Visualising results of statistical modeling is a key component of data science workflow. Statistical graphs often is the best means to explain and promote research findings. However,in order to find that one graph that tells the story worth sharing, we sometimes have to try out and sift through many data visualizations. How should we approach such a task? What can we do to make it easier from both production and evaluation perspectives?
Visualising results of statistical modeling is a key component of data science workflow. Statistical graphs are often the best means to explain and promote research findings. However, in order to find that one graph that tells the story worth sharing, we sometimes have to try out and sift through many data visualizations. How should we approach such a task? What can we do to make it easier from both production and evaluation perspectives?
Abstract While computational notebooks offer scientists and engineers many helpful features, the limitations of this medium make it but a starting point in creating software - the practical goal of data science. Where do we go from computational notebooks if our projects require multiple interconnected scripts and dynamic documents? How do we ensure reproducibility amidst growing complexity of analyses and operations?
I will use a concrete analytical example to demonstrate how constructing workflows for reproducible analyses can serve as the next step from computational notebooks towards creating an analytical software.