Exploratory data analysis (EDA) is an iterative process where data scientists interact with data to extract information about their quality and shape as well as derive knowledge and new insights into the related domain of the dataset. However, data scientists are rarely experienced domain experts who have tangible knowledge about a domain. Integrating domain knowledge into the analytic process is a complex challenge that usually requires constant communication between data scientists and domain experts. For this reason, it is desirable to reuse the domain insights from exploratory analyses in similar use cases. With this objective in mind, we present a conceptual system design on how to extract domain expertise while performing EDA and utilize it to guide other data scientists in similar use cases. Our system design introduces two concepts, interaction storage and analysis context storage, to record user interaction and interesting data points during an exploratory analysis. For new use cases, it identifies historical interactions from similar use cases and facilitates the recorded data to construct candidate interaction sequences and predict their potential insight—i.e., the insight generated from performing the sequence. Based on these predictions, the system recommends the sequences with the highest predicted insight to data scientist. We implement a prototype to test the general feasibility of our system design and enable further research in this area. Within the prototype, we present an exemplary use case that demonstrates the usefulness of recommended interactions. Finally, we give a critical reflection of our first prototype and discuss research opportunities resulting from our system design.
Clustering with Scikit-Learn in Python
