Channel
@Data_ScienceR
On this record: Growth · Engagement · What this channel posts · Posts · Cite this entry
2,284subscribers
-10 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001106225263 |
|---|---|
| Type | Channel |
| Username | @Data_ScienceR |
| Created | Between 1 February 2017 and 31 May 2019— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 25 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 25 August 2026 |
| On Telegram | t.me/Data_ScienceR |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 25 Aug 2026, 00:18 | 2,284 | -2 |
| 21 Aug 2026, 03:07 | 2,286 | -5 |
| 18 Aug 2026, 10:43 | 2,291 | -3 |
| 12 Aug 2026, 21:13 | 2,294 | +1 |
| 9 Aug 2026, 21:12 | 2,293 | -2 |
| 6 Aug 2026, 19:30 | 2,295 | +1 |
| 6 Aug 2026, 12:32 | 2,294 | first reading |
19 posts held, back to 6 November 2021 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 19 posts for this entry, the most recent from 21 July 2024. An engagement rate over an empty window would be a number about nothing.
Measured directly from 5 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
🔰 Clustering Models The choice of clustering model depends on the characteristics of the input data, such as size, shape, density, and the presence of noise or outliers. #DBSCAN #spectralClustering 🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰 ❇️ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
🚀🔰 Data engineer vs Data Analyst vs Data Scientist vs ML Engineer #mlops 🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰 🌐👇 link: https://medium.com/@khandanihossein69/data-engineer-vs-data-scientist-vs-ml-engineer-31f227ee4976
Signed Hossein Khandani
📒🔰 Data engineer vs Data scientist vs ML Engineer #data_scientist #ml_engineer 🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰 ❇️ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
📒 Various Types of Regression in Econometrics #Regression
Signed Hossein Khandani
📒 🚀 Best Free AI Tools #AI #chatgpt 🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰 ❇️ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
🔰 🚀Introducing Microsoft Fabric: Data analytics for the era of AI
Signed Hossein Khandani
🚀✅Shiny for python is now generally available (🖥 Web Application | Dashboard) 📒 شاینی برای پایتون (داشبورد و وب اپلیکیشن) 🌐👇 https://posit.co/blog/shiny-for-python-is-now-generally-available/ #shiny #python 🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰 ❇️ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
🅰️ پارت اول: معرفی و نصب فریمورک و اپلیکیشن #یادگیری عمیق در #آر و #پایتون 📒Part(1): AutoML with #H2O in R and Python ❇مزیت ها: امکان پیاده سازی مدل های یادگیری ماشین و عمیق انجام پیاده سازی #نیمه_اتوماتیک و بدون نیاز به #کد نویسی با #واسط_کاربری مدرس: خاندانی #AutoML #Deep_learning #H2O ➖➖➖➖➖➖➖➖➖ Link ❇ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
🅱️✴️پارت دوم: پیاده سازی مدل ها و الگوریتم های #یادگیری عمیق در #آر و #پایتون - خودکار سازی فرآیندهای یادگیری ماشین و عمیق #Part2: 📕 #AutoML with #H2O in R and Python ❇مزیت ها: امکان پیاده سازی مدل های یادگیری ماشین و #عمیق انجام پیاده سازی #نیمه_اتوماتیک و بدون نیاز به #کدنویسی با #واسط_کاربری مدرس: خاندانی #AutoML #Deep_learning #H2O ➖➖➖➖➖➖➖➖➖ Link ❇ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
🚀 Speed up your #Python code (big data and large dataset ) ☸️ افزایش #سرعت پردازش داده ها در پیش پردازش و دستکاری داده های حجیم در #پایتون 📒Python: #Speed boot on the Data#wrangling and Data #Manipulation operations on Large dataset and #big data (#Datatable) مشاهده در آپارات: https://www.aparat.com/v/fyCcH?playlist=312135 رفرنس: https://h2oai.github.io/db-benchmark/ مدرس: خاندانی #spark #datatable #bigdata ➖➖➖…
Signed Hossein Khandani
🚀 Speed up (big data and large dataset ) ☸️ افزایش سرعت پردازش داده ها در پیش پردازش و دستکاری داده های حجیم 📒A 3X #Speed boot on the Data#wrangling and Data #Manipulation operations on Large dataset and #big data مشاهده در آپارات: https://www.aparat.com/v/isoyE مدرس: خاندانی #spark #dtplyr #bigdata ➖➖➖➖➖➖➖➖➖ Link ❇️ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
✅ Big Data Pipelines on Azure, AWS, Google Cloud 🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰🟰 👇 ❇️ https://t.me/Data_ScienceR @Data_ScienceR
Signed Hossein Khandani
Showing the 12 most recent of 19 posts we hold for @Data_ScienceR. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 25 August 2026 — this entry's latest reading, not the date you are reading this.
“Data Science” (@Data_ScienceR), 2,284 subscribers as measured 25 August 2026. Telegram Register, tgregister.com/channel/Data_ScienceR.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.