🚀 Master Data Science & Programming!

Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!


🔰 Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.iss.one/CodeProgrammer

🔖 Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.iss.one/DataScienceM

🧠 Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.iss.one/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ

💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1

🧑‍🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC

😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT

💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab

🖊 Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.iss.one/DataScienceN

📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.iss.one/DataScienceV

📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.iss.one/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY

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Admin: @HusseinSheikho

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer

🧠 How to Slow Down Python to a Snail's Pace?

Do you dream of making your script run in slow motion? Use these proven methods to turn fast code into annoyingly slow crap.

Don't do this (the obvious and boring way)

import time

for i in range(10):
    time.sleep(1)  # Just wait a second on each iteration
    print(i)

The Problem:
It's too predictable. Any stupid reviewer will immediately notice it and delete it.

✔️ The Right Way (Creative Slowing Down)

import random
import threading
import sys

class GlobalSlowdown:
    def __init__(self):
        self.lock = threading.Lock()
    def heavy_calc(self, x):
        with self.lock:  # Imitate the GIL squared
            return sum(i * 0.000001 for i in range(int(x * 10000)))

def main():
    slowdown = GlobalSlowdown()
    data = list(range(1000))
    # Process each element in a random thread with a delay
    threads = []
    for item in data:
        t = threading.Thread(target=lambda: slowdown.heavy_calc(random.random()))
        t.start()
        threads.append(t)
        if random.choice([True, False]):
            sys.stdout.flush()  # A useless call for show
    for t in threads:
        t.join()  # Wait for everything

if __name__ == '__main__':
    main()

How it works:
We create a bunch of threads for a trivial operation. The global lock ensures that they won't work in parallel, but sequentially, but with the overhead of context switching. The perfect storm of inefficiency.

Let's complicate it: recursion + cache misses:

from functools import lru_cache

@lru_cache(maxsize=2)  # Cache for only 2 elements
def fib(n):
    if n <= 1:
        return n
    # Call it twice with the same arguments to hit the cache misses
    return fib(n-1) + fib(n-2)

# The calculation will take forever
print(fib(50))

The cache is too small to help. The algorithm slows down exponentially, wasting time on constant cache misses.

Pro Tip: Killing the Garbage Collector:

🔵Create cyclic references in huge object graphs

🔵Disable GC: gc.disable()

🔵Use globals() to store everything so the memory never gets freed

Important:
These tricks won't just slow down execution, they'll make the code completely unsupported. Your colleague will curse the day he decided to debug this.

👩‍💻 https://t.iss.one/DataScience4