I often see people say that it's impossible to enter the IT field without expensive courses.

However, there's a huge amount of high-quality materials available for free:

📚 Computer Science
https://github.com/ossu/computer-science

📚 Data Structures & Algorithms
https://github.com/jwasham/coding-interview-university

📚 System Design
https://github.com/donnemartin/system-design-primer

📚 Web Development
https://github.com/TheOdinProject/curriculum

📚 Frontend / Backend / DevOps / Cloud
https://github.com/kamranahmedse/developer-roadmap

📚 Data Engineering
https://github.com/DataTalksClub/data-engineering-zoomcamp

📚 Machine Learning & AI
https://github.com/microsoft/ML-For-Beginners

📚 MLOps
https://github.com/DataTalksClub/mlops-zoomcamp

📚 Cybersecurity
https://github.com/OWASP/CheatSheetSeries

📚 Linux
https://github.com/trimstray/the-book-of-secret-knowledge

📚 Free Programming Books
https://github.com/EbookFoundation/free-programming-books

If you have internet and a bit of free time, you can learn computer science, algorithms, system design, DevOps, clouds, security, and machine learning for free.

The problem now isn't a lack of information. The problem is regularly opening these repositories and actually working on them.

#FreeLearning #ITCareer #CodingResources #TechEducation #OpenSource #DevCommunity

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How to create an object from a dictionary with dot access — without classes and dataclasses?

When you're working with JSON, configurations, or APIs, constant access via dict['key'] clutters the code and worsens readability:

data = {"host": "localhost", "port": 5432}
data["host"]

SimpleNamespace gives the same result, but with dot access:

cfg = SimpleNamespace(**data)
print(cfg.host)

In this case, the object remains dynamic, and you can add fields:

cfg.debug = True

However, the keys must be valid attribute names, and this only works for flat dictionaries (nesting is not converted).

🔥Convenient for prototyping, testing, and simple data.

#Python #DataStructures #SimpleNamespace #CodingTips #DevTools #Programming

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What are lambda functions in Python?

Lambda functions — are anonymous functions, which are created using the lambda keyword. They can accept any number of arguments, but contain only one expression, the result of which is automatically returned.

They are most often used for short operations, for example during sorting, filtering or data processing.

# Example:
sorted_data = sorted(data, key=lambda x: x[1])
filtered_data = list(filter(lambda x: x > 0, numbers))

#Python #LambdaFunctions #DataScience #Coding #Programming #TechTips

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😍 learnxinyminutes — a brief and informative reference guide!

A convenient cheat sheet that allows you to quickly get acquainted with the basics of Python or refresh your knowledge before work. On one page, the key constructs of the language are collected: variables, functions, classes, collections, exception handling, etc. The format is as compact as possible, with a minimum of theory and a maximum of practical examples.

📌 I'll leave a link: learnxinyminutes.com

#Python #LearnToCode #Programming #CheatSheet #WebDev #CodingLife

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👍 Fluent Python — practical examples from one of the best Python books!

Here, the language's capabilities are explained, which many only know superficially. The material focuses not on basic syntax, but on a deep understanding and proper use of its capabilities in real projects. There are many examples of working with objects, collections, functions, decorators, generators, async code, and Python's internal logic.

I'll leave a link: https://github.com/fluentpython/example-code-2e

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What is the difference between copy() and deepcopy() in Python?

copy() creates a shallow copy of the object: nested elements remain shared between both copies. In contrast, deepcopy() recursively copies all nested objects, making the new structure completely independent. This is particularly important when working with mutable data

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The guide Path to Senior Engineer Handbook has gathered resources for developers who want to advance to the level of Senior Engineer. 🚀

Inside: 📚

More than 50 newsletters on professional growth, system design, leadership, and web development. 📈

A selection of books on communication, technical writing, and building working relationships. 🤝

Selected YouTube channels, podcasts, and professional communities. 🎧

Courses, scientific articles, and educational platforms for a deeper study of topics. 🎓

A good starting point for those who want to improve not only their technical skills, but also their architectural thinking, communication, and leadership competencies. 💡

Link: https://github.com/jordan-cutler/path-to-senior-engineer-handbook?utm_source=opensourceprojects.dev&ref=opensourceprojects.dev

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When applying multiple filters to a series in Pandas, it's better to break the condition into several lines:

s = pd.Series([10, 15, 20, 25, 30])

s.loc[
    (s > 20) &
    (s % 2 == 1)
]

Such code is easier to read, write, and maintain. 🛠️✨

As a result, the value:
25
will be selected,
since it is both greater than 20 and an odd number. 🎯

#Pandas #Python #DataScience #Coding #Programming #DataAnalysis

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# How to Implement a Decorator with Parameters Using a Class in Python

You can create a parameterized decorator in Python by using a class that implements the __call__ method. The class acts as the decorator factory, where the __init__ method stores the parameters, call__call__ method applies the decorator logic to the target function.

## Example Implementation

class my_decorator:
    def __init__(self, param):
        self.param = param

    def __call__(self, func):
        def wrapper(*args, **kwargs):
            print(f"Decorator parameter: {self.param}")
            return func(*args, **kwargs)
        return wrapper

@my_decorator("Hello")
def say_hello(name):
    print(f"Hello, {name}!")

say_hello("World")

## Explanation

1. Class Definition: Define a class that will serve as the decoratorinit.
2. **__init__**: Store the parameters passed to the decoracall.
3. **__call__**: Make the instance callable. This method receives the function being decorated and returns the wrappWrapperrapper**: The inner function executes the actual decorator logic before or after calling the original function.

Cheat sheet on Python Data Types 🐍

Python Data Types — the main data types in Python.

🔢 Numbers — numerical types (int, float, complex)
⚖️ Bool — logical values (True, False)
📝 String — text strings
📋 List — mutable ordered collections
🔒 Tuple — immutable ordered collections
🎲 Set — unique unordered elements
🗂️ Dict — collections of "key-value" pairs.

Helps to quickly orient yourself in data types and choose the appropriate structure for storing information 💡

#Python #DataScience #Coding #Programming #Tech #Learning

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Python is like New Year. 🎉 You can pass a tuple to startswith and endswith. 💻🐍

#Python #Programming #TechTips #Coding #DevCommunity #LearnToCode

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Variables in Python are stickers, not boxes.

A variable doesn't store a value, but points to it. If you change a list, both variables will see the change. This makes sense with stickers. But with boxes, it's magic: two boxes mysteriously change simultaneously.

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What useful functions are there in the functools module?

Among the most popular ones: lru_cache for caching results, partial for partial application of arguments, wraps for preserving metadata in decorators and cmp_to_key for transforming a comparison function into a sorting key 🐍✨

# functools module usage
from functools import lru_cache, partial, wraps, cmp_to_key

#Python #functools #coding #programming #softwaredev #tech

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