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

Сітка:Machine Learning with Python Росія22 836 Технології та додатки4 746...

📈 Аналітичний огляд Telegram-каналу Data Analytics

Канал Data Analytics (@dataanalyticsx) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 28 916 підписників, посідаючи 4 746 місце в категорії Технології та додатки та 22 836 місце у регіоні Росія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 28 916 підписників.

За останніми даними від 09 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 503, а за останні 24 години на 4, загальне охоплення залишається високим.

Статус верифікації: Не верифікований
Рівень залученості (ER): Середній показник залученості аудиторії становить 4.07%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.44% реакцій від загальної кількості підписників.
Охоплення публікацій: В середньому кожен допис отримує 1 175 переглядів. Протягом першої доби публікація в середньому набирає 417 переглядів.
Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 2.
Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як sellerflash, buybox, buyer, chaos, effortless.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho”

Завдяки високій частоті оновлень (останні дані отримано 10 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.

28 916

Підписники

+424 години

+767 днів

+50330 день

1 175

Перегляди допису

~ 41724 години

~ 54948 годин

4.07%

Коефіцієнт залучення

~ 2

Дописів на день

Ads index

beta

Архів дописів

28 916

Repost from Machine Learning with Python

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28 916

Repost from Machine Learning with Python

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Transformers & LLMs Cheatsheet.pdf1.41 MB

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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

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