Data analysis is considered by many researchers the most critical component of a research project/study. Data analysis is a process that allows researchers to determine what stories the data tell—which Covid vaccine is most effective? How well do children learn to read in heterogeneous groups? What is the effect of climate change on desert succulents—the answers to these questions, or any research question, derive from data analysis. During data analysis, researchers (a) verify/determine the accuracy/authenticity of the data, (b) apply the planned (and appropriate) procedures to the analyze the data, and (c) confirm what the data say. Successfully concluding these processes ensures that the data are clean/uncontaminated and that, when analyzed appropriately, provide answers to the research questions, inform conclusions, and support further decision-making. The focus of the chapter will be on types of data analyses related to three types of research methodologies—quantitative, qualitative, and mixed methods. Examples of embedded assignments containing the three methodologies and their analyses will be included as well as some of the key concepts and limitations related to each method, especially bias. Finally, the chapter concludes with readily available and popular software used in analyzing quantitative and qualitative data.