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

Network:Programming, data science, ML - free courses by Big Data Specialist India31 607 Technologies & Applications9 380...

📈 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 13 674 subscribers, ranking 9 380 in the Technologies & Applications category and 31 607 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 674 subscribers.

According to the latest data from 10 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 143 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 8.09%. Within the first 24 hours after publication, content typically collects 2.22% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 106 views. Within the first day, a publication typically gains 304 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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...”

Thanks to the high frequency of updates (latest data received on 11 June, 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.

13 674

Subscribers

+224 hours

+217 days

+14330 days

1 106

Post views

~ 30424 hours

~ 44448 hours

8.09%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

13 674

Data Science vs Mathematics

13 674

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👈

13 674

Completely unimportant but interesting fact we have 7777 subscribers ATM

13 674

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👈

13 674

The Data Science Sandwich

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Enjoy our content? Advertise on this channel and reach a highly engaged audience! 👉🏻 It's easy with http://Telega.io. As the leading platform for native ads and integrations on Telegram, it provides user-friendly and efficient tools for quick and automated ad launches. ⚡️ Place your ad here in three simple steps: 1 Sign up: https://telega.io/c/datascience_bds 2 Top up the balance in a convenient way 3 Create your advertising post If your ad aligns with our content, we’ll gladly publish it. Start your promotion journey now!

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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

13 674

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👈

13 674

The LLM Scientist Roadmap

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LLMOps vs MLOps

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Design patterns for AI Agentic workflow in LLM applications