Available now! Telegram Research 2025 โ the year's key insights 
Data Analytics
Open in Telegram
Dive into the world of Data Analytics โ uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
Show more๐ Analytical overview of Telegram channel Data Analytics
Channel Data Analytics (@dataanalyticsx) in the English language segment is an active participant. Currently, the community unites 28 861 subscribers, ranking 4 781 in the Technologies & Applications category and 22 925 in the Russia region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 28 861 subscribers.
According to the latest data from 03 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 565 over the last 30 days and by 25 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.46%. Within the first 24 hours after publication, content typically collects 1.99% reactions from the total number of subscribers.
- Post reach: On average, each post receives 998 views. Within the first day, a publication typically gains 575 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- Thematic interests: Content is focused on key topics such as sellerflash, buybox, buyer, chaos, effortless.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โDive into the world of Data Analytics โ uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikhoโ
Thanks to the high frequency of updates (latest data received on 04 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.
28 861
Subscribers
+2524 hours
+2067 days
+56530 days
Data loading in progress...
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| Date | Subscriber Growth | Mentions | Channels | |
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| 01 June | +40 |
Channel Posts
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| 20 | Data validation with Pydantic! ๐โจ
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.
๐ฅ Pydantic significantly reduces the amount of manual data validation and makes processing incoming structures more predictable.
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