Data science/ML/AI
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 14 064 subscribers, ranking 9 052 in the Technologies & Applications category and 28 197 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 14 064 subscribers.
According to the latest data from 25 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 4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 8.86%. 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 1 246 views. Within the first day, a publication typically gains 300 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.
📝 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 26 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.
import numpy as np
x = np.array([
[10, 20, 30],
[40, 50, 60]
])
x + 5[[15, 25, 35], [45, 55, 65]]But where did the
5 go? You didn't write a loop. You didn't copy 5 six times.
This is broadcasting.
NumPy automatically treats the smaller object as if it were expanded to match the larger array's shape.
So conceptually:
[[10, 20, 30], [[5, 5, 5], [40, 50, 60]] + [5, 5, 5]]Then it adds them element by element. And it works with arrays too:
x + np.array([1, 2, 3])[[10, 20, 30], [[1, 2, 3], [40, 50, 60]] + [1, 2, 3]]Result:
[[11, 22, 33], [41, 52, 63]]The important part: Broadcasting doesn't literally copy the data into a bigger array. NumPy applies the operation as if the smaller array had been expanded, while avoiding unnecessary copies. That's why this:
x + 5can work even though
x is 2D and 5 is just a scalar.
axis=0 and axis=1 Feel Backwards
You've probably seen:
np.mean(X, axis=0)"Why does axis 0 mean columns?"Think about what NumPy is doing. 📌
axis=0 means:
Collapse the rows.So you move down each column and get one value per column. 📌
axis=1 means:
Collapse the columns.So you move across each row and get one value per row. For:
1 2 3 4 5 6
np.mean(X, axis=0)[2.5, 3.5, 4.5]On the other hand:
np.mean(X, axis=1)[2, 5]Don't memorize the numbers. Just Remember: The axis you specify is the dimension that gets collapsed. #NumPy
