Applied data science covers all the activities and processes data analysts must typically undertake to deliver evidence-based results of their analyses. This includes data collection, preprocessing data that may contain some basic but frequently time-consuming data transformations, and manipulations, EDA to describe the data under investigation, research methods, and statistical models applicable to the data and related to the research questions, and finally, data visualizations and reporting the insights. Data science is an enormous field, covering a great number of specific disciplines, techniques, and tools, and there are hundreds of very good printed and online resources explaining the particulars of each method or application.
In this section, we will merely focus on a small fraction of selected topics in data science using the R language. From this moment on, we will also be using real data sets from socio-economic domains. These data sets, however, will...
