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Data science/ML/AI

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 πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Programming, data science, ML - free courses by Big Data SpecialistNetwork:Programming, data science, ML - free courses by Big Data SpecialistIndia28 863Technologies & Applications8 914

πŸ“ˆ Analytical overview of Telegram channel Data science/ML/AI

Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 906 subscribers, ranking 8 914 in the Technologies & Applications category and 28 863 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 13 906 subscribers.

According to the latest data from 29 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 86 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 7.48%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 039 views. Within the first day, a publication typically gains 284 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œData science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Thanks to the high frequency of updates (latest data received on 30 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

13 901
Subscribers
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+8630 days
Posts Archive
Data science/ML/AI
Data science/ML/AI
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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
21 most important equations in data science
21 most important equations in data science

Data science/ML/AI
Data science/ML/AI
13 906
Essential Charts for Data Analysis
Essential Charts for Data Analysis

Data science/ML/AI
Data science/ML/AI
13 906
Get started in Data Science with Microsoft's FREE course for beginners. - 10 weeks - 20 lessons - Lecture notes - 100% FREE h
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/

Data science/ML/AI
Data science/ML/AI
13 906
6 key data terms you should know
6 key data terms you should know

Data science/ML/AI
Data science/ML/AI
13 906
Repost from Programming, data science, ML - free courses by Big Data Specialist
R Programming Language free courses R Programming Tutorial - Learn the Basics of Statistical Computing πŸ†“ Free Online Course 🎬 20 video lesson Duration ⏰: 2-3 hours worth of material πŸƒβ€β™‚οΈ Self paced Resource: freecodecamp πŸ”— Course Link NOC:Foundations of R Software, IIT Kanpur 🎬 53 video lesson ⏰ 12 Modules Taught by: Prof. Shalabh Source: NPTEL πŸ”— Course Link R Basics - R Programming Language Introduction Rating ⭐️: 4.6 out of 5 Students πŸ‘¨β€πŸŽ“: 207,088 Duration ⏰: 4hr 06min Created by: R-Tutorials Training πŸ”— Course Link R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps πŸ†“ Free Online Course 🎬 8 video lesson Duration ⏰: 1-2 hours worth of material πŸƒβ€β™‚οΈ Self paced Resource: freecodecamp πŸ”— 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 πŸ”— 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 πŸ”— Course Link Mastering R Programming (Apr 2023) Rating ⭐️: 4.3 out of 5 Students πŸ‘¨β€πŸŽ“: 6,161 Duration ⏰: 1hr 47min Created by: Proton Expert Systems & Solutions πŸ”— Course Link Statistical Computing with R - a gentle introduction (Login Required) πŸ†“ Free Online Course Duration ⏰: 6-8 Hours study πŸƒβ€β™‚οΈ Self paced Teacher: Max Reuter, Chris Barnes Resource: University College London πŸ”— Course Link R Programming For Beginners-Full Course | Learn R in 3 Hours| R Language Tutorial | Great Learning πŸ†“ Free Online Course 🎬 14 video lesson Duration ⏰: 3-4 hours worth of material πŸƒβ€β™‚οΈ Self paced Resource: Great Learning πŸ”— 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 πŸ”— Course Link Learn Live - Explore and analyze data with R πŸ†“ Free Online Course 🎬 9 video lesson Duration ⏰: 1-2 hours worth of material πŸƒβ€β™‚οΈ Self paced Resource: Class Central πŸ”— Course Link R Programming Full Course for 2023 | R Programming For Beginners | R Tutorial | Simplilearn πŸ†“ Free Online Course 🎬 1 video lesson Duration ⏰: 10-11 hours worth of material πŸƒβ€β™‚οΈ Self paced Resource: Youtube πŸ”— Course Link Books The Book of R R Programming for Data Science - Roger D. Peng R for Beginners #R #R_Language #R_Programming_Language βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– πŸ‘‰Join @bigdataspecialist for moreπŸ‘ˆ

Data science/ML/AI
Data science/ML/AI
13 906
Data Science Helps Engineers Discover New Materials for Solar Cells and LEDs
Data Science Helps Engineers Discover New Materials for Solar Cells and LEDs

Data science/ML/AI
Data science/ML/AI
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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
Using data science to discover materials with extreme properties

Data science/ML/AI
Data science/ML/AI
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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
photo content

Data science/ML/AI
Data science/ML/AI
13 906
Google just dropped Generative AI learning path with 9 courses: πŸ€–: Intro to Generative AI πŸ€–: Large Language Models πŸ€–: Responsible AI πŸ€–: Image Generation πŸ€–: Encoder-Decoder πŸ€–: Attention Mechanism πŸ€–: Transformers and BERT Models πŸ€–: Create Image Captioning Models πŸ€–: Intro to Gen AI Studio https://www.cloudskillsboost.google/paths/118

Data science/ML/AI
Data science/ML/AI
13 906
Data-Driven Materials Science: Status, Challenges, and Perspectives
Data-Driven Materials Science: Status, Challenges, and Perspectives

Data science/ML/AI
Data science/ML/AI
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 πŸ”— Source link #ai #ml #data_science #deep_learning βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
Repost from Python Learning
Machine Learning Engineer Roadmap
Machine Learning Engineer Roadmap

Data science/ML/AI
Data science/ML/AI
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. βœ… Exploratory Analysis βœ… General Machine Learning βœ… Deep Learning βœ… Natural Language Processing βœ… Cloud-Based Machine Learning βœ… Time Series Analysis βœ… Recommender Systems βœ… Specific Industries βœ… Streaming Data βœ… Web Scraping βœ… Current Events πŸ”— Source Link #Data_Science #python #datasets βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 906
Data Science Workflow
Data Science Workflow

Data science/ML/AI
Data science/ML/AI
13 906
How do Transformers work? All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as languag
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 β€” that is, using human-annotated labels β€” on a given task πŸ”— Read More

Data science/ML/AI
Data science/ML/AI
13 906
Data Science Lifestyle
Data Science Lifestyle

Data science/ML/AI
Data science/ML/AI
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 βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group