Overview
Python has become the de facto language for data science and machine learning due to its simplicity and the rich ecosystem of libraries.
Today, we'll focus on essential Python libraries for data manipulation and analysis: NumPy, Pandas, and Matplotlib.
Key Concepts
- NumPy arrays and operations
- Pandas DataFrames and Series
- Data loading and manipulation with Pandas
- Basic data visualization with Matplotlib
- Jupyter notebooks for interactive development
Practice Exercise
Exercise: Data Exploration with Pandas
Using the Titanic dataset:
- Load the dataset using Pandas
- Display basic information about the dataset (shape, data types, etc.)
- Calculate summary statistics for numerical columns
- Find the survival rate by gender
- Create a simple visualization showing age distribution of passengers
Resources
Kaggle Python Course
Main resource for today
Python Data Science Handbook
Comprehensive guide to Python for data science
Pandas Documentation
Official documentation for Pandas
NumPy Tutorial
W3Schools NumPy Tutorial
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