Quick pocket size Revision Guide for Data Science field-Part1

Part 1: Data Science Libraries and Techniques

Pandas: Manages data with DataFrames and Series; supports loading, cleaning, manipulating, and exporting data.
NumPy: Performs numerical operations and linear algebra with arrays.
Scikit-learn: Facilitates machine learning with model building, preprocessing, and evaluation.
Seaborn & Matplotlib: Visualize data; Seaborn for statistical plots, Matplotlib for basic and custom charts.
Plotly & D3.js: Create interactive and advanced web-based visualizations.