Automating Exploratory Data Analysis via Machine Learning: An Overview

TL;DR: This tutorial reviews recent lines of work for automating EDA, starting from recommender systems for suggesting a single exploratory action, going through kNN-based classifiers and active-learning methods for predicting users' interestingness preferences, and finally to fully automates EDA using state-of-the-art methods such as deep reinforcement learning and sequence-to-sequence models.

Abstract: Exploratory Data Analysis (EDA) is an important initial step for any knowledge discovery process, in which data scientists interactively explore unfamiliar datasets by issuing a sequence of analysis operations (e.g. filter, aggregation, and visualization). Since EDA is long known as a difficult task, requiring profound analytical skills, experience, and domain knowledge, a plethora of systems have been devised over the last decade in order to facilitate EDA. In particular, advancements in machine learning research have created exciting opportunities, not only for better facilitating EDA, but to fully automate the process. In this tutorial, we review recent lines of work for automating EDA. Starting from recommender systems for suggesting a single exploratory action, going through kNN-based classifiers and active-learning methods for predicting users' interestingness preferences, and finally to fully automating EDA using state-of-the-art methods such as deep reinforcement learning and sequence-to-sequence models. We conclude the tutorial with a discussion on the main challenges and open questions to be dealt with in order to ultimately reduce the manual effort required for EDA.