Available now! Telegram Research 2025 โ the year's key insights 
Data Science & Machine Learning
Open in Telegram
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Show more๐ Analytical overview of Telegram channel Data Science & Machine Learning
Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 684 subscribers, ranking 2 114 in the Education category and 4 348 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 75 684 subscribers.
According to the latest data from 12 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 923 over the last 30 days and by 31 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.63%. Within the first 24 hours after publication, content typically collects 1.36% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 744 views. Within the first day, a publication typically gains 1 026 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
- Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โJoin this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_dataโ
Thanks to the high frequency of updates (latest data received on 13 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.
75 684
Subscribers
+3124 hours
+2057 days
+92330 days
Posts Archive
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1. Database Basics
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CREATE DATABASE db_name;
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2. Tables
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- Alter Table: ALTER TABLE table_name ADD column_name datatype;
3. Insert Data
- INSERT INTO table_name (col1, col2) VALUES (val1, val2);
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- UPDATE table_name SET col1 = value1 WHERE condition;
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- DELETE FROM table_name WHERE condition;
7. Joins
- Inner Join: SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;
- Left Join: SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;
- Right Join: SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;
8. Aggregations
- Count: SELECT COUNT(*) FROM table_name;
- Sum: SELECT SUM(col) FROM table_name;
- Group By: SELECT col, COUNT(*) FROM table_name GROUP BY col;
9. Sorting & Limiting
- Order By: SELECT * FROM table_name ORDER BY col ASC|DESC;
- Limit Results: SELECT * FROM table_name LIMIT n;
10. Indexes
- Create Index: CREATE INDEX idx_name ON table_name (col);
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11. Subqueries
- SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);
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- Create View: CREATE VIEW view_name AS SELECT * FROM table_name;
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1๏ธโฃ Understand Your Data
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Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.
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Mean, median, mode, variance, standard deviation, correlation, hypothesis testingโthese form the backbone of data interpretation.
4๏ธโฃ Master Data Visualization
Bar charts, histograms, scatter plots, and heatmaps make insights more accessible and actionable.
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Write optimized queries (
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Essential Python and SQL topics for data analysts ๐๐
Python Topics:
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Hope it helps :)
