Data Science
Learn how to analyze data effectively and manage databases with ease. Buy ads: https://telega.io/c/sql_databases
Show more๐ 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.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, 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|.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โLearn 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.
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| Date | Subscriber Growth | Mentions | Channels | |
| 27 June | 0 | |||
| 26 June | 0 | |||
| 25 June | 0 | |||
| 24 June | +7 | |||
| 23 June | +22 | |||
| 22 June | 0 | |||
| 21 June | 0 | |||
| 20 June | +2 | |||
| 19 June | 0 | |||
| 18 June | +1 | |||
| 17 June | +15 | |||
| 16 June | +1 | |||
| 15 June | +1 | |||
| 14 June | +1 | |||
| 13 June | +18 | |||
| 12 June | +8 | |||
| 11 June | +4 | |||
| 10 June | +8 | |||
| 09 June | 0 | |||
| 08 June | +3 | |||
| 07 June | +26 | |||
| 06 June | +8 | |||
| 05 June | +7 | |||
| 04 June | +15 | |||
| 03 June | 0 | |||
| 02 June | 0 | |||
| 01 June | +14 |
| 2 | ๐ฐ SQL CheatSheet ๐ฐ | 4 144 |
| 3 | ๐ Data Structure Cheat Sheet | 5 072 |
| 4 | ๐ฑData Science
๐ฑAdvanced Python: Top Tools for Data Science and Engineering | 5 990 |
| 5 | ๐
Advanced Python: Top Tools for Data Science and Engineering
๐ 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.
๐ Author: Joe Marini
๐ฐ Level: Intermediate
โฐ Duration: 2h 5m
๐ Topics: Pandas, Data Engineering, Data Science
๐ Join Data Science for more courses | 5 555 |
| 6 | Key Pandas Functions for Data Importing, Cleaning, and Statistics. Boost your data analysis workflow with essential Python commands | 5 596 |
| 7 | ๐ข Data Cleaning Tips Every Analyst Should Know
If your analysis feels off, itโs probably your data.
These 5 tips will help you clean your dataset like a pro:
โ๏ธ Handle missing values
โ๏ธ Remove duplicates
โ๏ธ Fix data types
โ๏ธ Standardize formats
โ๏ธ Detect and remove outliers
Clean data = better insights = better decisions. | 5 956 |
| 8 | ๐ Data Structures, you need to know for Coding interview | 6 659 |
| 9 | ๐ฑData Science
๐ฑData Science Reporting with Quarto for Python | 8 382 |
| 10 | ๐
Data Science Reporting with Quarto for Python
๐ Leverage the power of Quarto to build publication-quality reports, engaging presentation decks, and rich interactive webpages from Jupyter Notebook for Python.
๐ Author: Charlie Joey Hadley
๐ฐ Level: Intermediate
โฐ Duration: 2h 26m
๐ Topics: Data Reporting, Data Science, Data Analytics
๐ Join Data Science for more courses | 8 444 |
| 11 | ๐ Life Cycle of a Data Analytical Project | 7 940 |
| 12 | ๐ฅType of Databases | 10 035 |
| 13 | @LearnPython3 - Python Data Science Handbook 2nd ed.pdf | 11 369 |
| 14 | ๐ฐ ๐ Python Data Science Handbook 2nd Edition | 11 006 |
| 15 | ๐ฑData Science
๐ฑDecision Intelligence: Data Stories | 10 700 |
| 16 | ๐
Decision Intelligence: Data Stories
๐ Learn how to use key lessons from famous data stories around the world to improve decision-making, interpret data effectively, and communicate insights responsibly.
๐ Author: Franz Buscha
๐ฐ Level: Beginner
โฐ Duration: 45m
๐ Topics: Data Science, Decision Sciences, Data-driven Decision Making
๐ Join Data Science for more courses | 10 129 |
| 17 | ๐ฅ 8 Common database types explained | 10 296 |
| 18 | ๐ Learn Database
Databases power everything from websites and apps to enterprise systems. Hereโs a learning map that can help you master databases:
1 - Database Fundamentals
This includes topics like โWhat 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.) | 11 653 |
| 19 | Do you know the real difference between Data Engineering vs. Data Scientists vs. Data Analysts? | 9 698 |
| 20 | ๐ฑData Science
๐ฑThe 80/20 Rule of Data Science | 11 822 |
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