Анализ данных и Deep Learning 1. Примеры применения анализа данных, стандартные задачи и методы 2. Методы решения задачи классификации и регрессии 3. Кластеризация 4. Преобразование признаков 5. Введение в Text Mining 6. Введение в Deep Learning 7. Deep Learning for Data with Sequence Structure 8. Рекомендательные системы 9. Прогнозирование временных рядов ➡️Смотреть видео ⬇️ Скачать видео Data Science: Алгоритмы…

Channel
Data Science: Алгоритмы и Структуры данных
@structuredata
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Advertising · Posts · Citations · Telegram's recommendations · Cite this entry
7,691subscribers
-57 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 |
|---|---|
| Type | Channel |
| Username | @structuredata |
| Created | Between 1 December 2021 and 30 April 2023 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 16 September 2026 |
| Measurements held | 13 |
| Confirmed unchanged | 1 time, most recently 16 September 2026 |
| On Telegram | t.me/structuredata |
Topic
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 12 September 2026 and assigned it the closest of 31 fixed categories, at 100% 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.
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Sept 2026, 15:56 | 7,691 | -10 |
| 12 Sept 2026, 23:41 | 7,701 | -7 |
| 8 Sept 2026, 14:41 | 7,708 | -6 |
| 3 Sept 2026, 01:45 | 7,714 | -3 |
| 30 Aug 2026, 18:17 | 7,717 | -9 |
| 27 Aug 2026, 17:37 | 7,726 | -10 |
| 24 Aug 2026, 11:28 | 7,736 | -1 |
| 20 Aug 2026, 18:44 | 7,737 | -17 |
| 17 Aug 2026, 11:06 | 7,754 | +1 |
| 13 Aug 2026, 17:46 | 7,753 | +3 |
| 10 Aug 2026, 12:37 | 7,750 | +2 |
| 7 Aug 2026, 05:03 | 7,748 | no change |
| 7 Aug 2026, 03:17 | 7,748 | first reading |
Engagement
71 posts held, back to 28 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 19 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.93%
- avg views ÷ 7,691 subscribers
- Avg views / post
- 225
- 3 posts measured
- Reaction rate
- —
- this channel exposes no reaction counts
- Posts in window
- 3
- of 71 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.
| Window | Rolling 30 days · latest post in window 28 August 2026 |
|---|---|
| Posts held | 71 (28 July 2026 – 28 August 2026) |
| Views total | 676 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 28 Aug 2026, 20:47 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
- 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
29 reactions across 16 posts, in 3 distinct kinds. The most used accounts for 44.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 13 | 44.8% | |
| ❤ | 8 | 27.6% | |
| 🔥 | 8 | 27.6% |
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 21 of the 71 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 29 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 71 most recent posts we hold, published 28 July 2026 to 28 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
- 1.41%
- 1 of 71 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
- 472
- over 70 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.
| erid | Posts | First seen | Last seen |
|---|---|---|---|
| 2VSb5wq2yhk | 1 | 31 July 2026 | 31 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 71 most recent posts we hold, published 28 July 2026 to 28 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
Основа алгоритма Quick Sort Как я уже говорил сегодня, суть алгоритма заключается в разделении массива на два подмассива. Этот алгоритм эффективен для больших объемов данных, так как худшая сложность составит О(n^2) Алгоритм 1. Выберите наибольшее значение индекса - в качестве pivot 2. Возьмите 2 переменные, чтобы указать левую и правую часть списка. Исключая наш pivot. Эта переменная и будет хранить наш массив …
Быстрая сортировка (Quick Sort) Быстрая сортировка - является высокоэффективным алгоритмом сортировки. Основана она на разбиении массива на меньше подмассивы. Большой массив делится на 2 более маленьких: один из них содержит значение меньше выбранного (pivot), другой больше значения pivot. Базовая суть алгоритма представлена в GiF. Посмотрите ее внимательно! Data Science: Алгоритмы и Структуры данных
Мемоизация (Memoization) По факту - это всего лишь сохранение результатов выполнения функций, чтобы предотвратить повторные вычисления с теми же самыми параметрами. Это один из способов оптимизаци, который применяется для увелечения скорости выполнения программ или подпрограмм. Алгоритм работы: 1. Если функция не вызывалась, то вызвать ее и сохранить результат 2. Если вызывалась, достать сохраненный результат по…
👍1
Этапы динамического программирования 1. Проблему делять на меньшую перекрывающую подзадачу 2. Оптимальное решение достигается при помощи использования оптимального решения небольших задач 3. Под капотом, всегда (почти всегда) используется memoization Data Science: Алгоритмы и Структуры данных
👍2
Примеры использования динамического программирования 1. Ханойская башня 2. Кратчайший путь Дейкстры 3. Числовой ряд Фибоначи 4. Проблемы с рюкзаками, камнями и прочим набором задач, где надо сборка вещей 5. Все возможные пары кратчайшего пути по Флойд-Варшалл 6. Планирование Data Science: Алгоритмы и Структуры данных
👍1
Введение в динамическое программирование Сам подход динамического программирования очень схож с принципом, который мы не давно с вами рассмотрели: "Разделяй и Властвуй". То есть динамическое программироание - это тоже разбитие проблемы на более мелкие подзадачи. Однако разница между подходами есть! Подзадачи динамического программирования не решаются независимо. Данные результаты запоминаются и используются для ана…
Задача: за минимальное количество перестановок, найти сведение меньших или равных элементов к значению K Дан массив из n натуральных чисел и числа K. Найдите минимальное количество перестановок, необходимое для сведение всех чисел, что равны или меньше числу К. Алгоритм 1. Создайте счетчик count, занесите туда все элементы что меньше или равны K 2. Используя технику двух указателей и "двигающегося окна", длиной co…
TensorFlow.js: машинное обучение на JavaScript с доставкой в браузер ➡️Читать статью Data Science: Алгоритмы и Структуры данных
Shell Sort - сортировка массива Новый вариант сортировки, который мы сегодня разберем - Shell Sort. Этот алгоритм использует сортировку вставки для широко распространенных элементов, сначала сортируя их, а затем сортирует менее широко расположенные элементы. Этот интервал называется интервалом. Данный интервал высчитывается при помощи формулы Кнута: h = h * 3 + 1, где h - интервал с начальным значением 1. На ка…
Алгоритм удаления из BST (дерева бинарного поиска) Данный алгоритм очень схож с алгоритмом поиска. Поэтому по идее проблем с его пониманием не должно возникнуть. Кроме того, а что если у данного узла(что мы удаляем) - есть дочерние узлы. Что же, давайте рассмотрим данный алгоритм. 1. Находим узел который собираемся удалить и удаляем 2. Если у удаляемого узла есть только один дочерний элемент, то скопируйте дочерни…
SciPy — библиотека для языка программирования Python с открытым исходным кодом, предназначенная для выполнения научных и инженерных расчётов. Data Science: Алгоритмы и Структуры данных
Showing the 12 most recent of 71 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.
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.
Names
Channels on the register whose handles appear in this channel's posts.
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.
@porscode_tg · 28,393
Telegram ranks this channel #16 of 77 here — alongside 76 others — read 9 September 2026
@developer_shelf · 26,815
Telegram ranks this channel #18 of 63 here — alongside 62 others — read 11 September 2026
@wind_community · 41,313
Telegram ranks this channel #20 of 74 here — alongside 73 others — read 29 August 2026
@seniorpy · 39,864
Telegram ranks this channel #25 of 92 here — alongside 91 others — read 30 August 2026
@EnglishScript · 45,107
Telegram ranks this channel #34 of 82 here — alongside 81 others — read 27 August 2026
@osint_pythons · 36,535
Telegram ranks this channel #39 of 84 here — alongside 83 others — read 1 September 2026
@maths_lib · 25,423
Telegram ranks this channel #46 of 67 here — alongside 66 others — read 14 September 2026
@nuancesprog · 56,542
Telegram ranks this channel #49 of 84 here — alongside 83 others — read 23 August 2026
@databases_secrets · 30,002
Telegram ranks this channel #54 of 82 here — alongside 81 others — read 7 September 2026
@mediatrends · 584,160
Telegram ranks this channel #60 of 90 here — alongside 89 others — read 13 September 2026
@pythonist24 · 55,872
Telegram ranks this channel #68 of 88 here — alongside 87 others — read 23 August 2026
@Python_per_month · 28,238
Telegram ranks this channel #69 of 86 here — alongside 85 others — read 9 September 2026
@trendo · 67,788
Telegram ranks this channel #74 of 90 here — alongside 89 others — read 20 August 2026
This channel appears in 13 seed channels' Telegram-generated recommendation lists 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 16 September 2026 — this entry's latest reading, not the date you are reading this.
“Data Science: Алгоритмы и Структуры данных” (@structuredata), 7,691 subscribers as measured 16 September 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.