Data science/ML/AI

Ir al canal en Telegram

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 607 Tecnologías y Aplicaciones9 380...

📈 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 9 380 en la categoría Tecnologías y Aplicaciones y el puesto 31 607 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 10 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 143, y en las últimas 24 horas de 2, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.09%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.22% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 1 106 visualizaciones. En el primer día suele acumular 304 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 11 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

+224 horas

+217 días

+14330 días

1 106

Visitas de la publicación

~ 30424 horas

~ 44448 horas

8.09%

Tasa de compromiso

~ 1

Mensajes por día

Ads index

beta

Archivo de publicaciones

13 677

Data Science Techniques

13 677

WHICH CHART WHEN? The data Analyst's guide to choosing the right charts

13 677

Cloud Engineer Roadmap

13 677

Self guide to become a data analyst

13 677

Repost from Data science research papers

Going Denser with Open-Vocabulary Part Segmentation Publication date: 18 May 2023 Topic: Object detection Paper: https://arxiv.org/pdf/2305.11173v1.pdf GitHub: https://github.com/facebookresearch/vlpart Description: Object detection has been expanded from a limited number of categories to open vocabulary. Moving forward, a complete intelligent vision system requires understanding more fine-grained object descriptions, object parts. In this work, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation. This ability comes from two designs: 🔹 We train the detector on the joint of part-level, object-level and image-level data. 🔹 We parse the novel object into its parts by its dense semantic correspondence with the base object.

13 677

Roadmap to Devops

13 677

Why Statistics Matter in Data Science even in 2023

13 677

How to choose a graph

13 677

One question to make your data project 10x more valuable If you are the "data person" for your organization, then providing meaningful results to stakeholder data requests can sometimes feel like shots in the dark. However, you can make sure your data analysis is actionable by asking one magic question before getting started. The magic question Luckily, we don't need to spend all of our time defining the problem. Here is the one simple question that will get to the heart of any data request within minutes: "What decision are you trying to make?" Subtext: What action will you take once you have the answers? If there is no action, then there will be no impact. This question will cut through all of the clutter and get straight to the action. And the answer can be VERY telling! That's why it's so powerful. A good response is specific! Almost immediately, you should be able to picture what they'll do once they see the data. 🔗 Read more

13 677

Introduction to Data Science

13 677

R, ggplot, and Simple Linear Regression Begin to use R and ggplot while learning the basics of linear regression Rating ⭐️: 4.1 out 5 Students 👨‍🎓 : 42,633 Duration ⏰ : 2hr 14min of on-demand video Created by 👨‍🏫: Charles Redmond 🔗 Course Link #R #linear #Regression ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

13 677

Data Engineer's Pathway

13 677

Data Science: Theories, Models, Algorithms, and Analytics by SANJIV RANJAN DAS

13 677

Essential AI Tools For Data Analysis

13 677

Top 5 Reasons Why Machine Learning Projects Fail The intent of our article today is to help you get acquainted with the many reasons behind machine learning projects’ failure. We are hopeful that the information will help you plan a better implementation, one that carries fewer chances of failure in all three stages of ML execution: pre-project, during the project, and post-project. 1. Insufficient data 2. ML Models unsynchronized with the legacy systems 3. Lack of enough data scientists 4. Difficulty in updating 5. Lack of leaders’ support The solution to addressing these challenges more often than not lies with partnering with a skilled machine learning solution provider company that understands both business and technical implications of applying a new-gen technology in a non-digital organization. They can help you in not just creating a work plan of how to integrate machine learning projects but also with adopting the new system in the most optimal way. 🔗 Read more

13 677

Python Libraries For Data Science

13 677

7 Platforms for Getting High Paying Data Science Jobs 1. LinkedIn 2. Wellfound 3. Toptal 4. Upwork 5. Kolabtree 6. Indeed 7. Amazon Jobs

13 677

Logistic Regression Practical Case Study Breast Cancer detection using Logistic Regression Rating ⭐️: 4.7 out 5 Students 👨‍🎓 : 35,819 Duration ⏰ : 1hr 4min of on-demand video Created by 👨‍🏫: Hadelin de Ponteves, SuperDataScience Team, Ligency Team 🔗 Course Link #Logistic #Regression ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

13 677

8 Books that Will Teach You the Basics of Data Science In an era where data is hailed as the new oil, the demand for data scientists continues to soar. Data science, a multidisciplinary field that extracts insights and knowledge from data, has become a cornerstone of many industries. For those aspiring to enter this dynamic field, building a solid foundation is essential. Books are a timeless source of knowledge, and in this article, we’ll explore eight must-read books that will teach you the basics of data science, making your journey into this fascinating world more accessible. 1. “Python for Data Analysis” by Wes McKinney Wes McKinney’s book is a fantastic starting point for beginners. It focuses on the practical use of Python, one of the most popular programming languages in data science. You’ll learn how to work with data structures, perform data cleaning, and apply statistical analysis. The book also introduces the powerful Pandas library for data manipulation. Source-Link: analyticsinsight