Data Science
DS По всем вопросам- @haarrp @ai_machinelearning_big_data - machine learning @pythonl - Python @itchannels_telegram - 🔥 best it channels @ArtificialIntelligencedl - AI @pythonlbooks-📚 @programming_books_it -📚 Реестр РКН: https://clck.ru/3Fk3zS
Show more📈 Analytical overview of Telegram channel Data Science
Channel Data Science (@datascienceiot) is an active participant. Currently, the community unites 42 490 subscribers, ranking 3 078 in the Technologies & Applications category and 14 897 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 42 490 subscribers.
According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 341 over the last 30 days and by -13 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 11.73%. Within the first 24 hours after publication, content typically collects 2.56% reactions from the total number of subscribers.
- Post reach: On average, each post receives 4 985 views. Within the first day, a publication typically gains 1 088 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 0.
- Thematic interests: Content is focused on key topics such as llm, агентов, api, октября, разработчиков.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“DS
По всем вопросам- @haarrp
@ai_machinelearning_big_data - machine learning
@pythonl - Python
@itchannels_telegram - 🔥 best it channels
@ArtificialIntelligencedl - AI
@pythonlbooks-📚
@programming_books_it -📚
Реестр РКН: https://clck.ru/3...”
Thanks to the high frequency of updates (latest data received on 26 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
O(n³) и игнорируем константы. Для сравнения алгоритмов на больших данных это удобно.
Но современные CPU устроены намного сложнее: кэши, конвейеры, параллельное выполнение инструкций, SIMD, prefetching, память с разной задержкой.
Поэтому два алгоритма с одинаковым O(n) могут отличаться по скорости в разы.
А иногда алгоритм с формально «хуже» сложностью на реальных размерах данных оказывается быстрее.
Хорошая серия для тех, кто хочет перейти от «у этого O(n), значит быстро» к пониманию того, как код реально выполняется процессором.
en.algorithmica.org/hpc/complexity/
@machinelearning_books