Data Science & Machine Learning
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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
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Network:📈 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 77 335 subscribers, ranking 1 996 in the Education category and 3 959 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 335 subscribers.
According to the latest data from 30 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 354 over the last 30 days and by 45 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.69%. Within the first 24 hours after publication, content typically collects 1.10% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 081 views. Within the first day, a publication typically gains 847 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 learning, accuracy, distribution, panda, dataset.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“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”
Thanks to the high frequency of updates (latest data received on 31 August, 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.
77 335
Subscribers
+4524 hours
+377 days
+35430 days
Posts Archive
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SQL Cheatsheet 📝
This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.
1. Database Basics
-
CREATE DATABASE db_name;
- USE db_name;
2. Tables
- Create Table: CREATE TABLE table_name (col1 datatype, col2 datatype);
- Drop Table: DROP TABLE table_name;
- Alter Table: ALTER TABLE table_name ADD column_name datatype;
3. Insert Data
- INSERT INTO table_name (col1, col2) VALUES (val1, val2);
4. Select Queries
- Basic Select: SELECT * FROM table_name;
- Select Specific Columns: SELECT col1, col2 FROM table_name;
- Select with Condition: SELECT * FROM table_name WHERE condition;
5. Update Data
- UPDATE table_name SET col1 = value1 WHERE condition;
6. Delete Data
- 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);
- Drop Index: DROP INDEX idx_name;
11. Subqueries
- SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);
12. Views
- Create View: CREATE VIEW view_name AS SELECT * FROM table_name;
- Drop View: DROP VIEW view_name;
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When starting off your data analytics journey you DON'T need to be a SQL guru from the get-go.
In fact, most SQL skills you will only learn on the job with:
- real business problems.
- actual data sets.
- imperfect data architecture.
- other people to collaborate with.
So be kind to yourself, give yourself time to grow and above all...
try to become proficient at SQL rather than perfect.
The rest will take care of itself along the way! 😉
𝟮𝟱+ 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 😍
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What does this line do?
from mytools import cleaner
How do you install an external module like numpy?
What is required to make a Python folder a package?
Which of the following is a built-in Python module?
📊 Data Science Essentials: What Every Data Enthusiast Should Know!
1️⃣ Understand Your Data
Always start with data exploration. Check for missing values, outliers, and overall distribution to avoid misleading insights.
2️⃣ Data Cleaning Matters
Noisy data leads to inaccurate predictions. Standardize formats, remove duplicates, and handle missing data effectively.
3️⃣ Use Descriptive & Inferential Statistics
Mean, median, mode, variance, standard deviation, correlation, hypothesis testing—these form the backbone of data interpretation.
4️⃣ 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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Know key algorithms—linear regression, decision trees, random forests, and clustering—to develop predictive models.
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Essential Python and SQL topics for data analysts 😄👇
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 :)
📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗶𝗻 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱/𝗣𝘂𝗻𝗲 😍
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