This document introduces Python as a programming language for data science and AI, highlighting its community support, rich ecosystem, and powerful libraries for data analysis, machine learning, and deep learning.
This document covers Python implementations (CPython, PyPy, Jython) development environments, and IDE comparisons. It provides guidance on choosing and setting up the right tools for Python development and data science work.
This document provides a comprehensive introduction to Jupyter, a freely available web application for interactive computing. It covers Jupyter's key features, advantages for data science, and practical guidance on operating notebooks, including cell management, working with multiple notebooks presenting results, and managing sessions.
This document introduces Python data types, including integers, floats strings, booleans, and typecasting. It explains how Python represents and converts data types, with practical examples and key concepts for beginners.
This document explains Python expressions and variables, including arithmetic operations, assignment, variable naming, and practical usage for storing and manipulating values.
This document explains Python strings, including indexing, slicing concatenation, replication, immutability, escape sequences, and string methods for manipulating character data.
This document provides a comprehensive overview of Python data types operations, variables, string manipulation, and core programming concepts for data science applications.