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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Red:📈 Análisis del canal de Telegram Data science/ML/AI
El canal Data science/ML/AI (@datascience_bds) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 14 028 suscriptores, ocupando la posición 8 800 en la categoría Tecnologías y Aplicaciones y el puesto 28 280 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 14 028 suscriptores.
Según los últimos datos del 05 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 108, y en las últimas 24 horas de 9, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.13%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.17% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 140 visualizaciones. En el primer día suele acumular 305 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- Intereses temáticos: El contenido se centra en temas clave como panda, learning, row, api, ethic.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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...”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 06 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
14 028
Suscriptores
+924 horas
+587 días
+10830 días
Carga de datos en curso...
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
| 06 octubre | +1 | |||
| 05 octubre | +9 | |||
| 04 octubre | +6 | |||
| 03 octubre | +8 | |||
| 02 octubre | +13 | |||
| 01 octubre | +13 |
Publicaciones del Canal
| 2 |  📊 Pandas Cheatsheet Every Data Analyst Should Save
Pandas is one of the most important tools for data analysis. Master these core operations to work faster and more efficiently:
🔹 Read & Inspect Data
head(), shape, dtypes, describe()
🔹 Select & Filter Data
Extract relevant rows and columns with ease.
🔹 Row Selection
Use loc[] (labels) and iloc[] (positions).
🔹 Handle Missing Values
isnull(), dropna(), fillna()
🔹 Group & Aggregate
Summarize data using groupby() and aggregation functions.
🔹 Merge & Join Data
Combine datasets with merge() using different join types.
#Pandas | 323 |
| 3 | SQLBolt: Interactive SQL
You can learn SQL by writing real queries directly in the browser. Each short lesson ends with interactive exercises that give instant feedback. It covers SELECT, filters, joins, aggregates, inserting/updating data, creating tables, and more.
📚 Free Interactive Exercises
⏰ Duration: Self-paced (can finish in a few hours)
🏃♂️ Self Paced
👨🏫 Created by: SQLBolt
🔗 Link
#SQL #DataScience #Interactive
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
👉 Join @bigdataspecialist for more 👈 | 407 |
| 4 |  Tools vs MCP vs Skills: 3 Layers That Power Production AI Agents | 503 |
| 5 |  LLM inference speed with vs. without KV caching | 614 |
| 6 | Probability Distributions Cheat Sheet.pdf | 717 |
| 7 |  Most Asked Topics in AI Engineer Interviews
Based on 2026 candidate reports | 743 |
| 8 | 🗃 SQL has a trick beginners often miss
Suppose you want:
The top 3 customers by total spending.
You might write a complicated query.
But first think in two steps:
1. Calculate spending per customer
GROUP BY customer_id
2. Rank the result
ORDER BY total_spending DESC
LIMIT 3
So:
SELECT
customer_id,
SUM(amount) AS total_spending
FROM orders
GROUP BY customer_id
ORDER BY total_spending DESC
LIMIT 3;
The important idea isn't memorizing this query. It's learning to break SQL problems into: filter → group → calculate → sort → limit
Once you start thinking in those stages, complicated SQL questions become much easier to attack.
#SQL | 830 |
| 9 |  Generative AI Project Structure | 760 |
| 10 |  Data Science & Machine Learning: What’s the Connection?
Data Science and Machine Learning are closely connected, but they are not the same thing.
Data Science is the broader field of using data to discover insights, solve problems, and support better decisions.
Machine Learning (ML) is one of the key technologies used within Data Science to make predictions and automate decisions from data.
🔹 Data Science : Collects, cleans, analyzes, and visualizes data
🔹 Machine Learning : Learns patterns from data and makes predictions
🔹 Together : Turn raw data into useful insights and intelligent solutions
For example, a company can use Data Science to analyze customer behavior and then use Machine Learning to predict which customers are likely to leave.
👉 In simple terms: Data Science works with data to understand what is happening, while Machine Learning helps computers learn from that data to predict what may happen next. | 861 |
| 11 | Generative AI
#AI #Book | 878 |
| 12 | 🐼 Pandas Has a Built-In Way to Find Duplicates
Most people discover:
df.drop_duplicates()
But before deleting anything, try:
df.duplicated().sum()
This tells you how many duplicate rows exist.
Want to see them?
df[df.duplicated()]
Want to check duplicates based on specific columns?
df[df.duplicated(subset=["email"])]
And here's a useful one:
df[df.duplicated(subset=["email"], keep=False)]
keep=False marks every occurrence of the duplicate.
These commands come in handy when you're trying to understand why duplicates exist before removing them.
#Pandas
@datascience_bds | 1 048 |
| 13 |  Python vs R: Command Comparison
#Python #Research | 1 023 |
| 14 | 🎯Recommendation Systems
Have you wver wondered why YouTube recommends certain videos, Spotify suggests songs you might like, or Netflix shows movies that match your interests? One major reason is Data Science.
Recommendation systems analyze user behavior and use that information to predict what a person is likely to enjoy or interact with.
🔍 How Does It Work?
Imagine you watch several videos about:
🤖 Artificial Intelligence
🐍 Python
📊 Data Science
The system collects signals such as:
• What you watch
• How long you watch it
• What you like or dislike
• What you search for
• What you skip
• What similar users watch
The system can then identify patterns and recommend content that matches your interests.
🧠 Common Approaches
1. Collaborative Filtering
"If users similar to you liked these items, you may like them too."
2. Content-Based Filtering
"You liked this type of content before, so here is more content with similar characteristics."
3. Hybrid Systems
Combine multiple approaches to produce better recommendations.
🚀 Where Are Recommendation Systems Used?
🎬 Netflix: Movies & shows
▶️ YouTube: Videos
🎵 Spotify: Music & playlists
🛒 Amazon : Products
📱 Social media: Posts and content
The important idea is simple:
Data → Patterns → Predictions → Recommendations
This is a real-world example of how Data Science turns massive amounts of user data into personalized experiences. | 1 096 |
| 15 | Introduction to Artificial Intelligence
#AI #Book | 1 058 |
| 16 |  Difference Between Z Test and T Test | 1 194 |
| 17 | AI For DataScience
#Book | 1 252 |
| 18 | 🤖 50 Machine Learning Project Ideas
Looking to strengthen your Machine Learning portfolio? Here are 50 project ideas ranging from beginner to advanced.
🟢 Beginner
1. Iris Flower Classification
2. Titanic Survival Prediction
3. House Price Prediction
4. Student Score Prediction
5. Spam Email Detection
6. Movie Recommendation System
7. Customer Churn Prediction
8. Loan Approval Prediction
9. Wine Quality Prediction
10. Diabetes Prediction
11. Heart Disease Prediction
12. Car Price Prediction
13. Salary Prediction
14. Fake News Detection
15. Handwritten Digit Recognition
🟡 Intermediate
16. Sentiment Analysis on Reviews
17. Stock Price Prediction
18. Sales Forecasting
19. Credit Card Fraud Detection
20. Image Classification
21. Dog vs Cat Classifier
22. Traffic Sign Recognition
23. Face Mask Detection
24. Customer Segmentation
25. Music Recommendation System
26. Crop Recommendation System
27. Disease Prediction System
28. Energy Consumption Prediction
29. Resume Screening System
30. News Topic Classification
31. Emotion Detection from Text
32. Fake Job Posting Detection
33. Credit Risk Analysis
34. Movie Genre Classification
35. Flight Fare Prediction
🔴 Advanced
36. Object Detection with YOLO
37. Face Recognition Attendance System
38. AI Chatbot with NLP
39. Image Caption Generator
40. Speech Emotion Recognition
41. Plant Disease Detection
42. Brain Tumor Detection
43. Sign Language Recognition
44. AI Resume Analyzer
45. Medical Image Classification
46. Autonomous Lane Detection
47. Human Activity Recognition
48. DeepFake Detection
49. RAG-Based Question Answering System
50. AI Virtual Assistant
💡 Which Machine Learning project are you planning to build next? Let us know in the comments! 👇
@datascience_bds | 1 379 |
| 19 |  SQL Roadmap | 1 168 |
| 20 |  🔄 Why Cross Validation Is Better Than One Train/Test Split
Imagine flipping a coin 10 times. You might get 8 heads.
Does that mean the coin is biased?
Not necessarily.
A single train/test split can also give a misleading performance estimate.
Cross Validation repeats the process multiple times using different splits.
Instead of trusting one lucky result... You measure average performance across several experiments.
It's a much better estimate of how your model will perform on unseen data. | 1 274 |
