Data Collection and Literacy Defined for AI and Analytics
Data collection is the process of gathering and measuring information about specified variables for later analysis. The quality of these methods and measurements directly impacts subsequent conclusions or trained models; biased surveys or faulty sensors lead to weak evidence. Data literacy is defined as the ability to understand, analyze, interpret, use, and communicate data. This distinction is crucial because raw data, such as '25°C,' becomes information only when processed and given useful context, like '25°C indicates a warm day in JP Nagar,' where location and interpretation are added. In the realm of Artificial Intelligence (AI), evaluation involves comparing a trained model's predictions with known outcomes. It is essential to use test data that has not been seen during the training phase to ensure the measurement assesses the model's generalization capabilities rather than its memory of training data. This practice helps in understanding how well a model performs on new, unseen data.
