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
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Data science/ML/AI analitikasi
Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 14 064 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 052-o'rinni va Hindiston mintaqasida 28 197-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 14 064 obunachiga ega bo‘ldi.
25 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 97 ga, so‘nggi 24 soatda esa 4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 8.86% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.13% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 246 marta ko‘riladi; birinchi sutkada odatda 300 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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...”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 26 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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| 01 Oktabr | +13 |
Kanal postlari
| 2 | SQL Basic to Advanced | 508 |
| 3 |  10 Modern AI Agents Frameworks and Tools | 586 |
| 4 | Data Science Interview Questions.pdf | 787 |
| 5 | 7 baby steps to start with machine learning:
1. Start with Python
2. Learn to use Google Colab
3. Take a Pandas tutorial
4. Then a Seaborn tutorial
5. Decision Trees is a good first algorithm
6. Finish Kaggle's "Intro to Machine Learning"
7. Solve the Titanic challenge | 999 |
| 6 |  Power BI Mind Map | 1 104 |
| 7 | SQL Notes for Begineers.pdf | 1 079 |
| 8 |  Computer Vision Explained | 1 267 |
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| 10 |  Roadmap to Learn SQL | 1 313 |
| 11 | 🧮 Why does NumPy let you add a number to an entire matrix?
Look at this:
import numpy as np
x = np.array([
[10, 20, 30],
[40, 50, 60]
])
x + 5
You get:
[[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])
Conceptually:
[[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 + 5
can work even though x is 2D and 5 is just a scalar. | 1 229 |
| 12 | Intro to Datascience with Python.pdf | 1 397 |
| 13 |  8 RAG architectures for AI Engineers, visually explained: | 1 238 |
| 14 | A comprehensive summary of the Seaborn Library.pdf | 1 334 |
| 15 |  🧮 NumPy: Why axis=0 and axis=1 Feel Backwards
You've probably seen:
np.mean(X, axis=0)
and wondered:
"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)
gives:
[2.5, 3.5, 4.5]
On the other hand:
np.mean(X, axis=1)
gives:
[2, 5]
Don't memorize the numbers.
Just Remember: The axis you specify is the dimension that gets collapsed.
#NumPy | 1 389 |
| 16 | Data Science Formulas Cheat Sheet.pdf | 1 214 |
| 17 |  📊 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 | 1 381 |
| 18 | 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 👈 | 1 198 |
| 19 |  Tools vs MCP vs Skills: 3 Layers That Power Production AI Agents | 1 208 |
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