Data Science & Machine Learning
The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages. For promotions: @love_data
Show more📈 Analytical overview of Telegram channel Data Science & Machine Learning
Channel Data Science & Machine Learning (@datascienceinterviews) in the English language segment is an active participant. Currently, the community unites 27 238 subscribers, ranking 7 196 in the Education category and 15 552 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 27 238 subscribers.
According to the latest data from 23 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 79 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 0.48%. Within the first 24 hours after publication, content typically collects 0.32% reactions from the total number of subscribers.
- Post reach: On average, each post receives 131 views. Within the first day, a publication typically gains 88 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as insidead, mining, pinix, learning, neo.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages.
For promotions: @love_data”
Thanks to the high frequency of updates (latest data received on 24 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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| 10 | 🔹 DATA SCIENCE – INTERVIEW REVISION SHEET
1️⃣ What is Data Science?
> “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.”
Difference from Data Analytics:
• Data Analytics → past present (what/why)
• Data Science → future automation (what will happen)
2️⃣ Data Science Lifecycle (Very Important)
1. Business problem understanding
2. Data collection
3. Data cleaning preprocessing
4. Exploratory Data Analysis (EDA)
5. Feature engineering
6. Model building
7. Model evaluation
8. Deployment monitoring
Interview line:
> “I always start from business understanding, not the model.”
3️⃣ Data Types
• Structured → tables, SQL
• Semi-structured → JSON, logs
• Unstructured → text, images
4️⃣ Statistics You MUST Know
• Central tendency: Mean, Median (use when outliers exist)
• Spread: Variance, Standard deviation
• Correlation ≠ causation
• Normal distribution
• Skewness (income → right skewed)
5️⃣ Data Cleaning Preprocessing
Steps you should say in interviews:
1. Handle missing values
2. Remove duplicates
3. Treat outliers
4. Encode categorical variables
5. Scale numerical data
Scaling:
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• Standardization → normal distribution
6️⃣ Feature Engineering (Interview Favorite)
> “Feature engineering is creating meaningful input variables that improve model performance.”
Examples:
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• Create customer lifetime value
• Binning age groups
7️⃣ Machine Learning Basics
• Supervised learning: Regression, Classification
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8️⃣ Common Algorithms (Know WHEN to use)
• Regression: Linear regression → continuous output
• Classification: Logistic regression, Decision tree, Random forest, SVM
• Unsupervised: K-Means → segmentation, PCA → dimensionality reduction
9️⃣ Overfitting vs Underfitting
• Overfitting → model memorizes training data
• Underfitting → model too simple
Fixes:
• Regularization
• More data
• Cross-validation
🔟 Model Evaluation Metrics
• Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC
• Regression: MAE, RMSE
Interview line:
> “Metric selection depends on business problem.”
1️⃣1️⃣ Imbalanced Data Techniques
• Class weighting
• Oversampling / undersampling
• SMOTE
• Metric preference: Precision, Recall, F1, ROC-AUC
1️⃣2️⃣ Python for Data Science
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• Groupby
• Vectorization
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• Model drift
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> “Models must be monitored because data changes over time.”
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> “The goal was . I cleaned the data using . I performed EDA to identify . I built model and evaluated using . The final outcome was .”
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> “I enjoy solving complex problems using data and building models that automate decisions.”
Biggest challenge:
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Strength:
“Strong foundation in statistics and ML.”
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