Анализ данных (Data analysis)
Data science, наука о данных. @haarrp - админ РКН: clck.ru/3FmyAp
Show more📈 Analytical overview of Telegram channel Анализ данных (Data analysis)
Channel Анализ данных (Data analysis) (@data_analysis_ml) in the Russian language segment is an active participant. Currently, the community unites 51 963 subscribers, ranking 2 576 in the Technologies & Applications category and 11 782 in the Russia region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 51 963 subscribers.
According to the latest data from 12 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 567 over the last 30 days and by -10 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 11.80%. Within the first 24 hours after publication, content typically collects 8.86% reactions from the total number of subscribers.
- Post reach: On average, each post receives 6 134 views. Within the first day, a publication typically gains 4 606 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 245.
- Thematic interests: Content is focused on key topics such as llm, контекст, openai, архитектура, deepseek.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Data science, наука о данных.
@haarrp - админ
РКН: clck.ru/3FmyAp”
Thanks to the high frequency of updates (latest data received on 13 November, 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.
pip install ydata-profiling
ydata-profiling — это библиотека Python для профилирования данных, которая автоматизирует и стандартизирует создание подробных отчетов, дополненных статистикой и визуализацией.
Фишка ydata-profiling в том, что она позволяет подготовить данные к анализу всего 1 строкой кода
Использование ydata-profiling может выглядеть так:
import pandas as pd
from ydata_profiling import ProfileReport
df = pd.read_csv('data.csv')
profile = ProfileReport(df, title="Profiling Report")
🖥 GitHub
@data_analysis_mlpip install kan_gpt
🖥 GitHub
@data_analysis_mlpip install -U phidata
А вот пара примеров AI-веб-приложений, созданных с помощью Phidata:
🟡 PDF AI — обобщает и отвечает на вопросы из PDF-файлов
🟡 ArXiv AI — отвечает на вопросы о статьях ArXiv, используя ArXiv API
🟡 HackerNews AI — обобщает истории, пользователей и делится тем, что нового на HackerNews
🖥 GitHub
🟡 Доки
@data_analysis_ml
git clone https://github.com/drawdb-io/drawdb
cd drawdb
npm install
npm run dev
🖥 GitHub
🟡 Перейти в редактор
@data_analysis_ml