This repository contains the projects for the Python for Data Science Piscine. Each module introduces new concepts related to Python programming, building progressively to cover various topics essential for data science. Complete each project in the specified order to validate the piscine and develop a strong foundation in Python. 🚀
- Summary: Learn the basic syntax, variables, and simple operations in Python.
- Concepts Covered:
- Python syntax
- Variables and data types
- Summary: Work with arrays, their manipulations, and gain hands-on experience in image processing.
- Concepts Covered:
- List data structure in Python
- Accessing, modifying, and iterating through arrays
- Array slicing and indexing
- Introduction to NumPy for efficient array manipulation
- Libraries:
NumPy: Arrays, basic operationsPillow: Image manipulation
- Summary: Learn how to load, manipulate, and display data in a tabular format using Python.
- Concepts Covered:
- Introduction to Pandas for data manipulation
- Loading data from CSV files and other formats
- Dataframes and series
- Basic data cleaning: handling missing values, removing duplicates
- Sorting, filtering, and grouping data
- Libraries:
Pandas: Data manipulation and analysisMatplotlib: Plotting data (optional for basic visualization)
- Summary: Understand the principles of OOP, focusing on classes, inheritance, and other key OOP concepts.
- Concepts Covered:
- Introduction to Object-Oriented Programming
- Classes and objects
- Constructors, methods, and attributes
- Inheritance, polymorphism, and encapsulation
- Libraries:
- No external libraries required for this module, as the focus is on Python OOP concepts.
- Summary: Learn about different design structures in programming and improve your code organization.
- Concepts Covered:
- Introduction to Abstract Base Classes and Interfaces
- Best practices for writing clean and reusable code
- Libraries:
- No specific libraries required, but knowledge of Python’s abstract base classes and design patterns will be covered.