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.

A notable amount of work has been done to find sentence embeddings using compositional models in recent years. These works have shown that one of the simplest and most effective approaches to obtaining sentence embeddings is simple vector averaging of off-the-shelf word embeddings trained on large corpora. Recent literature introduced word weighting schemes based on the words frequency distribution into the simple averaging model. The frequency-based weighted averaging models augmented with the denoising steps are shown to outperform many complex deep learning models. However, these frequency-based weighting schemes derive the word weights solely based on their raw counts and ignore the diversity of contexts in which these words occur. This paper proposes an alternative weighting scheme that captures the contextual diversity in the word embedding space. The proposed weighting algorithm is simple, unsupervised, and non-parametric. Experimental results on semantic textual similarity tasks show that the proposed weighting method outperforms all the baseline models with significant margins and performs competitively to the current frequency-based state-of-the-art weighting approach. Furthermore, as the frequency distribution-based approaches and the proposed word embeddings geometry-based weighting approach capture two different properties of the words, we define hybrid weighting schemes to combine both the varieties. We also empirically demonstrate that the hybrid weighting methods perform consistently better than the corresponding individual weighting schemes.