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Common research activities in this phase include data collection (through existing datasets, first-hand collection, or experiments), data analysis, and documenting/maintaining the protocols or instruments used in the analysis.
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text using Julia, Python or R language. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Installation instruction here.
RStudio is an open-source integrated development environment (IDE) for R. It comes with debugging tools, syntax highlighters, and other features that makes working with R easier and more manageable. Download and installation instruction here.
Colab is a hosted Jupyter notebook service that also provides access to computing resources including GPUs. It allows anybody to write and execute arbitrary python code through the browser. It is free to use, with options for a premium subscription for more computing resources.
SciPy is a free and open-source Python library used for scientific and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, signal and image processing, and other tasks common in science and engineering.