Channel
@towards_data_science
On this record: Topic · Growth · Engagement · Posts · Cite this entry
405subscribers
-8 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001456902406 |
|---|---|
| Type | Channel |
| Username | @towards_data_science |
| Created | Between 1 April 2019 and 31 October 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 4 October 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 4 October 2026 |
| On Telegram | t.me/towards_data_science |
Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 22 September 2026 and assigned it the closest of 31 fixed categories, at 68% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 4 Oct 2026, 15:36 | 405 | -3 |
| 11 Sept 2026, 19:41 | 408 | -1 |
| 3 Sept 2026, 17:39 | 409 | -3 |
| 21 Aug 2026, 18:05 | 412 | -1 |
| 8 Aug 2026, 05:34 | 413 | no change |
| 7 Aug 2026, 10:13 | 413 | first reading |
20 posts held, back to 18 February 2023 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 page of 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 20 posts for this entry, the most recent from 22 February 2023. An engagement rate over an empty window would be a number about nothing.
#machinelearning Understanding Gradient Boosting: A Data Scientists Guide Discover the power of gradient boosting in this comprehensive guide for data scientists. Learn about weak learners, additive models, loss…
#machinelearning Studying the Gender Wage Gap in the US Using Distributional Random Forests An example of a real data analysis with DRF
#machinelearning When Stochastic Policies Are Better Than Deterministic Ones Why we let randomness dictate our action selection in Reinforcement Learning
#machinelearning Outlier Detection Using Distribution Fitting in Univariate Datasets Learn how to detect outliers using Probability Density Functions and bring fast and explainable results without training labels
#artificialintelligence How to Create Videos with Stable Diffusion Using frame interpolation to create videos with Stable Diffusion and Deforum
#artificialintelligence Unleashing the Power of GPT: How to Fine-Tune Your Model Step-by-step guide to GPT fine-tuning
#datavisualization Is Tourism Back to Its Pre-COVID-Crisis Level? Conducting an end-to-end analysis to answer a socio-economic question with nice graphs
#datascience Building Comprehension Pipelines in Python Comprehension pipelines are a Python-specific idea for building pipelines
#datascience Introduction of Four Types of Item Similarity Measures Covers how to choose the similarity measure when item embeddings are available
#artificialintelligence The DVC Guide: Data Version Control For All Your Data Science Projects Become familiar with data versioning just like code versioning
#datascience Step by Step Basics: Text Classifier An Instructional Guide and Flow Diagram for Building a Supervised Machine Learning Text Classifier in Python
#datascience Inference for Distributional Random Forests Confidence intervals for a powerful nonparametric method
Showing the 12 most recent of 20 posts we hold for @towards_data_science. 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 4 October 2026 — this entry's latest reading, not the date you are reading this.
“TDS” (@towards_data_science), 405 subscribers as measured 4 October 2026. Telegram Register, tgregister.com/channel/towards_data_science.
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.