Colaboratory (Colab) is a Google cloud hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources. The ‘Jupyter Notebook‘ is a 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, machine learning, and modern scientific data analysis, visualization and reporting.
The Jupyter ecosystem is rapidly expanding and has lots of self-hosted and cloud hosted options. Here we will focus on one of the most popular cloud hosted options, Google Colab, and the default and most common programming language used in Jupyter Notebooks, Python.
This is the point where most students and scientific researchers loose interest and prefer to use more familiar software applications, i.e., Microsoft OneNote, Word, Excel and PowerPoint (or Google Docs, Sheets, Slides). I know, it’s hard enough to train people up on Excel for data analysis and visualization. You’re really going to have them learn Python? Yes, really.
The ‘useability’ and ‘familiarity’ arguments typically drive the continued use of spreadsheets (e.g., Microsoft Excel, Google Sheets) for scientific data analysis and visualization and reporting. However, the simple fact is that Excel or Sheets just is NOT well-suited to handle most scientific data processing and analysis. Sooner or later, every scientist figures this out and ends up use a bunch of ‘other’ software most raw data processing and analysis. The Jupyter platform makes Python a lot more accessible and is significantly more powerful than Excel and much more generally applicable across all forms of scientific data processing, analysis and visualization.
A Jupyter Notebook (e.g., Colab) has two cell types, (i) code and (ii) text. The default ‘code’ is Python and the default ‘text’ is markdown (with LaTeX for mathematics). Yes, it requires new users to ultimately learn basic Python programming and commonly used extensions (libraries) as well as basic markdown (and LaTeX) syntax. However, the end result is a powerful skill set that is very broadly applicable across all areas of data science, scientific computing, computational journalism, machine learning, and modern scientific data analysis, visualization and reporting.
Jupyter Notebooks (Colab) by Example
While it can feel daunting at first, especially if you have never learned any scripting or programming language (Python, R, Julia, JavaScript), or any type of markup language (HTML, LaTeX, Markdown). Jupyter Notebooks (and associated AI assistants) really help overcome all the hurdle of what used to be a completely command line terminal process, making Python (and Markdown) effortless to access and quickly learn to use efficiently and effectively. Lets dive right in and look at some examples in colab.
- Getting Started with Google Colab
- Overview of Colaboratory Features (Google Colab Standard ‘Examples’)
- Introduction to Jupyter (Colab) Notebooks – Python ‘Code’ and MarkDown ‘Text’ Cells
- Introduction to Pchem Lab Data/Error Analysis using Colaboratory (Jupyter)
- Colaboratory Laboratory Notebook Template for Pchem Lab Reports
- Linear and Non-Linear Fitting of Data using Colaboratory (Jupyter)
BioPchem 