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 693 Technologies & Applications9 381...

📈 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 9 381 in the Technologies & Applications category and 31 693 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

13 670

Python for Machine Learning.pdf2.74 MB

13 670

Ultimate Guide to Data Cleaning.pdf2.11 MB

13 670

Binomial Distribution

13 670

ChatGPT Training Explained

13 670

Python for Data Analysis.pdf8.95 MB

13 670

How To Design a Neural Network

13 670

Machine_Learning_With_Python_For_Everyone_Addison_Wesley_Professional.pdf9.00 MB

13 670

Adaptive Query Execution (AQE) in Apache Spark is a feature introduced to improve query performance dynamically at runtime, based on actual data statistics collected during execution. This makes Spark smarter and more efficient, especially when dealing with real-world messy data where planning ahead (at compile time) might be misleading. 🔍 Importance of AQE in Spark Runtime Optimization: AQE adapts the execution plan on the fly using real-time stats, fixing issues that static planning can't predict. Better Join Strategy: If Spark detects at runtime that one table is smaller than expected, it can switch to a broadcast join instead of a slower shuffle join. Improved Resource Usage: By optimizing stage sizes and join plans, AQE avoids unnecessary shuffling and memory usage, leading to faster execution and lower cost. 🪓 Handling Data Skew with AQE Data skew occurs when some partitions (e.g., specific keys) have much more data than others, slowing down those tasks. AQE handles this using: Skew Join Optimization: AQE detects skewed partitions and breaks them into smaller sub-partitions, allowing Spark to process them in parallel instead of waiting on one giant slow task. Automatic Repartitioning: It can dynamically adjust partition sizes for better load balancing, reducing the "straggler" effect from skew. 💡 Example: If a join key like customer_id = 12345 appears millions of times more than others, Spark can split just that key’s data into chunks, while keeping others untouched. This makes the whole join process more balanced and efficient. In summary, AQE improves performance, handles skew gracefully, and makes Spark queries more resilient and adaptive—especially useful in big, uneven datasets.

13 670

Curve-Fitting Methods and What they Mean

13 670

Probability for Machine Learning 📝.pdf2.83 MB

13 670

Linear Regression

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How to Merge Pandas DataFrames?

13 670

This diagram explains how Reinforcement Learning (RL) works in Machine Learning. It starts with raw input data. An agent interacts with an environment by selecting actions. The environment gives feedback in the form of rewards and new states. The agent learns which actions give the best rewards and improves over time. The result is an optimized output, based on trial, error, and learning from feedback.

13 670

Data Wrangling with Pandas Cheatsheet

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Kafka Usecases

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Roadmap for AI Engineers

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Popular Models for Machine Learning

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Machine Learning Algorithms