This repo contains the 42 School Python "Piscine" (aka Bootcamp) for Data Science. These exercises are designed to help you understand the basics of Python and how to use it for data science.
Each exercise comes with its own tester (if applicable) and a README.md to help you understand the scope of the exercise, what I learned and my approach to solving them.
The exercises are divided into 4 main "days":
This day is an introduction to Python and its basic syntax. It covers the following topics:
- Variables
- Data Types
- Operators
- Control Structures
- Functions
- Generators
- Modules
- Libraries
- Virtual Environments
- Pip
This day is an introduction to Arrays and how to use them in Python. It covers the following topics:
- Lists
- List Comprehensions
- NumPy Arrays
- Multiple dimensions arrays
- Slicing
- Image Processing with NumPy, Matplotlib and PIL
This day is an introduction to DataFrames and how to use them in Python. It covers the following topics:
- Pandas DataFrames
- Data Cleaning
- Data Manipulation
- Data Visualization
- Plots with Matplotlib and Seaborn
This day is an introduction to Object Oriented Programming in Python. It covers the following topics:
- Classes
- Inheritance
- Encapsulation
- Polymorphism
- Magic Methods
- Decorators
- Abstract Classes
- Special Methods (Dunder Methods)
This day is an introduction to Data Oriented Design in Python. It covers the following topics:
- Nested Functions
- Wrapper Functions
- Data Classes
- Immutable Data Structures