Titlebook: Introduction to Data Science; A Python Approach to Laura Igual,Santi Seguí Textbook 2024Latest edition The Editor(s) (if applicable) and Th

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Descriptive Statistics, later chapter, in descriptive statistics we do not draw conclusions beyond the data we are analyzing; neither do we reach any conclusions regarding hypotheses we may make. We do not try to infer characteristics of the “population” (see below) of the data, but claim to present quantitative descripti

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Statistical Inference, descriptions we have seen are based on graphical representations that are easily interpreted by humans, while others are based on parameters that summarize important properties of the sample distribution. In this chapter we see how to infer predictions about a population. To this end we explore the

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Supervised Learning,ory and good practices for successful use of classifiers in a real case using Python. The chapter starts by introducing the classic machine learning pipeline, defining features, and evaluating the performance of a classifier. After that, the notion of generalization error is needed, which allows us

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Basics of Natural Language Processing, sentiment analysis is performed based on the processing of natural language, the analysis of text and computational linguistics. Although data can come from different data sources, in this chapter we analyze sentiment in text data, using two particular text data examples: one from film critics, whe

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