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لا تقع ضحية للمخادعين! تيليمتريو يكتشف ويُميّز هذه القنوات 👉 إذا كنت تريد رؤية العلامة، اشترك

Data Science & Machine Learning

Data Science & Machine Learning

الذهاب إلى القناة على Telegram

The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages. For promotions: @love_data

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Data Science & Machine Learning

تُعد قناة Data Science & Machine Learning (@datascienceinterviews) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 27 593 مشتركاً، محتلاً المرتبة 7 077 في فئة التعليم والمرتبة 14 539 في منطقة الهند.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 27 593 مشتركاً.

بحسب آخر البيانات بتاريخ 25 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -86، وفي آخر 24 ساعة بمقدار -3، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.35‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 0.53‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 0 مشاهدة. وخلال اليوم الأول يجمع عادةً 146 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 0.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل insidead, mining, pinix, learning, neo.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages. For promotions: @love_data”

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 26 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.

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Stop guessing what your poop means 😅 Ok listen… most “gut issues” are just you not having any baseline. So I stole the simpl
Stop guessing what your poop means 😅 Ok listen… most “gut issues” are just you not having any baseline. So I stole the simplest roadmap: use AI GutCheck like a daily gut mirror 🪞💩 Do it inside this gut-tracking app and it gets weirdly clear, fast. - Snap a pic 📸 → it tags stool type (Bristol-style) + the stuff you’d miss (color/texture clues) - Log it daily 🗓️ → the timeline shows patterns after travel, stress, keto, antibiotics… all that - Get suggestions 🥤🥦 → hydration/fiber nudges that actually match what you logged - Keep it private 🔒 → no awkward convos, just quick “ok I’m fine / nope fix this” vibes ➡️ Run a quick gut check today #ad 📢 InsideAd

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Stop guessing what your poop means 😅 Ok listen… most “gut issues” are just you not having any baseline. So I stole the simpl
Stop guessing what your poop means 😅 Ok listen… most “gut issues” are just you not having any baseline. So I stole the simplest roadmap: use AI GutCheck like a daily gut mirror 🪞💩 Do it inside this gut-tracking app and it gets weirdly clear, fast. - Snap a pic 📸 → it tags stool type (Bristol-style) + the stuff you’d miss (color/texture clues) - Log it daily 🗓️ → the timeline shows patterns after travel, stress, keto, antibiotics… all that - Get suggestions 🥤🥦 → hydration/fiber nudges that actually match what you logged - Keep it private 🔒 → no awkward convos, just quick “ok I’m fine / nope fix this” vibes ➡️ Run a quick gut check today #ad 📢 InsideAd

🔹 DATA SCIENCE – INTERVIEW REVISION SHEET 1️⃣ What is Data Science? > “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.” Difference from Data Analytics: • Data Analytics → past  present (what/why) • Data Science → future  automation (what will happen) 2️⃣ Data Science Lifecycle (Very Important) 1. Business problem understanding 2. Data collection 3. Data cleaning  preprocessing 4. Exploratory Data Analysis (EDA) 5. Feature engineering 6. Model building 7. Model evaluation 8. Deployment  monitoring Interview line: > “I always start from business understanding, not the model.” 3️⃣ Data Types • Structured → tables, SQL • Semi-structured → JSON, logs • Unstructured → text, images 4️⃣ Statistics You MUST Know • Central tendency: Mean, Median (use when outliers exist) • Spread: Variance, Standard deviation • Correlation ≠ causation • Normal distribution • Skewness (income → right skewed) 5️⃣ Data Cleaning  Preprocessing Steps you should say in interviews: 1. Handle missing values 2. Remove duplicates 3. Treat outliers 4. Encode categorical variables 5. Scale numerical data Scaling: • Min-Max → bounded range • Standardization → normal distribution 6️⃣ Feature Engineering (Interview Favorite) > “Feature engineering is creating meaningful input variables that improve model performance.” Examples: • Extract month from date • Create customer lifetime value • Binning age groups 7️⃣ Machine Learning Basics • Supervised learning: Regression, Classification • Unsupervised learning: Clustering, Dimensionality reduction 8️⃣ Common Algorithms (Know WHEN to use) • Regression: Linear regression → continuous output • Classification: Logistic regression, Decision tree, Random forest, SVM • Unsupervised: K-Means → segmentation, PCA → dimensionality reduction 9️⃣ Overfitting vs Underfitting • Overfitting → model memorizes training data • Underfitting → model too simple Fixes: • Regularization • More data • Cross-validation 🔟 Model Evaluation Metrics • Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC • Regression: MAE, RMSE Interview line: > “Metric selection depends on business problem.” 1️⃣1️⃣ Imbalanced Data Techniques • Class weighting • Oversampling / undersampling • SMOTE • Metric preference: Precision, Recall, F1, ROC-AUC 1️⃣2️⃣ Python for Data Science Core libraries: • NumPy • Pandas • Matplotlib / Seaborn • Scikit-learn Must know: • loc vs iloc • Groupby • Vectorization 1️⃣3️⃣ Model Deployment (Basic Understanding) • Batch prediction • Real-time prediction • Model monitoring • Model drift Interview line: > “Models must be monitored because data changes over time.” 1️⃣4️⃣ Explain Your Project (Template) > “The goal was . I cleaned the data using . I performed EDA to identify . I built model and evaluated using . The final outcome was .” 1️⃣5️⃣ HR-Style Data Science Answers Why data science? > “I enjoy solving complex problems using data and building models that automate decisions.” Biggest challenge: “Handling messy real-world data.” Strength: “Strong foundation in statistics and ML.” 🔥 LAST-DAY INTERVIEW TIPS • Explain intuition, not math • Don’t jump to algorithms immediately • Always connect model → business value • Say assumptions clearly Double Tap ♥️ For More

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10 SQL Concepts Every Data Analyst Should Master 👇 ✅ SELECT, WHERE, ORDER BY – Core of querying your data ✅ JOINs (INNER, LEFT, RIGHT, FULL) – Combine data from multiple tables ✅ GROUP BY & HAVING – Aggregate and filter grouped data ✅ Subqueries – Nest queries inside queries for complex logic ✅ CTEs (Common Table Expressions) – Write cleaner, reusable SQL logic ✅ Window Functions – Perform advanced analytics like rankings & running totals ✅ Indexes – Boost your query performance ✅ Normalization – Structure your database efficiently ✅ UNION vs UNION ALL – Combine result sets with or without duplicates ✅ Stored Procedures & Functions – Reusable logic inside your DB React with ❤️ if you want me to cover each topic in detail Share with credits: https://t.me/sqlspecialist Hope it helps :)

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