📄️ Introduction to Data Handling with Python
Data handling is one of the most crucial skills in the realm of Data Science and Machine Learning. Python, with its powerful libraries and easy-to-understand syntax, makes data handling a seamless task. In this tutorial, we will learn the basics of data handling using Python.
📄️ Using pandas for Data Analysis
Pandas is a powerful data analysis tool built on top of the Python programming language. It provides data scientists and analysts with high-performance, easy-to-use data structures, and data analysis tools. The name 'Pandas' is derived from the term 'Panel Data', an econometrics term for datasets that include observations over multiple time periods for the same individuals.
📄️ Data Visualization with matplotlib and seaborn
Data visualization is an essential part of data analysis. It allows us to visually assess the data, identify patterns, outliers and relationships between variables. Python offers several libraries for data visualization, but we will focus on two of the most powerful and popular ones: Matplotlib and Seaborn.
📄️ Working with CSV, JSON, and XML Data in Python
In this tutorial, we will be covering how to work with CSV, JSON, and XML data in Python. These are common data formats that you will encounter when working with data in Python. By the end of this guide, you should be able to read, write, and manipulate data in these formats.
📄️ Basics of Web Scraping with Python
Introduction