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Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

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Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun

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๐Ÿ“ˆ Analytical overview of Telegram channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources

Channel Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources (@learndataanalysis) in the English language segment is an active participant. Currently, the community unites 51 885 subscribers, ranking 3 347 in the Education category and 7 045 in the India region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 51 885 subscribers.

According to the latest data from 22 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 433 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 4.35%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 257 views. Within the first day, a publication typically gains 0 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as analyst, |--, excel, visualization, analytic.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œData Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfunโ€

Thanks to the high frequency of updates (latest data received on 23 June, 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 Education category.

51 885
Subscribers
-1024 hours
+277 days
+43330 days
Posts Archive
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An Introduction to Analysis William R Wade 4th ed, 2010

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Commonly used Python libraries are: ๐Ÿ‘‰๐ŸปNumPy: This library is used for scientific computing and working with arrays of data. It provides functions for working with arrays of data, including mathematical operations, linear algebra, and random number generation. ๐Ÿ‘‰๐ŸปPandas: This library is used for data manipulation and analysis. It provides tools for importing, cleaning, and transforming data, as well as tools for working with time series data and performing statistical analysis. ๐Ÿ‘‰๐ŸปMatplotlib: This library is used for data visualization. It provides functions for creating a wide range of plots, including scatter plots, line plots, bar plots, and histograms. ๐Ÿ‘‰๐ŸปScikit-learn: This library is used for machine learning. It provides a range of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for model evaluation and selection. ๐Ÿ‘‰๐ŸปTensorFlow: This library is used for deep learning. It provides a range of tools and libraries for building and training neural networks, including support for distributed training and hardware acceleration.