Telegram RegisterThe public register of Telegram

Channel

Just Yet Another Channel

@just_mind_data

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Polls · Citations · Cite this entry

437subscribers

+0 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001349996661
TypeChannel
Username@just_mind_data
DescriptionScience, Tech and 42 News, Questions and Answers Habr: https://habr.com/ru/users/Dilemma/publications/articles/ GitHub: https://github.com/dilemmalab Logo References: thanks to https://ml.berkeley.edu!
CreatedBetween 1 March 2018 and 13 May 2019— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live8 August 2026
Measurements held2
On Telegramt.me/just_mind_data

Growth

4377 Aug 2026, 13:02 — 437 subscribers8 Aug 2026, 05:16 — 437 subscribers7 Aug 2026, 13:028 Aug 2026, 05:16
2 measurements taken within a single day. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 436–438 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
8 Aug 2026, 05:16437no change
7 Aug 2026, 13:02437first reading

Engagement

20 posts held, back to 13 May 2019the 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.

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 4 July 2024. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Photos
6
Links
40

Lifetime counters from Telegram’s own channel header, read 8 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Reaction mix

107 reactions across 15 posts, in 3 distinct kinds. The most used accounts for 82.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍8882.2%
1413.1%
❤‍🔥54.67%

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 15 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 107reactions 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 13 May 2019 to 4 July 2024, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

4 Jul 2024, 07:25 UTC≈3,480 views32 reactionsread 8 August 2026

Всем привет, возможно Вам известно, что я немного преподаю и преподавала ранее в разных образовательных организациях и программах - во ВШЭ ФКН с коллегами-бывшими яндексоидами, в МГТУ им Н.Э.Баумана с товарищами, в Технопарке@Mail.Ru и ВК Образовании Залила на Github курсы от 2020 и 2021 годов по практической и продуктовой аналитике : https://github.com/DilemmaLab/educational_courses/tree/main Курсы 2020-2021 и 2021

👍2210

23 Apr 2024, 18:33 UTC≈1,260 views14 reactionsread 8 August 2026

Друзья, приветствую Вас :) Провели вебинар по математической статистике для новичков с замечательными Karpov.Courses: https://www.youtube.com/live/ODPPH-hzrGo Если Вам интересно, залетайте в канал https://t.me/KarpovCourses и на курс Start DA здесь или здесь по ссылочке

👍104

21 Mar 2024, 05:40 UTC≈1,410 views3 reactionsread 8 August 2026

Прекрасный Паша Бухтик опубликовал наш разбор по простой и интересной математической задаче: https://t.me/nodatanogrowth/364

👍3

20 Mar 2024, 08:35 UTC≈1,620 views21 reactionsread 8 August 2026

Друзья, всем привет :) Предлагаю Вашему вниманию 2 статьи по анализу данных: 1) Framework по запуску A/B-теста : https://dilemmalab.medium.com/фреймворк-для-дизайна-a-b-теста-4f7a4c822889 Упор в статье идет на алгоритм запуска, математический аппарат A/B-тестирования и методом bootstrap (он же Монте-Карло) Статья продублирована на Habr: https://habr.com/ru/articles/780932/ 2) Простой метод сегментации пользователей

👍16❤‍🔥5

27 May 2023, 12:48 UTC≈1,210 views4 reactionsread 8 August 2026

Добрый день :) Вчера провели небольшой workshop по bootstrap и линеаризации, материалы можно будет найти во вторник на замечательном сайте конференции Matemarketing Aha! 2023 https://matemarketing.ru/ Заодно оставлю здесь ссылки на наши прошедшие семинары по RFM-анализу, возможно, они будут Вам интересны: Семинар от EpicGrowth https://epicgrowth.ru/chirkina Семинар от Нетология: https://www.youtube.com/watch?v=Reh9X

👍4

27 Nov 2019, 07:46 UTC≈3,590 views4 reactionsread 8 August 2026

Друзья, мы в Яндексе выпустили расширенную версию статьи про множественные эксперименты :) В статье подробно объяснены математические основы применяемых методов и приведены примеры их программной реализации. Читайте в нашем Корпоративном блоге! 🙂 https://habr.com/ru/company/yandex/blog/476826/

👍4

4 Sept 2019, 23:09 UTC≈2,910 views4 reactionsread 8 August 2026

Друзья, 🙂 пока погода радует нас теплом и солнечнышком, MateMarketing, ведущая конференция по маркетинговой аналитике и не только, опубликовала свою интересную и насыщенную программу: https://matemarketing.ru/ С докладами выступят спикеры из ведущих компаний - Яндекс, Avito, Ebay, Альфа-Капитал и многие другие. Так же в программе ожидаются Tinder и даже PornHub 🙈 Не упустите уникальную возможность узнать про аналит

👍4

3 Aug 2019, 19:34 UTC≈2,220 viewsread 8 August 2026
Poll

Друзья, о чём Вам было бы интересно узнать из следующей статьи?

  1. Ratio-метрики52%
  2. Биномиальное распределение42%
  3. Свой вариант (можете написать в ЛС)6%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

3 Aug 2019, 18:27 UTC≈2,370 views3 reactionsread 8 August 2026

Доброго времени суток, дорогие и уважаемые Коллеги! :) Сегодня Вашему вниманию предлагается статья про оценку множественных статистических тестов. Мы вкратце коснёмся вопросов, в каких случаях множественные тесты могут быть полезны бизнесу, рассмотрим основные методики оценки результатов - оценки FWER (пока не рассматривая FDR), визуализируем результаты при помощи plotly. Буду благодарна за Ваши оценки и критические

👍3

22 Jul 2019, 00:58 UTC≈1,780 viewsread 8 August 2026

Друзья, спасибо за критический feedback! По совокупности отзывов статья взята на доработку :) https://habr.com/ru/post/460733/

21 Jul 2019, 21:52 UTC≈1,470 viewsread 8 August 2026

Перечень статистических тестов: http://www.machinelearning.ru/wiki/index.php?title=%D0%9A%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D1%8F:%D0%A1%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B5_%D1%82%D0%B5%D1%81%D1%82%D1%8B

25 Jun 2019, 07:29 UTC785 viewsread 8 August 2026
Forwarded from @BigQuery

​​Для упрощения работы с BigQuery ML, Google открыл репозиторий шаблонов SQL на данных стандартного экспорта Google Analytics 360 для решения таких маркетинговых задач как Customer segmentation для персонализации контента и Conversion/LTV prediction для оптимизации покупок. Еще один подробный пример прогнозирования покупок посетителей Интернет-магазина при помощи BigQuery ML с описанием. via @BigQuery

Showing the 12 most recent of 20 posts we hold for @just_mind_data. 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.

Polls

The 2 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.

3 Aug 2019, 19:34 UTCAnonymous Poll65 voters

Друзья, о чём Вам было бы интересно узнать из следующей статьи?

  1. Ratio-метрики52%
  2. Биномиальное распределение42%
  3. Свой вариант (можете написать в ЛС)6%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

13 May 2019, 09:59 UTCAnonymous Poll12 voters

Понравилась ли Вам статья?

  1. 👍92%
  2. 👎8%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Percentages only — there are no per-option vote counts here, because Telegram publishes none.The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.

The shares need not add up to 100.Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.

Read from the 20 most recent posts we hold, published 13 May 2019 to 4 July 2024. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.

Forward network

Republishes

Channels on the register whose posts this channel has forwarded.

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Cite this entry

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 8 August 2026 — this entry's latest reading, not the date you are reading this.

“Just Yet Another Channel” (@just_mind_data), 437 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/just_mind_data.

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.