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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 India28 047 Technologies & Applications9 084...

📈 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 14 053 subscribers, ranking 9 084 in the Technologies & Applications category and 28 047 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 053 subscribers.

According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 57 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 9.70%. Within the first 24 hours after publication, content typically collects 2.13% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 363 views. Within the first day, a publication typically gains 299 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 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 01 November, 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.

14 053

Subscribers

+124 hours

-37 days

+5730 days

1 363

Post views

~ 29924 hours

~ 41948 hours

9.70%

Engagement rate

No data

Posts per day

Ads index

beta

Posts Archive

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Types of Data Professionals

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CS109 Data Science By Harvard University ⌛️ 12 weeks ✅ Video lectures ✅ Slides ✅ Lab exercises 🔗 http://cs109.github.io/2015/pages/videos.html Note: i have issues with first video link but others are fine. #datascience #python #harvard ➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Classification of Deep Learning Models

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Source codes for data science projects from my Instagram post: https://www.instagram.com/p/CJwDIpCA0nc/ 1. Build chatbots: https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro 2. Credit card fraud detection: https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python 3. Fake news detection https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/ 4.Driver Drowsiness Detection https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/ 5. Recommender Systems (Movie Recommendation) https://data-flair.training/blogs/data-science-r-movie-recommendation/ 6. Sentiment Analysis https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/ 7. Gender Detection & Age Prediction https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/ #data_science #projects ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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4 pillars of data science

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

Detailed Data Science Roadmap to become a Data Scientist

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The Hierarchy of Data Jobs

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

Python course by kaggle Learn the most important language for data science. 🎬 8 lessons ⏰ 5 hours https://www.kaggle.com/learn/python #python ➖➖➖➖➖➖➖➖➖➖ Join @bigdataspecialist for more

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Hey folks, some of you probably already know that, I have Instagram page where i share educational posts about data science and machine learning. Your support in form of follow and possibly engagement on my posts would be very appreciated. Instagram Page Link: http://Instagram.com/bigdataspecialist ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Deep Learning

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👩‍💻 5 FREE DATA SCIENCE COURSES FOR BEGINNERS 👩‍🏫 CS109 Data Science (Harvard) - http://cs109.github.io/2015/pages/videos.html Data-Driven Decision Making (PwC) - https://www.coursera.org/learn/decision-making Machine Learning (Stanford) - https://www.coursera.org/learn/machine-learning Data Science Foundations (IBM) - https://cognitiveclass.ai/learn/data-science Data Science Specialization (JHU) - https://www.coursera.org/specializations/jhu-data-science Subscribe for more helpful data science learning materials and free courses #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Data Scientist Resume Checklist

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Best Statistic books for data science Practical statistics for data scientists by Peter Bruce and Andrew Bruce 🔗 Book Link Think Stats by Allen B. Downey 🔗 Book Link Computer Age Statistical Inference by Bradley Efron and Trevor Hastie 🔗 Book Link Statistics in Plain English by Timothy C. Urdan 🔗 Book Link #Statistics #books #data_science ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

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Anatomy of Data Scientistst

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Data Scientist

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

The "Approaching (Almost) Any Machine Learning Problem" book. by 4x Kaggle grandmaster Abhishek Thakur

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Hey, of course, If i find nice graphical representation I will send you, but now i can tell you how did I use every of these algorithms at my work. I used SVM for text and product classification (Some article belongs to sport category, some to business, medicine etc, similar with products, I used it to classify products into categories similar to what you have on Amazon. I used KNN for simple classification problems, but generally we don't use it much in production as there are more advanced ones. I used regression to predict continuous value as price of product. I used random forest (and Gradient boosting algorithms like LightGBM and XGBOOST) for predicting possibility that person will convert on some ad (for example that person will buy a product advertised in an ad). Both Random Forest and Gradient Boosting are based on decision trees, they are very similar but gradient boosting is more advanced. I used CNN for image recognition (finding patterns in images to recognize objects). I haven't used RNN (Recurrent neural networks ) much but they are used for problems that are recursive by their nature. For example good usage of it in my work would be for some NLP tasks (sentences could be considered as recursive so its used on text and speech data). Also they are used to simulate neuron activity in our brain). I used K-means for clusterization of articles or products into different unlabeled clusters. It helps to determine which articles/products are similar to each other. I used PCA (Principal Component Analysis) to reduce number of dimensions for datasets that have too many of them. It also helped me to remove personal data from some datasets and model them as doubles (instead of names, surnames, date of birth etc). I hope this helps. I will send this to main channel in case somebody else find it useful.

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Data Distribution

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

The journey of Data Scientist