Data science/ML/AI
Open in Telegram
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 š https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
Show more
Network:š 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 14 058 subscribers, ranking 9 091 in the Technologies & Applications category and 28 236 in the India region.
š Audience metrics and dynamics
Since its creation on Š½ŠµŠ²ŃŠ“Š¾Š¼Š¾, the project has demonstrated rapid growth, gathering an audience of 14 058 subscribers.
According to the latest data from 27 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 97 over the last 30 days and by -7 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 9.29%. Within the first 24 hours after publication, content typically collects 2.10% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 306 views. Within the first day, a publication typically gains 295 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.
š Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
āData science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
š https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...ā
Thanks to the high frequency of updates (latest data received on 28 October, 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.
14 058
Subscribers
-724 hours
-17 days
+9730 days
Posts Archive
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 š
Tomorrow we are kicking off a new short & free series called:
š Data Importing Series š
Weāll go through all the real ways to pull data into Python:
ā CSV, Excel, JSON and more
ā Databases & SQL databases
ā 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 donāt miss it š
Whoās excited? React with a š„ if you are.
14 059
Normalization vs Standardization: Why Theyāre Not the Same
People treat these two as interchangeable. theyāre not.
š Normalization (Min-Max scaling):
Compresses values to 0ā1.
Useful when magnitude matters (pixel values, distances).
š Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
š Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your modelās geometry becomes distorted.
14 059
š Data Science Riddle - CNN Kernels
Which convolution increases channel depth but not spatial size?
14 059
ā
Complete AI (Artificial Intelligence) Roadmap š¤š
1ļøā£ Basics of AI
š¹ What is AI?
š¹ Types: Narrow AI vs General AI
š¹ AI vs ML vs DL
š¹ Real-world applications
2ļøā£ Python for AI
š¹ Python syntax & libraries
š¹ NumPy, Pandas for data handling
š¹ Matplotlib, Seaborn for visualization
3ļøā£ Math Foundation
š¹ Linear Algebra: Vectors, Matrices
š¹ Probability & Statistics
š¹ Calculus basics
š¹ Optimization techniques
4ļøā£ Machine Learning (ML)
š¹ Supervised vs Unsupervised
š¹ Regression, Classification, Clustering
š¹ Scikit-learn for ML
š¹ Model evaluation metrics
5ļøā£ Deep Learning (DL)
š¹ Neural Networks basics
š¹ Activation functions, backpropagation
š¹ TensorFlow / PyTorch
š¹ CNNs, RNNs, LSTMs
6ļøā£ NLP (Natural Language Processing)
š¹ Text cleaning & tokenization
š¹ Word embeddings (Word2Vec, GloVe)
š¹ Transformers & BERT
š¹ Chatbots & summarization
7ļøā£ Computer Vision
š¹ Image processing basics
š¹ OpenCV for CV tasks
š¹ Object detection, image classification
š¹ CNN architectures (ResNet, YOLO)
8ļøā£ Model Deployment
š¹ Streamlit / Flask APIs
š¹ Docker for containerization
š¹ Deploy on cloud: Render, Hugging Face, AWS
9ļøā£ Tools & Ecosystem
š¹ Git & GitHub
š¹ Jupyter Notebooks
š¹ DVC, MLflow (for tracking models)
š Build AI Projects
š¹ Chatbot, Face recognition
š¹ Spam classifier, Stock prediction
š¹ Language translator, Object detector
14 059
š Data Science Riddle
Your model's loss fluctuates but doesn't decrease overall. What's the most likely issue?
14 059
The Difference Between Model Accuracy and Business Accuracy
A model can be 95% accurateā¦
yet deliver 0% business value.
Whyā
Because data science metrics ā business metrics.
š 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 āgreat modelā is just a great illusion.
14 059
š 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
š Data Science Riddle
A feature has low importance but domain experts insist it matters. What do you do?
14 059
š Data Science Riddle
You need fast reads of small files. What storage options fits best?
14 059
š ļø Running Code in Jupyter Notebooks
Jupyter Notebooks let you write & run code interactively.
Hereās a quick guide to make your workflow smoother:
ā¶ļø Kernel & Code Cells
- Each notebook is tied to a single kernel (e.g. IPython).
- Code cells are where you write and execute code.
āØļø Useful Shortcuts
- Shift + Enter ā run current cell, move to next
- Alt + Enter ā run current cell, insert new one below
- Ctrl + Enter ā run current cell, stay in place
š Kernel Management
- Interrupt the kernel if code hangs.
- Restart kernel to reset memory & variables.
š„ļø Output Handling
- Results & errors appear directly under the cell.
- Long-running code outputs appear as theyāre generated.
- Large outputs can be scrolled or collapsed for clarity.
š” Pro Tip:
Always āRestart & Run Allā before sharing or saving a notebook.
This ensures reproducibility and clean results.
š Explore
14 059
š 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
š Data Science Riddle
You want to detect extreme values visually in one plot. Which one is best?
14 059
š Data Science Riddle
A query runs slowly due to large table scans. What's the most targeted fix?
