Data science/ML/AI
Відкрити в 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
Показати більше
Сітка:📈 Аналітичний огляд Telegram-каналу Data science/ML/AI
Канал Data science/ML/AI (@datascience_bds) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 13 906 підписників, посідаючи 8 914 місце в категорії Технології та додатки та 28 863 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 13 906 підписників.
За останніми даними від 29 серпня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 86, а за останні 24 години на 0, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 7.48%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.04% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 039 переглядів. Протягом першої доби публікація в середньому набирає 284 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 5.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як panda, learning, row, api, ethic.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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...”
Завдяки високій частоті оновлень (останні дані отримано 30 серпня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
13 906
Підписники
Немає даних24 години
-57 днів
+8630 день
Архів дописів
13 906
R for Data Science
A weekly data project aimed at the R ecosystem. As this project was borne out of the R4DS Online Learning Community and the R for Data Science textbook, an emphasis was placed on understanding how to summarize and arrange data to make meaningful charts with ggplot2, tidyr, dplyr, and other tools in the tidyverse ecosystem. However, any code-based methodology is welcome - just please remember to share the code used to generate the results.
Creator: rfordatascience
Stars ⭐️: 5.6k
Forked By: 2.3k
https://github.com/rfordatascience/tidytuesday
#R #data_science
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
13 906
Get started in Data Science with Microsoft's FREE course for beginners.
- 10 weeks
- 20 lessons
- Lecture notes
- 100% FREE
https://microsoft.github.io/Data-Science-For-Beginners/
13 906
R Programming Language free courses
R Programming Tutorial - Learn the Basics of Statistical Computing
🆓 Free Online Course
🎬 20 video lesson
Duration ⏰: 2-3 hours worth of material
🏃♂️ Self paced
Resource: freecodecamp
🔗 Course Link
NOC:Foundations of R Software, IIT Kanpur
🎬 53 video lesson
⏰ 12 Modules
Taught by: Prof. Shalabh
Source: NPTEL
🔗 Course Link
R Basics - R Programming Language Introduction
Rating ⭐️: 4.6 out of 5
Students 👨🎓: 207,088
Duration ⏰: 4hr 06min
Created by: R-Tutorials Training
🔗 Course Link
R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
🆓 Free Online Course
🎬 8 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Resource: freecodecamp
🔗 Course Link
NOC:Essentials of Data Science With R Software _ 1: Probability and Statistical Inference, IIT Kanpur
🎬 71 video lesson
⏰ 13 Modules
Taught by: Prof. Shalabh
Source: NPTEL
🔗 Course Link
NOC:Essentials of Data Science With R Software _ 2: Sampling Theory and Linear Regression Analysis, IIT Kanpur
🎬 51 video lesson
⏰ 13 Modules
Taught by: Prof. Shalabh
Source: NPTEL
🔗 Course Link
Mastering R Programming (Apr 2023)
Rating ⭐️: 4.3 out of 5
Students 👨🎓: 6,161
Duration ⏰: 1hr 47min
Created by: Proton Expert Systems & Solutions
🔗 Course Link
Statistical Computing with R - a gentle introduction (Login Required)
🆓 Free Online Course
Duration ⏰: 6-8 Hours study
🏃♂️ Self paced
Teacher: Max Reuter, Chris Barnes
Resource: University College London
🔗 Course Link
R Programming For Beginners-Full Course | Learn R in 3 Hours| R Language Tutorial | Great Learning
🆓 Free Online Course
🎬 14 video lesson
Duration ⏰: 3-4 hours worth of material
🏃♂️ Self paced
Resource: Great Learning
🔗 Course Link
NOC:Business analytics and data mining Modeling using R, IIT Roorkee
🎬 60 video lesson
⏰ 12 Modules
Taught by: Dr. Gaurav Dixit
Source: NPTEL
🔗 Course Link
Learn Live - Explore and analyze data with R
🆓 Free Online Course
🎬 9 video lesson
Duration ⏰: 1-2 hours worth of material
🏃♂️ Self paced
Resource: Class Central
🔗 Course Link
R Programming Full Course for 2023 | R Programming For Beginners | R Tutorial | Simplilearn
🆓 Free Online Course
🎬 1 video lesson
Duration ⏰: 10-11 hours worth of material
🏃♂️ Self paced
Resource: Youtube
🔗 Course Link
Books
The Book of R
R Programming for Data Science - Roger D. Peng
R for Beginners
#R #R_Language #R_Programming_Language
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉Join @bigdataspecialist for more👈
13 906
Data Science Helps Engineers Discover New Materials for Solar Cells and LEDs
13 906
How To Label Data
At LightTag, we create tools to annotate data for natural language processing (NLP). At its core, the process of annotating at scale is a team effort. Managing the annotation process draws on the same principles as managing any other human endeavor. You need to clearly understand what needs to be done, articulate it repeatedly to your team, give them the tools and training to execute effectively, measure their performance against your goals, and help them improve over time. we will draw on our experience with various annotation projects to describe the seven distinct stages of an annotation life cycle that Jane will go through. We will explain the purpose of each stage, describe key considerations that should occur during each, and wrap each stage up with the assets you should expect to have at the end.
Link
#ml #data_science
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
Data Science Engineering, your way
An introduction to different Data Science engineering concepts and Applications using Python and R
These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python.
We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.
To get a feeling of what is going on regarding this hot topic, we refer the reader to DataCamp's Data Science War infographic. Their infographic explores what the strengths of R are over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.
Far from being a repetition from the previous, our series of tutorials will go hands-on into how to actually perform different data science taks such as working with data frames, doing aggregations, or creating different statistical models such in the areas of supervised and unsupervised learning.
We will use real-world datasets, and we will build some real data products. This will help us to quickly transfer what we learn here to actual data analysis situations.
Link
#ai #ml #data_science
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
Google just dropped Generative AI learning path with 9 courses:
🤖: Intro to Generative AI
🤖: Large Language Models
🤖: Responsible AI
🤖: Image Generation
🤖: Encoder-Decoder
🤖: Attention Mechanism
🤖: Transformers and BERT Models
🤖: Create Image Captioning Models
🤖: Intro to Gen AI Studio
https://www.cloudskillsboost.google/paths/118
13 906
1000 Data Science Projects
you can run on the browser with IPython.
Explore from 1000+ ready code templates to kickstart your AI projects
⭐️Classification
⭐️Regression
⭐️Clustering
🔗 Source link
#ai #ml #data_science #deep_learning
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
Datasets for Data Science and Machine Learning
Ten years ago, it use be years ago quite difficult to find good datasets for data science and machine learning projects. Today, we have the opposite problem.
We’ve been flooded with lists and lists of datasets. The problem nowadays is not finding datasets, but rather sifting through them to keep the relevant ones.
Well, we’ve done that for you right here.
Below, you’ll find a curated list of free datasets for data science and machine learning, organized by their use case. You’ll find both hand-picked datasets and our favorite aggregators.
✅ Exploratory Analysis
✅ General Machine Learning
✅ Deep Learning
✅ Natural Language Processing
✅ Cloud-Based Machine Learning
✅ Time Series Analysis
✅ Recommender Systems
✅ Specific Industries
✅ Streaming Data
✅ Web Scraping
✅ Current Events
🔗 Source Link
#Data_Science #python #datasets
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
How do Transformers work?
All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!
This type of model develops a statistical understanding of the language it has been trained on, but it’s not very useful for specific practical tasks. Because of this, the general pretrained model then goes through a process called transfer learning. During this process, the model is fine-tuned in a supervised way — that is, using human-annotated labels — on a given task
🔗 Read More
13 906
Data Science With Python Workflow Cheat Sheet
Creator: business Science
Stars ⭐️: 75
Forked By: 38
https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf
#Data #Science #cheatSheet
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
