A data scientist specializes in the analysis and discovery of knowledge from vast amounts of data. This can be structured or unstructured, and may involve programming, data warehousing, machine learning, and natural language processing. There are many differences between the terms, and a data science career can range from being a hobbyist to a full-time job. This article will briefly explain the differences between the terms and how they differ.
One example of categorical data is that which contains either yes or no answers. For example, hospital admission data may indicate whether or not a patient smokes. Another example of categorical data would be data describing restaurants. Then, based on this information, a classification of genres could be made. The term classifier is a process for carrying out this process. It also applies to statistical analysis. Data science is often used in conjunction with machine learning or other fields to make accurate predictions and make recommendations.
Another data science synonym is boosted learning. Neural networks can learn by example. One technique that boosts weak learners is called boosting. Another data science synonym is “fitting” or generalization. Fitting refers to how well a model fits a population, but overfitting refers to applying an unfitting model to a larger population. When a model has poor fit, it can be classified as a “poor fit”.
