Data Analytics

Open in Telegram

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

Network:Machine Learning with Python Russia22 694 Technologies & Applications4 713...

📈 Analytical overview of Telegram channel Data Analytics

Channel Data Analytics (@dataanalyticsx) in the English language segment is an active participant. Currently, the community unites 29 014 subscribers, ranking 4 713 in the Technologies & Applications category and 22 694 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 29 014 subscribers.

According to the latest data from 19 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 510 over the last 30 days and by -3 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 2.84%. Within the first 24 hours after publication, content typically collects 1.21% reactions from the total number of subscribers.
Post reach: On average, each post receives 824 views. Within the first day, a publication typically gains 350 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
Thematic interests: Content is focused on key topics such as sellerflash, buybox, buyer, chaos, effortless.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 20 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

29 014

Subscribers

-324 hours

+677 days

+51030 days

824

Post views

~ 35024 hours

~ 48748 hours

2.84%

Engagement rate

~ 3

Posts per day

Ads index

beta

Posts Archive

29 013

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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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29 013

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

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29 013

Repost from Machine Learning with Python

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