Data science/ML/AI

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

Red:Programming, data science, ML - free courses by Big Data Specialist India31 635 Tecnologías y Aplicaciones9 377...

📈 Análisis del canal de Telegram Data science/ML/AI

El canal Data science/ML/AI (@datascience_bds) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 13 672 suscriptores, ocupando la posición 9 377 en la categoría Tecnologías y Aplicaciones y el puesto 31 635 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 672 suscriptores.

Según los últimos datos del 09 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 155, y en las últimas 24 horas de 5, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.03%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.25% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 098 visualizaciones. En el primer día suele acumular 308 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
Intereses temáticos: El contenido se centra en temas clave como panda, learning, row, api, ethic.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 10 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

13 672

Suscriptores

+524 horas

+197 días

+15530 días

1 098

Visitas de la publicación

~ 30824 horas

~ 45248 horas

8.03%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

13 672

📚 Data Science Riddle Why do CNNs use pooling layers?

Anonymous voting

13 672

Why is Kafka Called Kafka❔ Here’s a fun fact that surprises a lot of people. The “Kafka” you use for real-time data pipelines is… named after the novelist Franz Kafka. Why? Jay Kreps (the creator) once explained it simply: - He liked the name. - It sounded mysterious. - And Kafka (the author) wrote a lot. That last part is key. Because Apache Kafka is all about writing: streams of events, logs, and data in motion. So the name stuck. Today, Millions of engineers across the globe talk about “Kafka” every single day… and most don’t realize they’re also invoking a 20th-century novelist. It's funny how small choices like naming your project can shape how the world remembers it.

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Cheatsheet: Bayes Theroem And Classifier

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Important LLM Terms 🔹 Transformer Architecture 🔹 Attention Mechanism 🔹 Pre-training 🔹 Fine-tuning 🔹 Parameters 🔹 Self-Attention 🔹 Embeddings 🔹 Context Window 🔹 Masked Language Modeling (MLM) 🔹 Causal Language Modeling (CLM) 🔹 Multi-Head Attention 🔹 Tokenization 🔹 Zero-Shot Learning 🔹 Few-Shot Learning 🔹 Transfer Learning 🔹 Overfitting 🔹 Inference 🔹 Language Model Decoding 🔹 Hallucination 🔹 Latency

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📚 Data Science Riddle In a medical diagnosis project, what's more important?

Anonymous voting

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ML models don’t all think alike 🤖 ❇️ Naive Bayes = probability ❇️ KNN = proximity ❇️ Discriminant Analysis = decision boundaries Different paths, same goal: accurate classification. Which one do you reach for first?

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📚 Data Science Riddle A dataset has 20% missing values in a critical column. What's the most practical choice?

Anonymous voting

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Introduction To Linear Regression

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SQL JOINS

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📚 Data Science Riddle Which Metric is best for imbalanced classification?

Anonymous voting

13 672

Machine Learning Cheatsheet

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Most Common Data Science Skills in Job Posting

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📊 Infographic Elements That Every Data Person Should Master 🚀 After years of working with data, I can tell you one thing: 👉 The chart ou choose is as important as the data itself. Here’s your quick visual toolkit 👇 🔹 Timelines * Sequential ⏩ great for processes * Scaled ⏳ best for real dates/events 🔹 Circular Charts * Donut 🍩 & Pie 🥧 for proportions * Radial 🌌 for progress or cycles * Venn 🎯 when you want to show overlaps 🔹 Creative Comparisons * Bubble 🫧 & Area 🔵 for impact by size * Dot Matrix 🔴 for colorful distributions * Pictogram 👥 when storytelling matters most 🔹 Classic Must-Haves * Bar 📊 & Histogram 📏 (clear, reliable) * Line 📈 for trends * Area 🌊 & Stacked Area for the “big picture” 🔹 Advanced Tricks * Stacked Bar 🏗 when categories add up * Span 📐 for ranges * Arc 🌈 for relationships 💡 Pro tip from experience: If your audience doesn’t “get it” in 3 seconds, change the chart. The best visualizations speak louder than numbers

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INFOGRAPHIC ELEMENTS

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📚 Data Science Riddle Why does bagging reduce variance?

Anonymous voting

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Big Data 5V

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Great Packages for R

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📚 Data Science Riddle Which algorithm is most sensitive to feature scaling?

Anonymous voting

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The RAG Developer Stack 2025 - Build Intelligent Al That Thinks, Remembers & Acts