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Channel

Анализ данных (Data analysis)

@data_analysis_ml

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Stars · Advertising · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Handles named that no longer answer · Telegram's recommendations · Cite this entry

51,923subscribers

+1,534 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001684146975
TypeChannel
Username@data_analysis_ml
DescriptionData science, наука о данных. @haarrp - админ РКН: clck.ru/3FmyAp
Created28 May 2022 — measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live10 November 2026
Measurements held63
Confirmed unchanged1 time, most recently 10 November 2026
On Telegramt.me/data_analysis_ml

Topic

Technology — a classification, not a measurement. 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 the other topics.

Growth

50,35751,93151,14451,9236 August 202610 November 2026
63 measurements spanning 96 days, net +1,534. 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 50,121–52,167 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 63
Measured (UTC)SubscribersChange
10 Nov 2026, 02:1951,923+16
8 Nov 2026, 13:3851,907-23
7 Nov 2026, 10:4151,930+1
6 Nov 2026, 07:5651,929+5
5 Nov 2026, 09:2851,924+8
4 Nov 2026, 09:1151,916-2
3 Nov 2026, 08:4651,918-12
2 Nov 2026, 07:5651,930+1
1 Nov 2026, 09:1851,929-2
31 Oct 2026, 09:0851,931+32
30 Oct 2026, 05:4051,899+18
29 Oct 2026, 04:5751,881+39
28 Oct 2026, 05:2651,842-3
27 Oct 2026, 06:0551,845+8
26 Oct 2026, 05:5751,837+51
25 Oct 2026, 09:3051,786+56
24 Oct 2026, 09:1851,730+2
23 Oct 2026, 10:5151,728+3
22 Oct 2026, 13:2051,725+160
21 Oct 2026, 04:4751,565first reading

Engagement

450 posts held, back to 31 July 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 207 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
9.46%
avg views ÷ 51,923 subscribers
Avg views / post
4,910
145 posts measured
Reaction rate
3.71%
reactions ÷ views · ER floor
Posts in window
145
of 450 held

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.

What these figures were computed from
WindowRolling 30 days · latest post in window 10 November 2026
Posts held450 (31 July 2026 – 10 November 2026)
Views total712,080
Reactions total26,423
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken10 Nov 2026, 22:44 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.

What this channel posts

Photos
≈3,810
Videos
≈471
Links
≈3,090

Lifetime counters from Telegram’s own channel header, read 10 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
58m 23s
Average length
1m 05s

Measured directly from 54 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.

Reaction mix

40,235 reactions across 448 posts, in 30 distinct kinds. The most used accounts for 17.5% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤7,04317.5%
👍6,62416.5%
🔥6,06115.1%
😁4,73811.8%
🤔3,4668.61%
💯3,4258.51%
👏3,3958.44%
🎉3,2968.19%
🤣1,2793.18%
🥴1550.385%
🥰1450.36%
🥱980.244%
❤‍🔥840.209%
😱730.181%
😢570.142%
🤨450.112%
🙏310.077%
😐300.075%
🤯280.07%
⚡250.062%
10 further kinds1370.34%

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 450 of the 450 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 40,344 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 450 most recent posts we hold, published 31 July 2026 to 10 November 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.

Telegram Stars

Stars received
19
across the posts below
Posts paid on
9
of 450 we hold a reading for · 2%
Most on one post
3
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @data_analysis_ml. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 450 most recent posts we hold for this entry, published 31 July 2026 to 10 November 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Advertising

Ad load
2.89%
13 of 450 posts carry an ad marker
Regulatory tokens
13
posts carrying an erid · 13 distinct tokens
Median views · ads
3,580
over 13 measured posts
Median views · rest
4,020
over 437 measured posts

An ad marker, not a judgement about a post. A post is counted here because it carries one of two explicit markings: an erid token, which Russian law has required on paid placements since 2022 and which is issued against a specific advertising contract, or a #реклама / #ad hashtag in the body, which is the channel declaring it itself. The first is documentary; the second is a self-declaration and is weaker. No classifier reads the text and decides — nothing on this site guesses that a post is an advertisement.

This is a floor, and it can only ever be a floor. A channel that runs paid placements without marking them produces no marker for us to count, and an unmarked ad is indistinguishable from an ordinary post on the public surface. The ad load above therefore means “the share of posts that declared themselves”, never “the share of posts that were paid for”. A low figure is not evidence of a channel that runs few ads.

Both figures are medians, and no ratio between them is published. Each is a view reading that actually occurred on a post, picked by percentile_disc rather than averaged, so one viral post cannot move it and no interpolated value is invented between two readings. They are printed side by side with the count behind each rather than as a ratio: an ad and an ordinary post are not otherwise matched — for topic, for length, for hour of day — so the gap between them is a description of two groups and not the effect of one being an ad.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2RanykMbxnf12 September 20262 September 2026
2RanynYbiiP113 October 202613 October 2026
2SDnjcHVw6K112 August 202612 August 2026
2VSb5y8u96r124 September 202624 September 2026
2VtzquuCwpo123 August 202623 August 2026
2Vtzqwno39C17 August 20267 August 2026
2VtzqxB8tRr130 September 202630 September 2026
2VtzqxDzTPz125 September 202625 September 2026
2W5zFG1fVN1110 August 202610 August 2026
2W5zFGPszeD118 September 202618 September 2026
2W5zFGhxJrD123 September 202623 September 2026
2W5zFJhqp8q118 October 202618 October 2026

A token repeated across several posts is one advertising contract placed more than once, which is what the identifier is for. The strings are reproduced exactly as they appeared in the post or in its click-through URL and are not validated against any registry — we record the marker a channel published, and whether it resolves to a real contract is a question for the register that issued it. 1 further token is held for this entry and not listed here.

Measured over the 450 most recent posts we hold, published 31 July 2026 to 10 November 2026. Views are the latest single reading held for each post, and any reading at or above 1,000 is rounded by Telegram to three significant figures.

Recent posts

Telegram’s own ‘edited’ marker: 21 of 145 posts held from the last 30 days (14.5%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

10 Nov 2026, 13:30 UTC≈2,480 views19 reactionsread 10 November 2026

⚡️️️️️️️ Google снизила ошибки квантовых вычислений в 40 раз Google Quantum AI впервые продемонстрировала технологию Magic State Cultivation на реальном квантовом процессоре Willow. Magic States - специальные квантовые состояния, необходимые для выполнения универсальных квантовых вычислений. Их создание может занимать до 95% ресурсов будущего квантового компьютера. Новый метод работает как фильтр: система многокра…

❤9👍5🔥3🤣1😐1

10 Nov 2026, 09:59 UTC≈3,240 views53 reactionsread 10 November 2026
Video

Ram Altman

😁34🔥8❤5🤨2🥱2👍1😐1

10 Nov 2026, 06:36 UTC≈3,430 views18 reactionsread 10 November 2026

Meituan выпустила веса LongCat-2.5: 1,6 трлн параметров и полностью китайские чипы 🔥 Meituan открыла веса новой модели LongCat-2.5. Основные характеристики: - 1,6 трлн параметров всего. - 48 млрд активных параметров (A48B). - Обучение полностью проведено на китайских отечественных чипах. - MIT License - открытая лицензия. Веса уже доступны на Hugging Face: https://huggingface.co/meituan-longcat

🔥12❤4🥰2

9 Nov 2026, 21:25 UTC≈4,360 views32 reactionsread 10 November 2026

Anthropic объяснила, как правильно промптить Claude Fable 5.2. Старые инструкции могут мешать новой модели работать эффективно. Anthropic опубликовала техническое руководство для разработчиков ИИ-агентов. В нём собраны особенности новой модели и рекомендации по настройке системных промптов. Самое интересное: 1. Правильно выбирайте уровень reasoning - low - быстрые задачи и простые агенты. - medium - повседневная р…

👍19❤7🔥4💔2

9 Nov 2026, 19:05 UTC≈2,960 views27 reactionsread 10 November 2026
Photo

Anthropic представила Claude Fable 5.2 и Mythos 5.2: 1000 ИИ-агентов формализовали доказательство теоремы Ферма за 30 часов Новые модели получили серьёзные улучшения в программировании, математике, научных исследованиях и кибербезопасности. Что удалось сделать: - Математика: 1000 агентов формализовали доказательство Великой теоремы Ферма на Lean за 30 часов. Предыдущей исследовательской версии Claude потребовалось…

❤15👍7🔥5

9 Nov 2026, 18:30 UTC≈3,490 views28 reactionsread 10 November 2026
Photo

Anthropic готовит политическую кампанию на $30 млн, чтобы сделать ИИ одной из главных тем выборов в США в 2028 году. Связанная с Anthropic организация Public First Action планирует влиять на президентскую гонку, выборы в Конгресс, губернаторские кампании и выборы генеральных прокуроров и законодателей штатов. В распоряжении организации и связанных с ней структур сейчас около $30 млн. Цели на 2027 год: - Добиться п…

😐10👍6🔥5❤3🌭3💔1

9 Nov 2026, 14:33 UTC≈22,900 views33 reactionsread 10 November 2026
Video

Модель на 800 млн параметров обошла Qwen 9B и Gemma 4 в генерации Linux-команд На Hugging Face появился dale-0.8b-GGUF - специализированная модель, которая превращает обычные текстовые запросы в команды Bash для Linux. Что умеет: Пишешь обычным языком: «Найди все большие файлы в текущей директории». Модель возвращает одну Bash-команду в структурированном JSON с кратким объяснением. Она также умеет отказываться о…

👍17🔥8❤7😁1

9 Nov 2026, 12:38 UTC≈3,580 views10 reactionsread 10 November 2026
Photo

Иногда очистка данных отнимает больше времени, чем сам анализ. Часть этой работы уже можно доверить LLM, но только если понимать, как всё настроить и проверить результат. Все подходы покажут на AI Analytics Week — бесплатном онлайн-интенсиве Школы анализа данных Яндекса. С 23 по 27 ноября на реальных кейсах компании разберут, где ИИ-инструменты помогают в повседневной аналитике: 1. Автоматическая очистка данных с L…

👍5❤3🔥2

9 Nov 2026, 11:58 UTC≈6,380 views93 reactionsread 10 November 2026
Photo

Больше мемов

😁80❤10🔥2🤣1

9 Nov 2026, 10:12 UTC≈4,050 views27 reactionsread 10 November 2026
Photo

✔️ На OpenRouter появилась анонимная мультимодальная модель Crouton Alpha Агрегатор моделей открыл доступ для бесплатного тестирования стелс-модели под названием Crouton Alpha. Она предлагает контекстное окно на 1 млн токенов, мультимодальность с поддержкой текста, изображений, аудио и видео, поддерживает сложные рассуждения и вызов внешних инструментов. В Х сообщество предполагает, что это неанонсированная модель…

❤17👍5🔥5

9 Nov 2026, 08:43 UTC≈4,190 views36 reactionsread 10 November 2026
Photo

Китайская разведка могла использовать близкую знакомую главы NVIDIA, чтобы шпионить за ним. Bloomberg и NYT раскрыли детали По данным The New York Times, американская разведка считает, что китайская исполнительница Alice Huang (Huang Le), руководитель компании Megaspeed, была завербована Министерством государственной безопасности Китая. Её задачей якобы было собирать информацию о Дженсене Хуанге, главе NVIDIA, и по…

🤣17❤13👍3🔥2👌1

8 Nov 2026, 18:13 UTC≈4,380 views39 reactionsread 10 November 2026
Photo

💀️️️️️️ Tencent не обещает открыть исходный код своей лучшей ИИ-модели На Future Science Prize 2026 главного научного сотрудника Tencent по ИИ спросили: если Hunyuan станет лучшей ИИ-моделью в мире, продолжит ли компания публиковать её в открытом доступе? Прямого «да» не последовало. Вместо этого он объяснил, почему лаборатории вообще открывают свои модели. Пока модель не занимает лидирующих позиций, открытый дост…

🤣18🔥9❤5🤨3😢2🍌1👍1

Showing the 12 most recent of 450 posts we hold for @data_analysis_ml. 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.

Posts edited after publishing

@data_analysis_ml edited 4 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
14 August 2026
Most recent edit
29 October 2026

Posts recorded as deleted after publishing

We recorded 14 posts on @data_analysis_ml as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
12 August 2026
Most recent seen missing
9 November 2026
Median time before seen missing
45 hours
Seen missing in the last 30 days
3

Forward network

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

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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

Named by 67 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.

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.

Channels Telegram recommends alongside this one

Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.

Machinelearning
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Machine learning Interview
@machinelearning_interview · 30,560
#3
Data Science. SQL hub
@sqlhub · 36,025
#4
Математика Дата саентиста
@data_math · 14,419
#5
Машинное обучение RU
@machinelearning_ru · 18,339
#6
Python/ django
@pythonl · 58,287
#7
Big Data AI
@bigdatai · 18,329
#8
Python вопросы с собеседований
@python_job_interview · 24,906
#9
Data Science
@datascienceiot · 42,496
#10
Data Science Jobs
@datascienceml_jobs · 21,642
#11
DeepSchool
@deep_school · 10,723
#12
Data Science | Machinelearning [ru]
@devsp · 19,659
#13
Нескучный Data Science
@not_boring_ds · 12,081
#14
Библиотека дата-сайентиста | Data Science, Machine learning, анализ данных, машинное обучение
@dsproglib · 18,290
#15
Аналитика данных / Data Study
@data_study · 9,414
#16
Datalytics
@datalytx · 8,783
#17
Аналитик данных
@dataanlitics · 6,386
#18
🏆 AIeron | Data Feeling
@datafeeling · 14,208
#19
Start Career in DS
@start_ds · 11,463
#20
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
#21
что-то на DL-ском
@nadlskom · 5,152
#22
Python RU
@pro_python_code · 12,302
#23
BOGDANISSSIMO
@bogdanisssimo · 7,019
#24
XOR
@xor_journal · 195,549
#25
Библиотека собеса по Data Science | вопросы с собеседований
@ds_interview_lib · 4,459
#26
Neural Networks | Нейронные сети
@neural · 11,724
#27
Mashkka про Data Science
@mashkka_ds · 5,305
#28
Artificial Intelligence
@ArtificialIntelligencedl · 16,815
#29
Библиотека задач по Data Science | тесты, код, задания
@ds_problems_lib · 3,966
#30
Пристанище Дата Сайентиста
@renat_alimbekov · 6,287
#31
Complete AI
@complete_ai · 7,887
#32
Machine Learning | Нейронные сети, ИИ, Big Data
@ML_secrets · 7,365
#33
Это разве аналитика?
@eto_analytica · 4,615
#34
Derp Learning
@derplearning · 13,992
#35
Библиотека питониста | Python, Django, Flask
@pyproglib · 36,972
#36
эйай ньюз
@ai_newz · 98,262
#37
Машиннное обучение | Наука о данных Библиотека
@machinelearning_books · 16,955
#38
Сиолошная
@seeallochnaya · 81,254
#39
Golang
@Golang_google · 40,453
#40
Время Валеры
@cryptovalerii · 32,082
#41
DevOps
@DevOPSitsec · 23,892
#42
Инжиниринг Данных
@rockyourdata · 23,685
#43
Dealer.AI
@dealerAI · 17,113
#44
LEFT JOIN
@leftjoin · 41,485
#45
Kali Linux
@linuxkalii · 55,842
#46
Data jobs — вакансии по data science, анализу данных, аналитике, искусственному интеллекту
@datajob · 15,139
#47
LLM под капотом
@llm_under_hood · 30,219
#48
Linux Academy
@linuxacademiya · 28,647
#49
Техножрица 👩‍💻👩‍🏫👩‍🔧
@tech_priestess · 15,110
#50
No Data No Growth | Pavel Bukhtik
@nodatanogrowth · 12,776
#51
Yandex for ML
@yandexforml · 19,136
#52
ODS #jobs
@odsjobs · 14,582
#53
настенька и графики
@nastengraph · 28,082
#54
Denis Sexy IT 🤖
@denissexy · 140,753
#55
Борис опять
@boris_again · 17,963
#56
Reveal the Data
@revealthedata · 27,678
#57
Старший Авгур
@senior_augur · 8,372
#58
Градиент обреченный
@doomgrad · 8,840
#59
Job for Analysts & Data Scientists
@foranalysts · 37,857
#60
Зарплатник Аналитика
@zarplatnik_analytics · 11,694
#61
data.csv
@data_csv · 13,773
#62
Data Science Jobs
@datasciencejobs · 22,587
#63
Golang Books
@golang_books · 16,863
#64
Библиотека С# С++
@cpluscsharp · 10,015
#65
Data Secrets | Карьера
@data_secrets_career · 7,395
#66
ML for Value / Ваня Максимов
@ml4value · 5,676
#67
Тагир Анализирует
@tagir_analyzes · 11,650
#68
karpov.courses
@KarpovCourses · 27,313
#69
Дата-сторителлинг
@data_publication · 11,916
#70
Институт AIRI
@airi_research_institute · 13,977
#71
novichkov.net
@novichkovnet · 3,343
#72
Аналитика и growth mind-set
@analyticsgrowthmindset · 17,391
#73
Reliable ML
@reliable_ml · 6,606
#74
🏄 Соревновательный Data Science | Kaggle | Чемпионаты
@data_science_winners · 3,366
#75
Борзило
@y_borzilo · 5,527
#76
РИСЕРЧОШНАЯ
@researchoshnaya · 2,464
#77
C++ Academy
@cpluspluc · 15,457
#78
Dev & ML Connectable Jobs
@dev_connectablejobs · 29,084
#79
Жёлтый AI
@zheltyi_ai · 9,371
#80
Python tests
@python_testit · 6,872
#81
New Yorko Times
@new_yorko_times · 10,720
#82
addmeto
@addmeto · 72,001
#83
Python Jobs
@python_djangojobs · 15,125
#84
Хабр
@habr_com · 134,497
#85
Метаверсище и ИИще
@cgevent · 53,591
#86
Нескучный Data Science Jobs
@not_boring_ds_jobs · 9,430
#87
[PYTHON:TODAY]
@python2day · 63,348
#88
Kantor.AI
@kantor_ai · 12,215
#89
Data jobs feed
@datajobschannel · 12,625
#90
Physics.Math.Code
@physics_lib · 146,676
#91
Поступашки - ШАД, Стажировки и Магистратура
@postypashki_old · 47,200
#92
Kali Novskaya
@rybolos_channel · 19,136
#93

Read from Telegram’s recommendation API, most recently 25 August 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.

Appears in Telegram’s recommendations for other channels

The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.

Data Science. SQL hub
@sqlhub · 36,025
Telegram ranks this channel #1 of 87 here — alongside 86 others — read 2 September 2026
Python/ django
@pythonl · 58,287
Telegram ranks this channel #2 of 84 here — alongside 83 others — read 22 August 2026
Искусственный интеллект. Высокие технологии
@vistehno · 70,139
Telegram ranks this channel #2 of 94 here — alongside 93 others — read 20 August 2026
Data Science | Machinelearning [ru]
@devsp · 19,659
Telegram ranks this channel #3 of 91 here — alongside 90 others — read 30 September 2026
Python вопросы с собеседований
@python_job_interview · 24,906
Telegram ranks this channel #3 of 90 here — alongside 89 others — read 15 September 2026
Machine learning Interview
@machinelearning_interview · 30,560
Telegram ranks this channel #3 of 98 here — alongside 97 others — read 7 September 2026
Data Science
@datascienceiot · 42,496
Telegram ranks this channel #3 of 76 here — alongside 75 others — read 29 August 2026
Machinelearning
@ai_machinelearning_big_data · 275,659
Telegram ranks this channel #4 of 95 here — alongside 94 others — read 10 August 2026
Data Science Jobs
@datascienceml_jobs · 21,642
Telegram ranks this channel #8 of 97 here — alongside 96 others — read 24 September 2026
Базы данных | Access, SQL, Big Data
@databases_secrets · 30,012
Telegram ranks this channel #15 of 82 here — alongside 81 others — read 7 September 2026
Sber AI
@SberAIScience · 26,512
Telegram ranks this channel #19 of 95 here — alongside 94 others — read 10 September 2026
XOR
@xor_journal · 195,549
Telegram ranks this channel #19 of 93 here — alongside 92 others — read 12 August 2026
Data Secrets
@data_secrets · 96,035
Telegram ranks this channel #23 of 98 here — alongside 97 others — read 17 August 2026
Библиотека питониста | Python, Django, Flask
@pyproglib · 36,972
Telegram ranks this channel #25 of 84 here — alongside 83 others — read 1 September 2026
Python Hacks
@python_secrets · 40,332
Telegram ranks this channel #25 of 82 here — alongside 81 others — read 30 August 2026
Senior Python Developer
@seniorpy · 39,134
Telegram ranks this channel #29 of 92 here — alongside 91 others — read 30 August 2026
Zen of Python
@zen_of_python · 18,645
Telegram ranks this channel #31 of 91 here — alongside 90 others — read 5 October 2026
Python Academy
@python_academy · 43,823
Telegram ranks this channel #34 of 87 here — alongside 86 others — read 28 August 2026
Python и 1000 уязвимостей
@osint_pythons · 36,463
Telegram ranks this channel #38 of 84 here — alongside 83 others — read 1 September 2026
Job for Analysts & Data Scientists
@foranalysts · 37,857
Telegram ranks this channel #41 of 96 here — alongside 95 others — read 1 September 2026
Учебные фильмы 🎞
@maths_lib · 25,588
Telegram ranks this channel #42 of 67 here — alongside 66 others — read 14 September 2026
Заметки Бэкендера
@backend_it · 18,688
Telegram ranks this channel #43 of 73 here — alongside 72 others — read 4 October 2026
Python обучающий
@pythonist24 · 55,221
Telegram ranks this channel #43 of 88 here — alongside 87 others — read 23 August 2026
Простой Python | Программирование
@python_piton_javascript · 123,691
Telegram ranks this channel #50 of 84 here — alongside 83 others — read 13 August 2026

This channel appears in 63 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

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 10 November 2026 — this entry's latest reading, not the date you are reading this.

“Анализ данных (Data analysis)” (@data_analysis_ml), 51,923 subscribers as measured 10 November 2026. Telegram Register, tgregister.com/channel/data_analysis_ml.

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.