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Data Analytics

Data Analytics

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Dive into the world of Data Analytics โ€“ uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho

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๐Ÿ“ˆ Analytical overview of Telegram channel Data Analytics

Channel Data Analytics (@dataanalyticsx) in the English language segment is an active participant. Currently, the community unites 28 972 subscribers, ranking 4 734 in the Technologies & Applications category and 22 757 in the Russia region.

๐Ÿ“Š Audience metrics and dynamics

Since its creation on ะฝะตะฒั–ะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 28 972 subscribers.

According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 507 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.66%. Within the first 24 hours after publication, content typically collects 1.27% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 061 views. Within the first day, a publication typically gains 369 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as sellerflash, buybox, buyer, chaos, effortless.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œDive into the world of Data Analytics โ€“ uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikhoโ€

Thanks to the high frequency of updates (latest data received on 15 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 Technologies & Applications category.

28 972
Subscribers
+824 hours
+837 days
+50730 days
Posts Archive
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SQL interview.pdf4.57 MB

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This channels is for Programmers, Coders, Software Engineers. 0๏ธโƒฃ Python 1๏ธโƒฃ Data Science 2๏ธโƒฃ Machine Learning 3๏ธโƒฃ Data Visualization 4๏ธโƒฃ Artificial Intelligence 5๏ธโƒฃ Data Analysis 6๏ธโƒฃ Statistics 7๏ธโƒฃ Deep Learning 8๏ธโƒฃ programming Languages โœ… https://t.me/addlist/8_rRW2scgfRhOTc0 โœ… https://t.me/Codeprogrammer

#๐—ฆ๐—ค๐—Ÿ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—–๐—ต๐—ฒ๐—ฎ๐˜๐˜€๐—ต๐—ฒ๐—ฒ๐˜ โ†’ ๐—–๐—ฟ๐—ฎ๐—ฐ๐—ธ ๐—”๐—ป๐˜† ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—ช๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐—–๐—ผ๐—ป๐—ฐ๐—ฒ๐—ฝ๐˜๐˜€ โžœ
#๐—ฆ๐—ค๐—Ÿ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—–๐—ต๐—ฒ๐—ฎ๐˜๐˜€๐—ต๐—ฒ๐—ฒ๐˜ โ†’ ๐—–๐—ฟ๐—ฎ๐—ฐ๐—ธ ๐—”๐—ป๐˜† ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—ช๐—ถ๐˜๐—ต ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐—–๐—ผ๐—ป๐—ฐ๐—ฒ๐—ฝ๐˜๐˜€ โžœ ๐Ÿญ. ๐—ฆ๐—ค๐—Ÿ ๐—๐—ผ๐—ถ๐—ป๐˜€ โ†’ Combine data from multiple tables ๐Ÿก† INNER JOIN โ†’ Returns matching records from both tables ๐Ÿก† LEFT JOIN โ†’ Returns all records from the left table + matches from the right ๐Ÿก† RIGHT JOIN โ†’ Returns all records from the right table + matches from the left ๐Ÿก† FULL JOIN โ†’ Returns all records from both tables โžœ ๐Ÿฎ. ๐——๐—”๐—ง๐—” ๐—™๐—œ๐—Ÿ๐—ง๐—˜๐—ฅ๐—œ๐—ก๐—š โ†’ Retrieve specific records efficiently ๐Ÿก† WHERE โ†’ Filters rows before aggregation ๐Ÿก† HAVING โ†’ Filters after aggregation ๐Ÿก† BETWEEN, IN, LIKE โ†’ Advanced filtering โžœ ๐Ÿฏ. ๐—”๐—š๐—š๐—ฅ๐—˜๐—š๐—”๐—ง๐—œ๐—ข๐—ก & ๐—š๐—ฅ๐—ข๐—จ๐—ฃ๐—œ๐—ก๐—š โ†’ Summarize data ๐Ÿก† GROUP BY โ†’ Groups data based on a column ๐Ÿก† COUNT, SUM, AVG, MIN, MAX โ†’ Perform calculations โžœ ๐Ÿฐ. ๐—ฆ๐—จ๐—•๐—ค๐—จ๐—˜๐—ฅ๐—œ๐—˜๐—ฆ & ๐—–๐—ง๐—˜๐˜€ โ†’ Break complex queries ๐Ÿก† Subquery โ†’ Nested query inside another query ๐Ÿก† Common Table Expressions (CTE) โ†’ Temporary named query โžœ ๐Ÿฑ. ๐—œ๐—ก๐——๐—˜๐—ซ๐—œ๐—ก๐—š & ๐—ข๐—ฃ๐—ง๐—œ๐— ๐—œ๐—ญ๐—”๐—ง๐—œ๐—ข๐—ก โ†’ Speed up queries ๐Ÿก† Indexes โ†’ Improve search speed ๐Ÿก† EXPLAIN PLAN โ†’ Analyze query performance โžœ ๐Ÿฒ. ๐—”๐——๐—ฉ๐—”๐—ก๐—–๐—˜๐—— ๐—ฆ๐—ค๐—Ÿ ๐—ง๐—ข๐—ฃ๐—œ๐—–๐—ฆ ๐Ÿก† Window Functions โ†’ RANK, ROW_NUMBER, LEAD, LAG ๐Ÿก† Stored Procedures & Triggers โ†’ Automate tasks ๐Ÿก† Normalization & Denormalization โ†’ Optimize database design โšก๏ธ BEST DATA SCIENCE CHANNELS ON TELEGRAM ๐ŸŒŸ

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