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 13 674 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 13 674 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.

13 674

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

13 674

Data Science vs Mathematics

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Python for Data Science with Assignments A Comprehensive and Practical Hands-On Guide to Learning Python for Beginners, Aspiring Developers, Self-Learners, etc. Rating ⭐️: 4.7 out 5 Students 👨‍🎓 : 18046 Duration ⏰ : 9.5 hours on-demand video Created by 👨‍🏫: Meritshot Academy 🔗 Course Link ⚠️ Its free for first 1000 enrollments only! #python #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

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Completely unimportant but interesting fact we have 7777 subscribers ATM

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Statistics test flow chart

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Accelerate Data Science Workflows with Zero Code Changes by nvidia Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows. By participating in this course, you will: Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes Experience the significant reduction in processing time when workflows are GPU-accelerated Prerequisites: Basic understanding of data processing and knowledge of a standard data science workflow on tabular data Experience using common Python libraries for data analytics Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX 🆓 Free Online Course ⏰ Duration : More than 1 hour 🏃‍♂️ Self paced ✅ Certification available Course Link #datascience #nvidia ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @bigdataspecialist for more👈

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The Data Science Sandwich

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Data Science Techniques

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Important Data Terms

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Statistical models cheatsheet

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Harolds_Stats_Distributions_Cheat_Sheet.pdf1.16 MB

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Statistical distributions cheatsheet

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Career Path of A Data Analyst

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Flow chart of commonly used statistical tests

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Introduction to Probability and Statistics for Engineers List of probability and statistics cheatsheets by Stanford

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Brain of an AI Engineer

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[Compilation]1000+ Data Science Interview Questions/Preparation Resources Compilation created by kaggle users 1. GIT interview questions for DS and SQL Interview questions 2. 50 ML questions 3. Four years on interview questions 4. Compilation of pandas interview questions 5. Difference between common ML algortihms 6. Scenario based Data questions 7. Top python interview questions 8. Internship questions for DS interns 9. Questions from DS- Netflix 10. India specific Data science interview questions 11. R interview questions 12. Explain a project in Data science 13. A great collection of cheatsheets, analyzed here 14. A collection of questions on Github here 15. Cheat Sheets for Machine Learning Interview Topics 16. Compiled list of 600+ Q&As for Data Science interview prep 🎉 17. Approaching almost any ML Problem, originally shared on Kaggle 18. A Basics refresher 19. A notebook 20. Companies and Data Science Interview questions Megathread 21. Data Scientist - Interview Question Bank 22. ML Interview questions 23. Machine Learning Interviews Book 👇 https://www.kaggle.com/discussions/questions-and-answers/239533 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

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The LLM Scientist Roadmap

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LLMOps vs MLOps

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Design patterns for AI Agentic workflow in LLM applications