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Data Science: Алгоритмы и Структуры данных

@structuredata

On this record: Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Citations · Telegram's recommendations · Cite this entry

7,737subscribers

-11 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001674242589
TypeChannel
Username@structuredata
DescriptionМы не претендуем на оригинальность контента, мы лишь собираем материал из открытых источников. Ссылка: @Portal_v_IT Сотрудничество, авторские права: @oleginc, @tatiana_inc Канал на бирже: https://telega.in/c/structuredata
CreatedBetween 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live20 August 2026
Measurements held6
Confirmed unchanged1 time, most recently 20 August 2026
On Telegramt.me/structuredata

Growth

7,7377,7547,745.57 August 2026 — 7,748 subscribers7 August 2026 — 7,748 subscribers10 August 2026 — 7,750 subscribers13 August 2026 — 7,753 subscribers17 August 2026 — 7,754 subscribers20 August 2026 — 7,737 subscribers7 August 202620 August 2026
6 measurements spanning 14 days, net -11. 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 7,734–7,757 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
20 Aug 2026, 18:447,737-17
17 Aug 2026, 11:067,754+1
13 Aug 2026, 17:467,753+3
10 Aug 2026, 12:377,750+2
7 Aug 2026, 05:037,748no change
7 Aug 2026, 03:177,748first reading

Engagement

59 posts held, back to 28 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 16 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
5.48%
avg views ÷ 7,737 subscribers
Avg views / post
424
59 posts measured
Reaction rate
0.315%
reactions ÷ views · ER floor
Posts in window
59
of 59 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. It is computed over the 17 of 59 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 22 August 2026
Posts held59 (28 July 202622 August 2026)
Views total25,032
Reactions total24
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken22 Aug 2026, 20:46 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
361
Videos
42
Links
3,290

Lifetime counters from Telegram’s own channel header, read 22 August 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
8m 38s
Average length
2m 53s

Measured directly from 3 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

24 reactions across 12 posts, in 3 distinct kinds. The most used accounts for 33.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
833.3%
👍833.3%
🔥833.3%

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 17 of the 59 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 24reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 59 most recent posts we hold, published 28 July 2026 to 22 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.

Advertising

Ad load
1.69%
1 of 59 posts carry an ad marker
Regulatory tokens
1
posts carrying an erid · 1 distinct token
Median views · ads
447
over 1 measured post
Median views · rest
441
over 58 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. The sample on one side is under five posts, which is too thin to compare. The two figures are shown side by side with the count behind each, and deliberately not divided into a headline like “ads get x% fewer views” — an arithmetic that is easy to print and, at this sample size, means nothing.

Advertising tokens recorded on this entry
eridPostsFirst seenLast seen
2VSb5wq2yhk131 July 202631 July 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.

Measured over the 59 most recent posts we hold, published 28 July 2026 to 22 August 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

22 Aug 2026, 16:11 UTC160 viewsread 22 August 2026

Анализ данных на R в примерах и задачах Видеокурс из двух частей от Computer Science Center ➡️Первая часть ➡️Вторая часть Data Science: Алгоритмы и Структуры данных

22 Aug 2026, 11:12 UTC256 viewsread 22 August 2026
Photo

Двоичное дерево поиска Двоичное дерево поиска(BST) - дерево, в котором все узлы следуют свойствам: 1. Левое поддерево узла имеет ключ, меньше или равный ключу его родительского узла 2. Правое поддерево узла имеет ключ больше, чем ключ родительского узла. Таким образом дерево делит все свои поддеревью на два сегмента: левое и правое поддеревья. Есть еще одно определение данного дерева: leftsubtree(keys) =< node(k

22 Aug 2026, 08:17 UTC279 viewsread 22 August 2026
Photo

Решение задачи через один только проход Да, эту задачу можно решить также и тупо одним проходом, нам снова понадобиться count(счетчик). Только теперь, кроме него нам нужна дополнительная функция swap(которая меняет местами элементы) Итого у нас будет изначально count = 0 и цикл от начала до конца нашего массива. Мы будем проверять, если наш текущий элемент не равен 0, мы будем менять местами count элемент и элемент

21 Aug 2026, 16:12 UTC335 viewsread 22 August 2026
Photo

Настраиваем Python для машинного обучения на Windows В этой статье рассказываем о возможностях Python для машинного обучения на Windows, описание дистрибутива Anaconda, процесс его установки и создание нейронной сети. ➡️Читать статью Data Science: Алгоритмы и Структуры данных

21 Aug 2026, 11:12 UTC397 views0 reactionsread 22 August 2026
Photo

Алгоритм решения задачи Есть куча способов решить данную задачу. Ниже я приведу простой и интересный способ решить ее. Пройдитесь по заданному массиву слева направо. Во время обхода поддерживайте количество ненулевых элементов в массиве (к примеру в счетчике count). Для каждого ненулевого элемента gjvtcnbnt элемент в arrcount и увеличьте сам count. После полного обхода все ненулевые элементы уже будут перемещены в

21 Aug 2026, 08:17 UTC411 views3 reactionsread 22 August 2026
Photo

Задача: переместить нули в конец Дан массив случайных чисел, необходимо переместить все нули данного массива в конец. Например, если задан массив 1, 9, 8, 4, 0, 0, 2, 7, 0, 6, 0, необходимо его изменить к виду: 1, 9, 8, 4, 2, 7, 6, 0, 0, 0, 0. Порядок остальных элементов должен остаться прежним. Сложность O(n), а пространство О(1) Data Science: Алгоритмы и Структуры данных

🔥3

20 Aug 2026, 16:10 UTC460 views0 reactionsread 22 August 2026
Photo

Топ-10 алгоритмов машинного обучения В машинном обучении есть нечто, называемое теоремой «No Free Lunch». Вкратце, в ней говорится, что ни один алгоритм не работает лучше всего для каждой проблемы, и это особенно важно для контролируемого обучения (т.е. predictive modeling). ➡️Читать статью Data Science: Алгоритмы и Структуры данных

20 Aug 2026, 13:13 UTC463 viewsread 22 August 2026
Photo

Алгоритмы машинного обучения Данное видео поможет вам разобраться, какие проблемы есть в Machine Learning, и познакомит с различными алгоритмами. Ключевые алгоритмы машинного обучения - это линейная регрессия, логистическая регрессия, дерево решений, случайный лес и алгоритм KNN. Все алгоритмы представлены с простыми примерами и реализованы на языке Python. ➡️Смотреть видео ➡️Скачать видео Data Science: Алгоритмы

20 Aug 2026, 11:35 UTC461 views6 reactionsread 22 August 2026

Возвращаем прямой формат контента по тематике канала. Полностью пересмотрели подход к постам. Надеемся, что вам будет полезно.

👍3🔥3

20 Aug 2026, 11:12 UTC466 views4 reactionsread 22 August 2026
Photo

Вращение в AVL-деревьях Двойные повороты - достаточно сложная тема, но я нашел достаточно хорошее объяснение этому. Обратите ваше внимание на картинку. В данном случае представлено вращение влево-вправо. Поворот влево-вправо - это комбинация вращений влево, за которым следует вращение вправо. Есть аналогичное вращение вправо-влево, только оно с точностью наоборот. Сначала вращение вправо, а после уже влево! Data

3👍1

20 Aug 2026, 10:36 UTC430 views2 reactionsread 22 August 2026
Video

Как работает сортировка вставками и чем она отличается от сортировки выбором В видео разберём принцип работы алгоритма сортировки вставками и его ключевое отличие от сортировки выбором. Также покажем реализацию алгоритма на Python и наглядно разберём, как он сортирует элементы. ➡️Смотреть видео ➡️Скачать видео Data Science: Алгоритмы и Структуры данных

👍2

19 Aug 2026, 16:07 UTC441 views0 reactionsread 22 August 2026

Iron Core. Часть 3: Бессмертная командная строка Публикуем перевод третьей статьи из серии (первая часть, вторая), посвящённой информационным технологиям в авиаперевозках. Сегодня поговорим о режиме командной строки системы Amadeus, работа в которой опирается на язык, созданный для телетайпов. Этот язык до сих пор обеспечивает огромный процент бронирований билетов во всём мире — как тех, что выполняются различными а

Showing the 12 most recent of 59 posts we hold for @structuredata. 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.

Citation-graph rank

Citation-graph rank — 1,490,600 of 1,583,249entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

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

Named by 2 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.

Named by

Channels on the register whose posts name this channel's handle.

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.

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.

Однажды в трендах
@trendo · 68,975
Telegram ranks this channel #74 of 90 here — alongside 89 others — read 20 August 2026

This channel appears in 1 seed channel's Telegram-generated recommendation list in total. 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 20 August 2026 — this entry's latest reading, not the date you are reading this.

“Data Science: Алгоритмы и Структуры данных” (@structuredata), 7,737 subscribers as measured 20 August 2026. Telegram Register, tgregister.com/channel/structuredata.

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.