GROUP BY, WHERE) to retrieve relevant data from databases. 6️⃣ Build Strong Programming Skills Python (Pandas, NumPy, Scikit-learn) and R are essential for data manipulation and analysis. 7️⃣ Understand Machine Learning Basics Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models. 8️⃣ Learn Dashboarding & Storytelling Power BI and Tableau help convert raw data into actionable insights for stakeholders. 🔥 Pro Tip: Always cross-check your results with different techniques to ensure accuracy! Data Science Learning Series: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D DOUBLE TAP ❤️ IF YOU FOUND THIS HELPFUL!

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🚀 Git Commands Every Developer Should Know 🔹 git clone 🔹 git status 🔹 git add . 🔹 git commit -m "message" 🔹 git push 🔹 git pull 🔹 git fetch 🔹 git switch -c <branch> 🔹 git branch 🔹 git merge 🔹 git diff 🔹 git log --oneline React 👍 if you use Git reguarly #techinfo

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Today, let's understand another programming concept: 🔥 Searching Algorithms 🔍💻 Searching is used to find an element in a dataset. It’s one of the most common operations in programming and interviews. 📌 What is Searching? Searching means locating a specific element inside a collection (array, list, etc.). Example: Find 7 in [2, 4, 7, 10] 🧠 Important Searching Algorithms 1️⃣ Linear Search Concept: Check each element one by one until the target is found. Example: Find 7 in [2, 4, 7, 10] → check 2 → check 4 → check 7 ✅ Key Points: • Works on unsorted data • Simple to implement • Time Complexity: O(n) 2️⃣ Binary Search Concept: Divide the sorted array into halves and search efficiently. Condition: 👉 Array must be sorted Example: Find 7 in [2, 4, 7, 10] → middle = 7 → found immediately Another case: Find 10 → middle = 7 → go right → find 10 Key Points: • Much faster than linear search • Time Complexity: O(log n) ⚡ Linear vs Binary Search • Linear Search → checks every element • Binary Search → eliminates half of data each step 👉 Binary is much faster for large datasets. 🎯 When to Use What • Data is unsorted → Linear Search • Data is sorted → Binary Search • Small dataset → Linear is fine • Large dataset → Binary is preferred ⚠️ Common Interview Mistakes ❌ Using binary search on unsorted data ❌ Forgetting boundary conditions ❌ Infinite loop in binary search ❌ Wrong mid calculation ⭐ Interview Questions • Difference between Linear Binary Search • When to use Binary Search • Time complexity comparison • Implement Binary Search • Edge cases (empty array, single element) 💡 Real-World Usage • Searching in databases • Finding users/products • Autocomplete systems • Search engines Double Tap ❤️ For More

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✅ React.js Essentials ⚛️🔥 React.js is a JavaScript library for building user interfaces, especially single-page apps. Created by Meta, it focuses on components, speed, and interactivity. 1️⃣ What is React? React lets you build reusable UI components and update the DOM efficiently using a virtual DOM. Why Use React? • Reusable components • Faster performance with virtual DOM • Great for building SPAs (Single Page Applications) • Strong community and ecosystem 2️⃣ Key Concepts 📦 Components – Reusable, independent pieces of UI.

function Welcome() {
  return <h1>Hello, React!</h1>;
}

🧠 Props – Pass data to components

function Greet(props) {
  return <h2>Hello, {props.name}!</h2>;
}
<Greet name="Riya" />

💡 State – Store and manage data in a component

import { useState } from 'react';

function Counter() {
  const [count, setCount] = useState(0);
  return (
    <>
      <p>Count: {count}</p>
      <button onClick={() => setCount(count + 1)}>Add</button>
    </>
  );
}

3️⃣ Hooks useState – Manage local state useEffect – Run side effects (like API calls, DOM updates)

import { useEffect } from 'react';

useEffect(() => {
  console.log("Component mounted");
}, []);

4️⃣ JSX JSX lets you write HTML inside JS.

const element = <h1>Hello World</h1>;

5️⃣ Conditional Rendering

{isLoggedIn ? <Dashboard /> : <Login />}

6️⃣ Lists and Keys

const items = ["Apple", "Banana"];
items.map((item, index) => <li key={index}>{item}</li>);

7️⃣ Event Handling

<button onClick={handleClick}>Click Me</button>

8️⃣ Form Handling

<input value={name} onChange={(e) => setName(e.target.value)} />

9️⃣ React Router (Bonus) To handle multiple pages

npm install react-router-dom

import { BrowserRouter, Route, Routes } from 'react-router-dom';

🛠 Practice Tasks ✅ Build a counter ✅ Make a TODO app using state ✅ Fetch and display API data ✅ Try routing between 2 pages 💬 Tap ❤️ for more

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𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜😍 Curriculum designed and taught by alumni from IITs & leading tech companies, with practical GenAI applications. * 2000+ Students Placed * 41LPA Highest Salary * 500+ Partner Companies - 7.4 LPA Avg Salary 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:- 🔹 Online :- https://pdlink.in/4hO7rWY 🔹 Hyderabad :- https://pdlink.in/4cJUWtx 🔹 Pune :- https://pdlink.in/3YA32zi 🔹 Noida :- https://linkpd.in/NoidaFSD Hurry Up 🏃‍♂️! Limited seats are available.

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Here’s a solid 𝗕𝗘𝗛𝗔𝗩𝗜𝗢𝗥𝗔𝗟 𝗥𝗢𝗨𝗡𝗗 𝗧𝗜𝗣 to boost your chances to nail that job offer! Technical skills might get you through initial rounds, but behavioral rounds are where many stumble — especially with senior managers who really want to know if you fit the team. Here’s how to ace it: 1️⃣ When HR shares your interviewer's name, hunt for their LinkedIn profile. 2️⃣ Check out their work history and interests to find common ground. 3️⃣ Mention something relevant during the chat — it shows you’ve done your homework and builds rapport. 4️⃣ Remember, this round is two-way: they’re checking if you suit their culture, and you’re seeing if they suit your career goals. 5️⃣ So, ask smart questions about the role and company culture — it proves you’re genuinely interested. 💡 𝗣𝗿𝗼 𝘁𝗶𝗽: Stay polite but confident; senior leaders love that mix!

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𝗔𝗜/𝗠𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗕𝘆 𝗩𝗶𝘀𝗵𝗹𝗲𝘀𝗮𝗻 𝗶-𝗛𝘂𝗯, 𝗜𝗜𝗧 𝗣𝗮𝘁𝗻𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻😍 Freshers are getting paid 10 - 15 Lakhs by learning AI & ML skill Upgrade your career with a beginner-friendly AI/ML certification. 👉Open for all. No Coding Background Required 💻 Learn AI/ML from Scratch 🎓 Build real world Projects for job ready portfolio 🔥Deadline :- 19th April 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :- https://pdlink.in/41ZttiU . Get Placement Assistance With 5000+ Companies

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Which sorting algorithm requires extra memory?

Anonymous voting

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Which sorting algorithm repeatedly swaps adjacent elements?

Anonymous voting