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Data Science

Data Science

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Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases

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๐Ÿ“ˆ Analytical overview of Telegram channel Data Science

Channel Data Science (@sql_databases) in the English language segment is an active participant. Currently, the community unites 70 985 subscribers, ranking 2 262 in the Education category and 4 575 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 70 985 subscribers.

According to the latest data from 26 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -11 over the last 30 days and by -29 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 10.67%. Within the first 24 hours after publication, content typically collects 2.43% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 7 573 views. Within the first day, a publication typically gains 1 723 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 database, learning, linkedin, udemy, 029k|.

๐Ÿ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
โ€œLearn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databasesโ€

Thanks to the high frequency of updates (latest data received on 27 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.

70 985
Subscribers
-2924 hours
-537 days
-1130 days
Posts Archive
๐Ÿ“‹ Checklist to become Data Analyst
๐Ÿ“‹ Checklist to become Data Analyst

๐Ÿ”ฐ SQL CheatSheet ๐Ÿ”ฐ
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๐Ÿ”ฐ SQL CheatSheet ๐Ÿ”ฐ

๐Ÿ“– Data Structure Cheat Sheet
๐Ÿ“– Data Structure Cheat Sheet

๐Ÿ“ฑData Science ๐Ÿ“ฑAdvanced Python: Top Tools for Data Science and Engineering

๐Ÿ”… Advanced Python: Top Tools for Data Science and Engineering ๐Ÿ“ This comprehensive course is designed to equip you with the
๐Ÿ”… Advanced Python: Top Tools for Data Science and Engineering ๐Ÿ“ This comprehensive course is designed to equip you with the essential skills for data analysis and application development using Python and popular data tools and libraries. ๐ŸŒ Author: Joe Marini ๐Ÿ”ฐ Level: Intermediate โฐ Duration: 2h 5m ๐Ÿ“‹ Topics: Pandas, Data Engineering, Data Science ๐Ÿ”— Join Data Science for more courses

Key Pandas Functions for Data Importing, Cleaning, and Statistics. Boost your data analysis workflow with essential Python co
Key Pandas Functions for Data Importing, Cleaning, and Statistics. Boost your data analysis workflow with essential Python commands

๐Ÿ”ข Data Cleaning Tips Every Analyst Should Know If your analysis feels off, itโ€™s probably your data. These 5 tips will help y
๐Ÿ”ข Data Cleaning Tips Every Analyst Should Know If your analysis feels off, itโ€™s probably your data. These 5 tips will help you clean your dataset like a pro: โœ”๏ธ Handle missing values โœ”๏ธ Remove duplicates โœ”๏ธ Fix data types โœ”๏ธ Standardize formats โœ”๏ธ Detect and remove outliers Clean data = better insights = better decisions.

๐Ÿ“– Data Structures, you need to know for Coding interview
๐Ÿ“– Data Structures, you need to know for Coding interview

๐Ÿ“ฑData Science ๐Ÿ“ฑData Science Reporting with Quarto for Python

๐Ÿ”… Data Science Reporting with Quarto for Python ๐Ÿ“ Leverage the power of Quarto to build publication-quality reports, engagi
๐Ÿ”… Data Science Reporting with Quarto for Python ๐Ÿ“ Leverage the power of Quarto to build publication-quality reports, engaging presentation decks, and rich interactive webpages from Jupyter Notebook for Python. ๐ŸŒ Author: Charlie Joey Hadley ๐Ÿ”ฐ Level: Intermediate โฐ Duration: 2h 26m ๐Ÿ“‹ Topics: Data Reporting, Data Science, Data Analytics ๐Ÿ”— Join Data Science for more courses

๐Ÿ”„ Life Cycle of a Data Analytical Project
๐Ÿ”„ Life Cycle of a Data Analytical Project

๐Ÿ–ฅType of Databases
๐Ÿ–ฅType of Databases

@LearnPython3 - Python Data Science Handbook 2nd ed.pdf19.70 MB

๐Ÿ”ฐ ๐Ÿ“™ Python Data Science Handbook 2nd Edition
๐Ÿ”ฐ ๐Ÿ“™ Python Data Science Handbook 2nd Edition

๐Ÿ“ฑData Science ๐Ÿ“ฑDecision Intelligence: Data Stories

๐Ÿ”… Decision Intelligence: Data Stories ๐Ÿ“ Learn how to use key lessons from famous data stories around the world to improve d
๐Ÿ”… Decision Intelligence: Data Stories ๐Ÿ“ Learn how to use key lessons from famous data stories around the world to improve decision-making, interpret data effectively, and communicate insights responsibly. ๐ŸŒ Author: Franz Buscha ๐Ÿ”ฐ Level: Beginner โฐ Duration: 45m ๐Ÿ“‹ Topics: Data Science, Decision Sciences, Data-driven Decision Making ๐Ÿ”— Join Data Science for more courses

๐Ÿ–ฅ 8 Common database types explained
๐Ÿ–ฅ 8 Common database types explained

๐Ÿ“– Learn Database Databases power everything from websites and apps to enterprise systems. Hereโ€™s a learning map that can hel
๐Ÿ“– Learn Database Databases power everything from websites and apps to enterprise systems. Hereโ€™s a learning map that can help you master databases: 1 - Database Fundamentals This includes topics like โ€œWhat is a databaseโ€, RDBMS, SQL vs NoSQL, ACID vs BASE, OLTP vs OLAP, Transactions, and Isolation Levels. 2 - Data Models and Types Consists of topics like Relational Databases, Non-Relational Databases, and Data Types (Integer, String, Boolean, Date, JSON, etc). 3 - Querying and Language This includes topics like SQL Basics (SELECT, INSERT, etc), Advanced SQL (Views, Indexes, CTEs, etc), and NoSQL Querying (Aggregation and Key-Value Lookups). 4 - Indexing and Optimization Consists of topics like Indexing (B-Tree, Hash, and Bitmaps), Query Execution Plans, Denormalization vs Normalization, Sharding, Connecting Pooling, and Query Batching. 5 - Security, Backups, and Scaling This includes topics like User Roles, Permissions, Encryption, SQL Injection, High Availability (Replication and Failover), Horizontal vs Vertical Scaling. 6 - Tools and Ecosystem Consists of topics like Popular SQL Databases, NoSQL Database, GUI Tools, ORMs, Cloud DB services (RDS, DynamoDB, Google Cloud SQL, etc.)

Do you know the real difference between Data Engineering vs. Data Scientists vs. Data Analysts?
Do you know the real difference between Data Engineering vs. Data Scientists vs. Data Analysts?

๐Ÿ“ฑData Science ๐Ÿ“ฑThe 80/20 Rule of Data Science