Python

Powerful and Flexible Programming Language

Python, with its simple syntax and powerful libraries, is one of the most preferred programming languages in data science, web development, artificial intelligence, and automation. Easy to learn, effective to use.

Data Science Web Development Artificial Intelligence Automation

History of Python

Python's development journey from Guido van Rossum to the present day

1989 - Beginning

Guido van Rossum began developing Python. Inspired by the ABC language, he aimed to create a simpler and more readable language.

1991 - First Version

Python 0.9.0 was released. Basic features such as classes, exception handling, and functions were added.

2000 - Python 2.0

List comprehensions, garbage collection, and Unicode support were added. Python became more powerful.

2008 - Python 3.0

Python 3.0 was released with major changes. Backward compatibility was broken, but the language became more consistent.

2020 - End of Python 2

Python 2 support ended. The entire community completed the transition to Python 3, and the modern Python era began.

Key Features

Core features and advantages that make Python powerful

Simple Syntax

Python's readable and understandable syntax makes learning and development easier.

def hello(name):

return f"Hello, {name}!"

print(hello("World"))

Rich Library Ecosystem

Powerful libraries like NumPy, Pandas, and Django enable development of all kinds of projects.

import pandas as pd

import numpy as np

df = pd.DataFrame(data)

Platform Independence

Works seamlessly on all operating systems like Windows, macOS, and Linux.

import os

import platform

print(platform.system())

Object-Oriented Programming

Strong OOP support makes it easy to organize large and complex projects.

class Car:

def __init__(self, brand):

self.brand = brand

Rapid Development

Dynamic typing and interactive development environment make prototyping very fast.

# List comprehension

squares = [x**2 for x in range(10)]

Strong Community

Active and helpful Python community supports the learning process.

import this

# Python's philosophy

# "Beautiful is better than ugly"

Frameworks and Libraries

Most popular tools in the Python ecosystem

Django

Web Framework

Powerful framework designed with a "batteries included" philosophy for rapid web development.

Web Development Admin Panel ORM

from django.http import HttpResponse

def index(request):

return HttpResponse("Hello Django!")

Flask

Micro Framework

Minimalist and flexible web framework. Ideal for small projects, scalable to larger ones.

API Development Microservices Prototypes

from flask import Flask

app = Flask(__name__)

@app.route('/')

def hello(): return 'Hello!'

NumPy

Scientific Computing

Fundamental library for multidimensional arrays and mathematical functions.

Data Science Mathematics Arrays

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

print(arr.mean())

Pandas

Data Analysis

Library providing powerful tools for data manipulation and analysis.

Data Analysis CSV/Excel DataFrame

import pandas as pd

df = pd.read_csv('data.csv')

print(df.head())

TensorFlow

Machine Learning

Machine learning and artificial intelligence library developed by Google.

Artificial Intelligence Deep Learning Neural Networks

import tensorflow as tf

model = tf.keras.Sequential()

model.add(tf.keras.layers.Dense(64))

FastAPI

Modern API Framework

Framework designed for developing high-performance, modern Python APIs.

REST API Async OpenAPI

from fastapi import FastAPI

app = FastAPI()

@app.get("/")

def read_root(): return {"message": "Hello"}

Project Examples

Python projects at different difficulty levels

Calculator

Beginner

Simple calculator application that can perform basic mathematical operations.

Tkinter GUI

Web Scraper

Intermediate

Automated scraper application that extracts and analyzes data from websites.

BeautifulSoup Requests Pandas

ML Model

Advanced

Application that makes predictions and trains models using machine learning.

Scikit-learn NumPy Matplotlib

REST API

Intermediate

Modern and fast REST API application developed with FastAPI.

FastAPI SQLAlchemy PostgreSQL

Data Dashboard

Intermediate

Data visualization dashboard with interactive charts and analyses.

Streamlit Plotly Pandas

Test Automation

Advanced

Automated test scenarios for web applications using Selenium.

Selenium Pytest WebDriver

Career Opportunities

Career paths and opportunities opened by Python

Data Scientist

Analyzing large data sets to provide valuable insights to businesses.

• Pandas, NumPy, Matplotlib

• Statistics and mathematics

• SQL and databases

Backend Developer

Developing server-side of web applications and designing APIs.

• Django, Flask, FastAPI

• RESTful API design

• Database management

ML Engineer

Developing machine learning models and producing artificial intelligence solutions.

• TensorFlow, PyTorch

• Scikit-learn, Keras

• Deep learning algorithms

DevOps Engineer

Automating software development processes and managing infrastructure.

• Docker, Kubernetes

• CI/CD pipelines

• Cloud platforms