Why Python?

Python is one of the most popular programming languages in data science due to its simplicity, readability, and extensive libraries. It is widely used for data manipulation, analysis, and visualization.

Key Python Libraries for Data Science

NumPy:

Provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

Pandas:

Offers data structures and functions for data manipulation and analysis, making it easy to handle large datasets.

Matplotlib:

A plotting library for creating static, animated, and interactive visualizations.

Scikit-Learn:

A machine learning library that provides simple and efficient tools for data mining and data analysis.

Seaborn:

A statistical data visualization library based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics.

Basic Python Operations

Reading Data:

Importing data from various sources like CSV files using Pandas.

Data Manipulation:

Cleaning and transforming data using Pandas.

Data Visualization:

Creating plots and charts using Matplotlib and Seaborn.

Machine Learning:

Building and evaluating models using Scikit-Learn.

Example

Using Pandas, you can load a CSV file into a DataFrame, clean the data by removing missing values, and then create a bar chart using Matplotlib to visualize the distribution of a specific column.

Activity

Write a simple Python script to load a CSV file into a Pandas DataFrame, print the first few rows, and create a basic plot using Matplotlib.

Quiz