Data Analytics

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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho

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Сеть:Machine Learning with Python Россия22 829 Технологии и приложения4 741...

📈 Аналитический обзор Telegram-канала Data Analytics

Канал Data Analytics (@dataanalyticsx) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 28 918 подписчиков, занимая 4 741 место в категории Технологии и приложения и 22 829 место в регионе Россия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 28 918 подписчиков.

Согласно последним данным от 10 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 490, а за последние 24 часа — 16, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 4.41%. В первые 24 часа после публикации контент обычно набирает 1.27% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 1 275 просмотров. В течение первых суток публикация набирает 368 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 2.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как sellerflash, buybox, buyer, chaos, effortless.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho”

Благодаря высокой частоте обновлений (последние данные получены 11 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

28 918

Подписчики

+1624 часа

+677 дней

+49030 день

1 275

Просмотры поста

~ 36824 часа

~ 47448 часов

4.41%

Коэффициент вовлеченности

~ 2

Постов в день

Ads index

beta

Архив постов

28 916

Repost from Machine Learning with Python

A new collection of free courses has been added: 🔗 https://github.com/dair-ai/ML-Course-Notes Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. 📚 Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. 🧠 What's inside: • Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others • A table with lectures, descriptions, videos, notes, and authors • Links to the original lectures and accompanying notes • WIP markers for incomplete materials • Instructions for contributors on adding and improving notes The idea was appreciated. 👍 Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. 🗺️ #MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

28 916

Repost from Machine Learning with Python

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28 916

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Learning AI doesn’t need another random tutorial rabbit hole. 🚫🐇 AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. 📚🤖 It helps you make your own learning path by collecting the materials the author used while learning AI, with quick-start recommendations up front and sections you can scan by resource type. 🗺️✨ Key features: 🌟 • TL;DR starting path – points to one book, one LLM video, and the Hugging Face Agents Course 📖🎥 • Books section – lists AI/ML/DL books with short notes on where each one helps 📚 • Courses and videos – collects practical lectures, tutorials, and talks from sources like MIT, NVIDIA, Hugging Face, Karpathy, and 3Blue1Brown 🎓 • Tools and libraries map – groups frameworks, platforms, visualization tools, and Python libraries for builders 🛠️ • Broader study material – includes models, model hubs, articles, papers, datasets, and AI notes 📄 Free public GitHub repo. 🆓 https://github.com/ArturoNereu/AI-Study-Group #AI #MachineLearning #DeepLearning #GitHub #StudyGroup #TechLearning ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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28 916

Repost from Learn Python Coding

Data validation with Pydantic! 🐍✨ In the early stages of development, data validation usually doesn't cause problems. In many Python projects, validation initially looks simple:

if not isinstance(age, int):
    raise ValueError("age must be an int")

But then come email, JSON from APIs, query parameters, nested objects, configs, nullable fields, and type conversion. At some point, the code turns into a set of if/else and manual checks. For such tasks, Pydantic is often used. Installation:

pip install pydantic
pip install "pydantic[email]"

Create a model:

from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

Now the data is validated automatically:

user = User(
    name="Alex",
    age="30"
)

print(user.age)
print(type(user.age))

The result: 30 <class 'int'> Pydantic will automatically convert the string "30" to an int. If you pass an incorrect value, you'll get a ValidationError:

User(
    name="Alex",
    age="test"
)

This is especially convenient when working with APIs, JSON, query parameters, and incoming data from outside. A common production case is checking email:

from pydantic import BaseModel, EmailStr

class User(BaseModel):
    email: EmailStr

User(email="alex@test.com")

If the email is invalid, Pydantic will throw a ValidationError. You can set default values:

from pydantic import BaseModel

class Config(BaseModel):
    host: str = "localhost"
    port: int = 5432

And allow None:

from pydantic import BaseModel

class User(BaseModel):
    nickname: str | None = None

This field becomes optional. A practical example is processing an API response:

from pydantic import BaseModel

class Product(BaseModel):
    id: int
    title: str
    price: float

data = {
    "id": "1",
    "title": "Keyboard",
    "price": "99.5"
}

product = Product(**data)

print(product)

The types will be automatically converted. For nested model structures, you can combine:

from pydantic import BaseModel

class Address(BaseModel):
    city: str
    zip_code: str

class User(BaseModel):
    name: str
    address: Address

user = User(
    name="Alex",
    address={
        "city": "Berlin",
        "zip_code": "10115"
    }
)

print(user)

The nested object will also be validated. Serialization in Pydantic v2:

print(user.model_dump())
print(user.model_dump_json())

Pydantic is actively used in FastAPI, ETL, microservices, data pipelines, and API clients. For working with environment variables in Pydantic v2, a separate package is usually used:

pip install pydantic-settings

It's important to understand: Pydantic is not an ORM and does not replace business logic. Its task is to validate data, convert types, and describe schemas. 🔥 Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable. #Python #Pydantic #DataValidation #FastAPI #Coding #DevOps ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

28 916

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