Data Analysis

One of the fundamental principles behind any large-scale data science procedure is the simple fact that any Machine Learning (ML) model produced is only as good as the data on which it is trained. Beginner data scientists often make the mistake of assuming that they just need to find the right ML model for their use case and then simply train or fit the data to the model. However, nothing could be further from the truth. Getting the best possible model requires exploring the data, with the goal being to fully understand the data. Once the data scientist understands the data and how the ML model can be trained on it, the data scientist often spends most of their time further cleaning and modifying the data, also referred to as wrangling the data, to prepare it for model training and building.

While this data analysis task may seem conceptually straightforward, the task becomes far more complicated when we factor in the type (images, text, tabular, and so on) and the...