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 829 Technologies & Applications4 741...

📈 Analytical overview of Telegram channel Data Analytics

Channel Data Analytics (@dataanalyticsx) in the English language segment is an active participant. Currently, the community unites 28 918 subscribers, ranking 4 741 in the Technologies & Applications category and 22 829 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 28 918 subscribers.

According to the latest data from 10 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 490 over the last 30 days and by 16 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.41%. Within the first 24 hours after publication, content typically collects 1.27% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 275 views. Within the first day, a publication typically gains 368 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.

📝 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 11 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.

28 918

Subscribers

+1624 hours

+677 days

+49030 days

1 275

Post views

~ 36824 hours

~ 47448 hours

4.41%

Engagement rate

~ 2

Posts per day

Ads index

beta

Posts Archive

28 920

Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026. Sign up for FREE and save your seat here: luma.com/qgz4g4r7 Why should you join? Many people interested in AI Engineering are asking the same questions: ❓ Where do I start? 🤔 Do I need deep math first? 🧠 Should I focus on ML, LLMs, RAG, or AI agents? 🧭 How do I avoid wasting time learning the wrong things? 🚀 How do I go from learning to becoming hireable? If you’re interested in AI Engineering but unsure how to approach it, this livestream is for you. What you’ll learn ✦ What AI Engineering really is ✦ Where beginners should start ✦ What skills and topics actually matter ✦ Common mistakes to avoid ✦ Self-study vs bootcamp vs MSc ✦ How to think about becoming hireable in AI ✦ Practical advice from someone already working in the field Sign up for FREE and save your seat: luma.com/qgz4g4r7

28 920

Repost from Machine Learning with Python

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28 920

Repost from Machine Learning with Python

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28 920

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28 920

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28 920

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