Jupyter, RMarkdown, Quarto
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This lecture will introduce learners to notebooks for research computing. Computational notebooks are an excellent resource for interactive development and data analysis using Python, R, and other languages. Notebooks can contain live code, equations, visualizations, and explanatory text which provide an integrated environment to use, share, and teach interactive data analysis.
Level: This is a beginner level workshop. No previous introduction to notebooks, programming, or reproducible reporting is necessary.
Learning Outcomes: Learners will become familiar with the anatomy of a notebook, will be introduced to notebook workflows, and will learn to author, use, and translate between reports in multiple formats. Learners will develop an understanding of how notebooks may improve their research workflow, and differences between existing notebook options.
Zoom link for this workshop.
Check out the above series of data science illustrations and more @ https://allisonhorst.com/data-science-art