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 14 058 suscriptores, ocupando la posición 9 091 en la categoría Tecnologías y Aplicaciones y el puesto 28 236 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 14 058 suscriptores.
Según los últimos datos del 27 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 97, y en las últimas 24 horas de -7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 9.29%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.10% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 1 306 visualizaciones. En el primer día suele acumular 295 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- 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 28 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.
14 058
Suscriptores
-724 horas
-17 días
+9730 días
Archivo de publicaciones
14 059
Loading an Excel file in Python
Excel files are packed with headers, logos, merged cells, and multiple sheets but pandas handles it all.
With just a few extra parameters, you can skip junk rows, pick exact columns,e.t.c
# Import the pandas library
import pandas as pd
# Specify the path to your Excel file (.xlsx or .xls)
filename = "data.xlsx"
# Read the Excel file into a DataFrame
# Common options you'll use all the time:
df = pd.read_excel(
filename,
sheet_name=0, # 0 = first sheet
header=0, # Row (0-indexed) to use as column names
skiprows=4, # Skip first 4 rows
nrows=1000, # Load only first 1000 rows
)
# Check the first five rows
df.head()14 059
Loading a CSV file in Python
CSV stands for Comma-Separated Values the most common format for tabular data everywhere.
With pandas, turning a CSV into a powerful, queryable DataFrame takes just a few clear lines.
# Import the pandas library
import pandas as pd
# Specify the path to your CSV file
filename = "data.csv"
# Read the CSV file into a DataFrame
df = pd.read_csv(filename)
#Checking the first five rows
df.head()14 059
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?
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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
14 059
📚 Data Science Riddle
Your model's loss fluctuates but doesn't decrease overall. What's the most likely issue?
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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.
14 059
📚 Data Science Riddle
Your estimate has high variance. Best fix?
14 059
Covers Spark for ML, graph processing (GraphFrames), and integration with Hadoop from Stanford University.
14 059
📚 Data Science Riddle
A feature has low importance but domain experts insist it matters. What do you do?
14 059
📚 Data Science Riddle
You need fast reads of small files. What storage options fits best?
14 059
🛠️ 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
14 059
📚 Data Science Riddle
You want to prevent inconsistent data across environments. What helps most?
14 059
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.
14 059
📚 Data Science Riddle
You want to detect extreme values visually in one plot. Which one is best?
14 059
📚 Data Science Riddle
A query runs slowly due to large table scans. What's the most targeted fix?
