Channel
@data_science_weekly
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Cite this entry
679subscribers
+5 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001654768758 |
|---|---|
| Type | Channel |
| Username | @data_science_weekly |
| Created | Between 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 23 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 23 August 2026 |
| On Telegram | t.me/data_science_weekly |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 23 Aug 2026, 00:44 | 679 | +5 |
| 8 Aug 2026, 02:32 | 674 | no change |
| 7 Aug 2026, 14:33 | 674 | first reading |
20 posts held, back to 22 March 2026 — 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 pageof Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 3 August 2026 |
|---|---|
| Posts held | 20 (22 March 2026 – 3 August 2026) |
| Views total | 137 |
| Reactions total | 7 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 02:32 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Measured directly from 1 video 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.
142 reactions across 20 posts, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 142 | 100.0% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 20 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 142reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 22 March 2026 to 3 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
A Visual Guide to Quantization As their name suggests, Large Language Models (LLMs) are often too large to run on consumer hardware. These models may exceed billions of parameters and generally need GPUs with large amounts of VRAM to speed up inference. As such, more and more research has been focused on making these models smaller through improved training, adapters, etc. One major technique in this field is calle…
👍7
📊 Most cited sources in A/B Testing A hand-curated leaderboard from Ron Kohavi — the researcher behind much of the modern A/B testing literature (ex-Microsoft, Amazon, Airbnb) — ranking the most-cited work in A/B testing / Online Controlled Experiments by citations per year, with a strict cutoff of 10+ cites/yr. Scoped deliberately to controlled experiments, not causal inference in general. Each paper's citation cou…
👍2
Maths, CS & AI Compendium Most textbooks bury good ideas under dense notation, skip the intuition, assume you already know half the material, and quickly get outdated in fast-moving fields like AI. This is an open, unconventional textbook covering maths, computing, and artificial intelligence from the ground up. Written for curious practitioners looking to deeply understand the stuff, not just survive an exam/interv…
👍7
The Algorithms - GitHub's largest open-source algorithm library It's not a single repo, but an entire collection: algorithms and data structures implemented in virtually every language — Python, Java, C, C++, JavaScript, Rust, Go, Julia, Fortran, Zig, Nim, and even Mojo. Every repository is educational by design: sorting, searching, graphs, dynamic programming, mathematics, machine learning, physics. Clean, readabl…
👍9
CS336: Language Modeling from Scratch by Stanford Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of downstream tasks. As the field of artificial intelligence (AI), machine learning (ML), and NLP continues to grow, possessing a deep understanding of language models becomes essential …
👍6
LLM Engineering Essentials Gain the skills to build LLM-powered services that work. Master LLM APIs and self-hosted LLMs as you code, experiment, and create a platform for custom AI-powered NPCs. 1. Understand the fundamentals of LLM APIs and workflows to create a chatbot based on your favorite fantasy character 2. Learn to work with self-hosted LLMs, encoders, and vector stores, and build a RAG system 3. Explore m…
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Causal Inference: What If by Jamie Robins and Miguel Hernán This book will be helpful to anyone interested in causal inference, including epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in three parts of increasing difficulty: (1) causal inference without models (2) causal inference with models (3) causal inference from complex …
👍6
Recommenders Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems. Recommenders is a project under the Linux Foundation of AI and Data. This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples…
👍4
System Design Interview – Step By Step Guide Topics mentioned in the video: • Stages of a typical system design interview: functional requirements (API), non-functional requirements, high-level design, detailed design, bottlenecks and tradeoffs. • Why requirements clarification is so important. • What questions to ask the interviewer. • How to design API. • Non-functional requirements to consider: scalability, perfo…
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Statistical Rethinking by Richard McElreath The unfortunate truth about data is that nothing much can be done with it, until we say what caused it. This course teaches data analysis, but it focuses on scientific models: • Conceptual, causal models and precise questions about those models • Bayesian data analysis to connect scientific models to evidence • Powerful computational tools for coping with high-dimension, …
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ML Systems Design Interview Guide by Patrick Halina One of the trickiest interview rounds for ML practitioners is ML systems design. If you’re applying to be a Data Scientist, ML Engineer or ML Manager at a big tech company, you’ll probably face an ML Systems design question. Author recently tackled this question at a few big tech companies on his way to becoming a Staff ML Engineer at Pinterest. In this article he…
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Kaggle Tips for Feature Engineering and Selection Gilberto Titericz explores essential techniques for improving model accuracy, emphasizing exploratory data analysis and human intuition in feature engineering. Learn strategies for handling high-cardinality categorical features, missing values, and combining variables to maximize performance in competitions. Link: YouTube Navigational hashtags: #armknowledgesharin…
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Showing the 12 most recent of 20 posts we hold for @data_science_weekly. 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 23 August 2026 — this entry's latest reading, not the date you are reading this.
“Artem Ryblov’s Data Science Weekly” (@data_science_weekly), 679 subscribers as measured 23 August 2026. Telegram Register, tgregister.com/channel/data_science_weekly.
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.