Data science/ML/AI

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

Network:Programming, data science, ML - free courses by Big Data Specialist India31 635 Technologies & Applications9 377...

📈 Analytical overview of Telegram channel Data science/ML/AI

Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 674 subscribers, ranking 9 377 in the Technologies & Applications category and 31 635 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 674 subscribers.

According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 155 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 8.03%. Within the first 24 hours after publication, content typically collects 2.25% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 098 views. Within the first day, a publication typically gains 308 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 panda, learning, row, api, ethic.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Thanks to the high frequency of updates (latest data received on 10 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 Technologies & Applications category.

13 674

Subscribers

+524 hours

+197 days

+15530 days

1 098

Post views

~ 30824 hours

~ 45248 hours

8.03%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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Statistical Moments (M1, M2) for Data Analysis Here are 5 curated PDFs diving into the mean (M1), variance (M2), and their applications in crafting research questions and sourcing data. A channel member requested resources on this topic and we delivered. If you have a topic you want resources on let us know, and we’ll make it happen! @datascience_bds

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📚 Data Science Riddle Model Accuracy improves after dropping half the features. Why?

Anonymous voting

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The Data Analyst Cheatsheet

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Cheatsheet: Imbalanced Data In Classification

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📚 Data Science Riddle You're building a chatbot but it gives generic answers. What's the root issue?

Anonymous voting

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Top ML Interview Questions & Answers.pdf1.42 KB

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Phases To Master Agentic AI

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Data Drift: The reason Good Models Go Bad You built a model that performed amazingly last month. Now? Accuracy tanked. Confusion Matrix looks like a crime scene. Welcome to Data Drift. The silent model killer. 📉 What Is Data Drift? It’s when the data your model sees today is different from the data it was trained on. Imagine you trained a model on pre-COVID shopping data then you tried to predict online purchases in 2021. People’s behavior changed. Your model didn’t. That’s drift. Reality shifted, but your math stayed still. 🧠 The Core Types ➡️ Covariate Drift: Input features change (e.g., user age distribution shifts). ➡️ Prior Drift: The target variable’s frequency changes (e.g., fewer defaults now). ➡️ Concept Drift: The relationship between input and output changes entirely. The last one is deadly. your model’s logic literally stops making sense. 🚨 Why It’s Dangerous Models decay quietly. By the time you notice lower performance, the damage( business or otherwise ) is already done. That’s why top teams monitor models like systems, not code. 🧩 The Fix 1. Track feature distributions over time (use KS test, PSI, or histograms). 2. Monitor prediction confidence — sudden uncertainty = red flag. 3. Retrain models periodically with fresh data. AI isn’t “build once.” It’s “maintain forever.”

A model is only as good as the world it was trained in and the world never stops changing.

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Comprehensive Feature Engineering Techniques

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📚 Data Science Riddle You're classifying product reviews (positive/negative). Which feature method is more effective for capturing context?

Anonymous voting

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Parameters vs Hyperparameters People confuse these all the time. Parameters: learned by the model during training. (e.g., weights in a neural network, coefficients in regression). Hyperparameters: set before training. They control how the model learns. (e.g., learning rate, number of layers, batch size). ✔️ Parameters = the student’s knowledge (changes as they study). ✔️ Hyperparameters = the teacher’s instructions (fixed rules of how to study). Tuning hyperparameters is often the difference between a good model and a useless one.

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DSA Cheatsheet

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📚 Data Science Riddle In Naive Bayes, what's the "naive" assumption?

Anonymous voting

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📚 Data Science Riddle You're training a hiring model. What's the biggest ethical risk?

Anonymous voting

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Cheatsheet: Ensemble Learning in ML

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cheatsheet-deep-learning.pdf3.35 KB

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AI/ML Cheatsheet

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Artificial Intelligence for Learning.pdf2.76 MB

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Softmax vs Sigmoid Functions Two of the most common activation functions… and two of the most misunderstood. Sigmoid: squashes input into a range between 0 and 1. Perfect for binary classification (yes/no problems). Example: spam or not spam. Softmax: takes a vector of numbers and turns them into probabilities that sum to 1. Perfect for multi-class classification (cat vs dog vs horse). 👉 Rule of thumb: Binary task → use Sigmoid. Multi-class task → use Softmax. Simple, but if you get this wrong, your model will never make sense.

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Data Visualization Cheatsheet