This blog entry includes material that I am using to learn about Jupyter Notebooks in VSCode
This entry assumes that you already know how to work with Git and GitHub, and also already have set up VSCode, Python, and VSCode extensions such as Python and Jupyter.
References
- Titanic Doom Predictor GitRepo https://github.com/claudiaregio/data-science
- Documentation from VS Code doc site https://code.visualstudio.com/docs/datascience/jupyter-notebooks
What are VSCode, Anaconda, and Jupyter?
What is VS Code?
https://code.visualstudio.com/
VS Code (short for Visual Studio Code) is a free, open-source code editor developed by Microsoft that is widely used by developers for writing, testing, and debugging code. It is a lightweight and customizable editor that supports a wide range of programming languages and provides many features to enhance productivity, including syntax highlighting, code completion, debugging, and version control integration.
VS Code is designed to work with a variety of programming languages and platforms, and it includes many extensions that can be installed to add support for additional languages, frameworks, and tools. It also includes a built-in terminal and task runner, making it easy to run and test code directly from the editor.
Overall, VS Code is a popular choice for developers due to its versatility, ease of use, and extensive features, and it has a large and active community of users who contribute to its development and maintenance.
What is Anaconda?
Anaconda is a popular open-source distribution of the Python and R programming languages for data science, machine learning, and scientific computing. It provides a comprehensive set of tools and libraries for data analysis, including data visualization, data manipulation, and numerical computing.
The Anaconda distribution includes over 250 packages, including popular packages such as NumPy, SciPy, Pandas, and Matplotlib, as well as tools for managing package dependencies and virtual environments. It also includes the Conda package manager, which simplifies the process of installing, updating, and removing packages.
One of the key advantages of Anaconda is that it is designed to work across multiple platforms, including Windows, macOS, and Linux, and it provides a consistent environment that makes it easy to reproduce analyses across different machines.
Anaconda is widely used in industry, academia, and research, and it is considered a standard tool for data science and scientific computing. The distribution is available for free and can be downloaded from the official Anaconda website.
What is Jupyter?
https://jupyter.org/
Jupyter is an open-source web-based interactive computing platform that allows users to create and share documents containing live code, equations, visualizations, and narrative text. The name “Jupyter” is a combination of the three programming languages it was designed to support: Julia, Python, and R.
Jupyter provides a web-based interface for creating and editing documents, which are called notebooks. Notebooks can include code written in multiple programming languages, as well as formatted text and images. The code in a notebook can be executed interactively, allowing users to test and refine their code and see the results immediately. Jupyter notebooks can also be used for data exploration, visualization, and modeling, making it a popular tool for data science and scientific computing.
Jupyter supports a wide range of programming languages, including Python, R, Julia, and many others, and it has a large and active community of contributors who create and maintain extensions and plugins to add additional functionality.
Jupyter notebooks can be run locally on a user’s computer or on a remote server, and they can be shared with others through online hosting services like GitHub, or as standalone files that can be opened and run by anyone with the Jupyter environment installed.
Step 1: Set up VSCode for Jupyter Notebooks
Step 2 Walk Through the Titanic Doom Prediction
- Titanic Doom Predictor GitRepo https://github.com/claudiaregio/data-science
