Documenting Your Work

Abstract

Data science and scientific computing are human-centered, collaborative endeavors that gather teams of experts covering multiple domains. You will rarely perform any serious data analysis task alone. Therefore, efficient intra-team communication that ensures proper information exchange within a team is required. There is also a need to convey all details of an analysis to relevant external parties. Your team is part of a larger scientific community, so others must be able to easily validate and verify your team’s findings. Reproducibility of an analysis is as important as the result itself. Achieving this requirement—to effectively deliver data, programs, and associated narrative as an interactive bundle—is not a trivial task. You cannot assume that everybody who wants to peek into your analysis is an experienced software engineer. On the other hand, all stakeholders aspire to make decisions based on available data. Fortunately, there is a powerful open-source solution for reconciling differences in individuals’ skill sets. This chapter introduces the project Jupyter (see https://jupyter.org ), the most popular ecosystem for documenting and sharing data science work.