Exploratory Data Analysis (EDA) derives meaningful insights from extensive and complex datasets. This process typically involves a series of analytical operations to identify the patterns within the data. However, the effectiveness of EDA is often limited by the user's domain knowledge and proficiency in data exploration methods. To overcome these challenges, we developed QUIS, a fully automated EDA system that uncovers insights by generating data-related questions and exploring subspaces in the dataset without prior training. QUIS allows users to control key system parameters such as beam width, beam depth, and expansion factor for subspace selection, the interestingness score for filtering valuable insights, and parameters for managing the quality and quantity of generated questions.

It is widely accepted that data preparation is one of the most time-consuming steps of the machine learning (ML) lifecycle. It is also one of the most important steps, as the quality of data directly influences the quality of a model. In this tutorial, we will discuss the importance and the role of exploratory data analysis (EDA) and data visualisation techniques to find data quality issues and for data preparation, relevant to building ML pipelines. We will also discuss the latest advances in these fields and bring out areas that need innovation. To make the tutorial actionable for practitioners, we will also discuss the most popular open-source packages that one can get started with along with their strengths and weaknesses. Finally, we will discuss on the challenges posed by industry workloads and the gaps to be addressed to make data-centric AI real in industry settings.