Python and Development Environment
Get introduced to the basics of Python and its associated development environments.
Python
Python is a versatile, high-level, interpreted, and general-purpose programming language conceived by Guido van Rossum in 1991. One of its standout features is its adaptability, making it suitable for a wide array of applications. It’s not confined to specific tasks, making it highly sought after in scientific computing and machine learning. As an open-source language, Python welcomes contributions from a diverse community, allowing for constant development and refinement. Python is acclaimed for its readability and ease of use, making it an ideal choice for both beginners and seasoned developers.
Jupyter Notebook
Jupyter Notebook is an open-source web application tailored for crafting and sharing interactive computational documents. These documents, primarily written in Python, can seamlessly blend code, text, and visualizations to create dynamic and engaging notebooks. A key advantage of Python and Jupyter Notebook is their open-source nature, encouraging a collaborative community effort. Many individuals, rather than a singular entity, contribute code that enhances its functionality. While Python is our primary choice, alternatives like R or Matlab can also be employed to solve TSP. The choice is often a subject of debate, but Python stands out for its vast ecosystem and active, supportive community. Choosing Python for this course will be a rewarding decision, thanks to its versatility, community, and robust capabilities.
Anaconda
Choosing Anaconda is driven not only by the playful connection to the species but primarily because it stands as the world’s most widely adopted Python distribution platform, boasting over 20 million users worldwide according to Anaconda itself. Anaconda is an acclaimed free and open-source distribution providing Python language, particularly tailored for scientific computing. Its focus encompasses a spectrum of domains, including data science, machine learning applications, large-scale data processing, predictive analytics, and more. Aiming to streamline package management and deployment, Anaconda amalgamates over 1,500 open-source packages within its robust framework. Accessible across Windows, macOS, and Linux, it serves as an invaluable tool for data scientists, machine learning engineers, and professionals, enabling the development and deployment of Python-based applications. Furthermore, Anaconda equips users with tools to manage virtual environments effectively and create shareable, reproducible data science projects.
Beyond its convenience, Anaconda enjoys a track record of long-term commitment to supporting this open-source ecosystem. It comprises essential ...