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Data Science & Machine Learning

Data Science & Machine Learning

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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data

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📈 Análisis del canal de Telegram Data Science & Machine Learning

El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 75 933 suscriptores, ocupando la posición 2 103 en la categoría Educación y el puesto 4 204 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 933 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 731, y en las últimas 24 horas de 33, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.95%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.86% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 2 239 visualizaciones. En el primer día suele acumular 650 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
  • Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 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 Educación.

75 933
Suscriptores
+3324 horas
+587 días
+73130 días
Archivo de publicaciones
Coding and Aptitude Round before interview Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking. Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round. Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you. Resources for Prep: For algorithms and data structures prep,Leetcode and Hackerrank are good resources. For aptitude prep, you can refer to IndiaBixand Practice Aptitude. With respect to data science challenges, practice well on GLabs and Kaggle. Brilliant is an excellent resource for tricky math and statistics questions. For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself. Things to Note: Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do! In case, you are finished with the test before time, recheck your answers and then submit. Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

New research paper by Facebook on End-to-End Object Detection with Transformers https://research.fb.com/publications/end-to-end-object-detection-with-transformers/

Data Visualization Cheat Sheet

🔶Udacity - Data Analyst Nanodegree v1.0.0🔶 Download link: https://mega.nz/folder/GM0QiCjL#zSyBWDlIFEPtorGw_nI7cQ

Machine Learning Glossary This glossary defines general machine learning terms, plus terms specific to TensorFlow https://developers.google.com/machine-learning/glossary/#convolutional_neural_network