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
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Network:📈 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 13 906 subscribers, ranking 8 914 in the Technologies & Applications category and 28 863 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 906 subscribers.
According to the latest data from 29 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 86 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.48%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 039 views. Within the first day, a publication typically gains 284 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 30 August, 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.
13 906
Subscribers
No data24 hours
-57 days
+8630 days
Posts Archive
13 906
Python for Data Science: A Beginner’s Guide
Python is a programmer darling for plenty of reasons: the language is easy to read and work with, relatively simple to learn, and popular enough that there’s a great community and plenty of resources available.
And if you needed one more reason to consider starting Python for beginners, it plays an important role in lucrative data careers as well! Learning Python for data science or data analysis will give you a variety of useful skills.
✅ Free Online Tutorial
🧱 8 modules
🏃♂️ Self paced
Source: learntocodewithme
🔗 Course Link
#Data_Science #python #Python_For_Data_Science
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👉Join @bigdataspecialist for more👈
13 906
Visualize data on Google Maps Platform
Learn to translate external data sources to graphics on maps.
✅ Free Online Course
🧱 4 modules
🎬 Video Lectures
🏃♂️ Self paced
📊 Lab: 1
🧮 Quiz
Source: Google
🔗 https://developers.google.com/learn/pathways/maps-visualize-data?hl=en
#Data_Science #Google_Map #Data_Visualization
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👉Join @bigdataspecialist for more👈
13 906
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
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
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
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
Detailed Data Science Roadmap to become a Data Scientist
13 906
Python course by kaggle
Learn the most important language for data science.
🎬 8 lessons
⏰ 5 hours
https://www.kaggle.com/learn/python
#python
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Join @bigdataspecialist for more
13 906
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
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
👩💻 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
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
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
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Join @datascience_bds for more cool data science materials.
*This channel belongs to @bigdataspecialist group
13 906
The "Approaching (Almost) Any Machine Learning Problem" book.
by 4x Kaggle grandmaster Abhishek Thakur
13 906
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.
