🚀 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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/DataScience4

🎯 PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ

💾 Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1

🧑‍🎓 Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC

😀 ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT

💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9

🐍 Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/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://news.1rj.ru/str/DataScienceN

📺 Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV

📈 Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX

🎧 Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53

⭐️ Research Papers
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY

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Admin: @HusseinSheikho

❔ Interview Question

How does the map() function work?

Answer:map() takes a function and an iterable object, and returns an iterator that sequentially yields the result of applying this function to each element. The map() itself does not create a list — it lazily forms values on demand. If a list is needed, the result can be wrapped in list().

tags:#interview

➡ @DataScienceQ

Checking Memory Usage in Python

psutil is imported, then through psutil.virtual_memory() memory data is obtained.

The function convert_bytes converts bytes to gigabytes.

Then the code calculates:

- total RAM
- available RAM
- used RAM
- percentage usage

And outputs this to the console.

import psutil

memory = psutil.virtual_memory()

def convert_bytes(size):
    # Convert bytes to GB
    gb = size / (1024 ** 3)
    return gb

total_gb = convert_bytes(memory.total)
available_gb = convert_bytes(memory.available)
used_gb = convert_bytes(memory.used)

print(f"Total RAM: {total_gb:.3f} GB")
print(f"Available RAM: {available_gb:.3f} GB")
print(f"Used RAM: {used_gb:.3f} GB")
print(f"RAM Usage: {memory.percent}%")

Or simply press CTRL + ALT + DELETE and open Task Manager. It has worked since the days of Windows 95.

The RAM usage percentage loses its meaning if you have Chrome open — it will consume everything on its own 😄

👉  https://news.1rj.ru/str/DataScienceQ

❔ Interview Question

What can be a key in a dictionary?

Answer: Dictionaries in Python are hash tables. Instead of keys in the dictionary, hashes are used. Consequently, any hashable data type can be a key in the dictionary, which includes all immutable data types.

tags: #interview

➡ @DataScienceQ

❔ Question from the interview

Why does isinstance(True, int) return True?

Answer: In Python, bool is a subclass of int. True and False are instances of int with values 1 and 0, respectively.

tags: #interview

➡️ @DataScienceQ

❔ Python Quiz

❔ Question from the interview

What is Big O notation?

Answer: Big O notation is a way of describing how quickly the running time or memory consumption of an algorithm increases as the size of the input data increases. It shows the asymptotic complexity: the upper bound of the algorithm's behavior, without taking into account constants and minor details.

For example, O(n) grows linearly, O(n²) - quadratically, O(1) - does not depend on the size of the input.

Big O does not give exact figures, but allows you to compare algorithms in terms of their scalability.

tags: #interview

➡ @DataScienceQ ⭐️

❔ Interview question

What is __slots__?

Answer: By default, class instances store their attributes in the internal dictiondictct__. This is flexible, but it requires more memory and makes access to fields a bit slower, because the search is done in the dictionarslotsts__ allows you to fix a set of allowed attributes and abandon the usedictct__. Instead of a dictionary, Python allocates compact slots — thereby reducing the memory consumption for each object and speeding up access to attributes. This is especially important when millions of class instances are created in a program or the performance of data access is critical.

There is one restriction: it is not possible to add an attribute that is notslotsts__. To retain the ability to dynamically create fields, you can dictct__ to the list of slots.

tags: #interview

➡ @DataScienceQ

❔ Question from the interview

What is a message broker and which ones are typically used with Python?

Answer: A message broker is an intermediary component that accepts messages from one service and delivers them to another, allowing microservices and asynchronous tasks to interact without direct connection. It ensures reliable delivery, queues, routing, and scalability.

In Python projects, RabbitMQ, Apache Kafka, and Redis are often used as simple broker solutions (for example, in combination with Celery). The choice depends on the tasks: Kafka for stream processing, RabbitMQ for flexible routing, and Redis for simple queues.

tags: #interview

➡ @DataScienceQ