Jupyter Notebooks Optional Scientific Computing Workbook

Leo Migdal

-Nov 17, 2025, 12:50 AM

jupyter notebooks optional scientific computing workbook

This material was included in last year’s module NSCI0010. Complete this material if you want to refresh your memory or if you didn’t take that module. After completing this worksheet, you will be able to: Open your NSCI0036 Cocalc project, then click on the Before_you_begin folder. The online platform we will be using, providing online access to virtual computers hosted in the cloud. You access Cocalc via your browser at www.cocalc.com.

Every student has a Cocalc account allowing access to a Cocalc project, which is a virtual computer including operating system (Linux) and software libraries. JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality. The Jupyter Notebook is the original web application for creating and sharing computational documents. It offers a simple, streamlined, document-centric experience.

Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. Notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. Your code can produce rich, interactive output: HTML, images, videos, LaTeX, and custom MIME types. This page provides zip folders containing the Jupyter notebooks of discipline-specific Try This! exercises and homework problems. All files – including the notebooks, images and datasets specific to the notebooks – are found in the related ZIP folder.

A description of the files in each folder is found in a README.md file in the respective folder. Sections 2.2.3 and 3.2.4 in the textbook describe how to use Jupyter notebooks. Operating system-specific advice for starting a Jupyter notebook is found here. See these instructions on opening ZIP files for Windows and Mac. On Linux, here are command-line instructions on handling ZIP files. The notebooks, and all accompanying files, are also available to run in-browser, via a cloud-computing environment.

Simply select your discipline – Biology, Chemistry, Physics – to be redirected to the relevant page. Homework problem solutions are available only to instructors, here. Visit Higher Education from Cambridge University Press for teaching and learning content. Jupyter Notebook is an open-source web application to record, create and share computational documents. It is an interactive computing platform and utilizes Python. Jupyter Notebook can be used to compute and analyze mathematics and physical sciences data.

It offers a simple, streamlined, document centric experience. Two common ways to install Jupyter notebooks Anaconda is an open-source software that includes Python and Jupyter notebooks, and many other scientific computing utilities. To install Anaconda, go to Anaconda and download the latest Python version on your computer. Once finished installing, open the Anaconda navigator. Click the 'launch' button on the Jupyter Notebooks section.

It would open another window in your browser such as: Please note that this tutorial requires the user to have a basic understanding of the options available in Jupyter. If you are not familiar with Jupyter, I recommend exploring other tutorials in section : Jupyter Lab is an interactive web-based tool that allows users to create and share documents that contain live code, equations, visualizations, and narrative text (e.g., documentation), offering benefits such as data exploration, reproducibility, and... In Jupyter notebook, users can leverage various Python libraries, including graphical ones, to analyze and visualize data all in one document, providing a powerful and efficient environment for Python-based developments. It offers a convenient way to organize and document a project, making it easier to share and collaborate with others.

Notebooks can be easily shared as a .ipynb file or hosted on online platforms (e.g., Google Colab ⤴), allowing collaborators to access and modify the same document in real-time, which streamlines collaboration and helps... YES, Jupyter is a powerful modern interactive development environment! Estimated time to complete: 60 to 90 minutes. Welcome to the online materials for this online course on programming in Python for mathematical computing (a.k.a. scientific computing). This first unit is an introduction to some basic features of the Python software that we will be using.

Specifically, we use Python 3, and even more specifically, Python version 3.9 or higher. We start directly with Jupyter notebooks via the tool JupyterLab as a way to use Python interactively like a scientific calculator, and aim to work with these notebooks as much as possible. Later, we will also learn the more advanced code development tools offered by the Integrated Development Environment Spyder. This supports both both interactive use of Python and also working with files of Python code: creating, opening and editing files, running and debugging code, and so on. It has more advanced tools for developing Python code than the Jupyter notebook system, so for more substantial programming tasks it can be better to develop code within Spyder — even if that code... Python is a popular programming language available on all major computer platforms, including macOS, Linux, and Windows.

It is a scripting language, which means that the moment the user presses the Return key or Run, the Python software interprets and runs the code. This is in contrast to a compiled language like C, where the code must first be translated into binary (i.e., machine language) before it can be run. On-the-fly interpretation makes Python quick to use and often provides the user with rapid results. This is ideal for scientific data analysis, where the user is routinely making changes to the processing and visualization of the data. Python is free, open-source software and is maintained by the non-profit Python Software Foundation. This is appealing for two major reasons.

The first is that it is widely, freely, and irrevocably available to anyone who wants to use it, regardless of budget. With proprietary software, which is more and more commonly offered under a subscription model, if a company stops offering or updating a software package, it may simply become unavailable, leaving users without the software... Second, it is open source, so anyone can inspect and modify the code. This allows anyone to review the code to ensure it does what it claims instead of relying on the assertions of the software distributor. Another reason to use Python over other options, free or otherwise, is the power and the community support available to Python users. Python is a common and popular programming language that has been applied to a wide variety of applications, including data analysis, visualization, machine learning, robotics, web scraping, 3D graphics, and more.

As a result, there is a large community built around Python that provides valuable support for those who need assistance. If you are stuck on a problem or have a question, a quick internet search will likely provide the answer. Common internet forums include stackexchange.com or stackoverflow.com among others. If you have a question or need help on something, you are probably not the first person to ask that question. Along with Python, this book uses the IPython environment and Jupyter notebooks as a medium for running and sharing Python code. More details are given below on Jupyter notebooks, but for now, know that they provide interactive environments ideal for scientific computing.

In addition, we will use a variety of free, open-source libraries to provide collections of useful functions for scientific data processing, analysis, and visualization. Think of a library as an add-on or tool pack for Python, and there are many to choose from. Software installation instructions may have changed since these instructions were written and may vary depending on the operating system. Jupyter is an Integrated Development Environment (IDE) that provides an interactive and collaborative environment for scientific computing. This interactive coding environment allows for immediate execution and visualization of code, facilitating on-the-fly data analysis and visualization. It supports over 40 programming languages (including Python, R, Julia, Java, and Scala) and seamlessly integrates with popular data science libraries.

Its collaborative work environment simplifies sharing of results and workflows, and integrates well with version control systems like Git. With its extensibility and modularity, users can customize their workspace to fit specific needs. Finally, it offers rich text and Markdown support (for user-friendly project description and code documentation) and provides a suite of integrated tools (e.g., file browser or debugger), making it an all-in-one platform for data... The Jupyter Project offers a suite of open-source software tools, including: Both JupyterLab and JupyterNotebook tools are currently in active use on SCINet infrastructure, including Ceres and Atlas clusters. Jupyter operates as a client/server application.

When you run Jupyter, the application starts a server that serves the Jupyter application to your web browser. SCINet users can use Jupyter in one of the following ways:

Jupyter Notebooks Optional Scientific Computing Workbook

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