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 743 Technologies & Applications9 391...

📈 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 13 667 subscribers, ranking 9 391 in the Technologies & Applications category and 31 743 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 667 subscribers.

According to the latest data from 08 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 150 over the last 30 days and by 4 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.97%. Within the first 24 hours after publication, content typically collects 2.27% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 089 views. Within the first day, a publication typically gains 310 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 09 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.

13 667

Subscribers

+424 hours

+437 days

+15030 days

1 089

Post views

~ 31024 hours

~ 45848 hours

7.97%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

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Hey everyone 👋 Tomorrow we are kicking off a new short & free series called: 📊 Data Importing Series 📊 We’ll go through all the real ways to pull data into Python: → CSV, Excel, JSON and more → Databases & SQL databases → APIs, Google Sheets, even PDFs & web scraping Short lessons, ready-to-copy code, zero boring theory. First part drops tomorrow. Turn on notifications so you don’t miss it 🔔 Who’s excited? React with a 🔥 if you are.

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Normalization vs Standardization: Why They’re Not the Same People treat these two as interchangeable. they’re not. 👉 Normalization (Min-Max scaling): Compresses values to 0–1. Useful when magnitude matters (pixel values, distances). 👉 Standardization (Z-score): Centers data around mean=0, std=1. Useful when distribution shape matters (linear/logistic regression, PCA). 🔑 Key idea: Normalization preserves relative proportions. Standardization preserves statistical structure. Pick the wrong one, and your model’s geometry becomes distorted.

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📚 Data Science Riddle - CNN Kernels Which convolution increases channel depth but not spatial size?

Anonymous voting

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✅ Complete AI (Artificial Intelligence) Roadmap 🤖🚀 1️⃣ Basics of AI 🔹 What is AI? 🔹 Types: Narrow AI vs General AI 🔹 AI vs ML vs DL 🔹 Real-world applications 2️⃣ Python for AI 🔹 Python syntax & libraries 🔹 NumPy, Pandas for data handling 🔹 Matplotlib, Seaborn for visualization 3️⃣ Math Foundation 🔹 Linear Algebra: Vectors, Matrices 🔹 Probability & Statistics 🔹 Calculus basics 🔹 Optimization techniques 4️⃣ Machine Learning (ML) 🔹 Supervised vs Unsupervised 🔹 Regression, Classification, Clustering 🔹 Scikit-learn for ML 🔹 Model evaluation metrics 5️⃣ Deep Learning (DL) 🔹 Neural Networks basics 🔹 Activation functions, backpropagation 🔹 TensorFlow / PyTorch 🔹 CNNs, RNNs, LSTMs 6️⃣ NLP (Natural Language Processing) 🔹 Text cleaning & tokenization 🔹 Word embeddings (Word2Vec, GloVe) 🔹 Transformers & BERT 🔹 Chatbots & summarization 7️⃣ Computer Vision 🔹 Image processing basics 🔹 OpenCV for CV tasks 🔹 Object detection, image classification 🔹 CNN architectures (ResNet, YOLO) 8️⃣ Model Deployment 🔹 Streamlit / Flask APIs 🔹 Docker for containerization 🔹 Deploy on cloud: Render, Hugging Face, AWS 9️⃣ Tools & Ecosystem 🔹 Git & GitHub 🔹 Jupyter Notebooks 🔹 DVC, MLflow (for tracking models) 🔟 Build AI Projects 🔹 Chatbot, Face recognition 🔹 Spam classifier, Stock prediction 🔹 Language translator, Object detector

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📚 Data Science Riddle Your model's loss fluctuates but doesn't decrease overall. What's the most likely issue?

Anonymous voting

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The Difference Between Model Accuracy and Business Accuracy A model can be 95% accurate… yet deliver 0% business value. Why❔ Because data science metrics ≠ business metrics. 📌 Examples: - A fraud model catches tiny fraud but misses large ones - A churn model predicts already obvious churners - A recommendation model boosts clicks but reduces revenue Always align ML metrics with business KPIs. Otherwise, your “great model” is just a great illusion.

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📚 Data Science Riddle Your estimate has high variance. Best fix?

Anonymous voting

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Repost from Programming, data science, ML - free courses by Big Data Specialist

Covers Spark for ML, graph processing (GraphFrames), and integration with Hadoop from Stanford University.

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📚 Data Science Riddle A feature has low importance but domain experts insist it matters. What do you do?

Anonymous voting

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6 Must-Know Data Engineering Tools For Beginners

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📚 Data Science Riddle You need fast reads of small files. What storage options fits best?

Anonymous voting

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🛠️ Running Code in Jupyter Notebooks Jupyter Notebooks let you write & run code interactively. Here’s a quick guide to make your workflow smoother: ▶️ Kernel & Code Cells - Each notebook is tied to a single kernel (e.g. IPython). - Code cells are where you write and execute code. ⌨️ Useful Shortcuts - Shift + Enter → run current cell, move to next - Alt + Enter → run current cell, insert new one below - Ctrl + Enter → run current cell, stay in place 🔄 Kernel Management - Interrupt the kernel if code hangs. - Restart kernel to reset memory & variables. 🖥️ Output Handling - Results & errors appear directly under the cell. - Long-running code outputs appear as they’re generated. - Large outputs can be scrolled or collapsed for clarity. 💡 Pro Tip: Always “Restart & Run All” before sharing or saving a notebook. This ensures reproducibility and clean results. 👉 Explore

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📚 Data Science Riddle You want to prevent inconsistent data across environments. What helps most?

Anonymous voting

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If you want to become a Data Scientist, this is the path to follow.

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The Big Data bible from Stanford: MapReduce, Spark, recommendation systems, PageRank, locality-sensitive hashing, Large scale machine learning and mining social networks/streams all explained clearly with real algorithms you can code today. 500 pages of pure gold.

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📚 Data Science Riddle You want to detect extreme values visually in one plot. Which one is best?

Anonymous voting

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Everything You need To Know About Databricks

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📚 Data Science Riddle A query runs slowly due to large table scans. What's the most targeted fix?

Anonymous voting

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The Simplest Machine Learning Cheatsheet

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📚 Data Science Riddle Two team members run the same notebook but get different results. What's the culprit?

Anonymous voting