The origin of data science dates back to the early foundations of statistics and probability in the early 60 s, which was then further developed with the rise of computing and database systems. However, it was not until the 2010s with the emergence of big data (i.e. large volumes of heterogeneous data) and the maturity of machine learning algorithms that data science gained enormous popularity. Data science is a discipline in charge of studying data with the aim of extracting knowledge from hidden patterns, descriptive analytics, and causal inference, among other sources of knowledge. Data science is an interdisciplinary field of study involving multiple areas, such as mathematics, statistics, and computer science. Data visualization, which is one of the key parts of data science, involves the process of creating a visual representation of data so that knowledge and insights are effectively communicated. There are different types of data visualizations, such as bar charts, histograms, line graphs, scatter plots, and heatmaps, among others. In addition, there are several tools for data visualization, such as Python libraries, R packages, and web-based tools (e.g. Tableau and Power BI). Nevertheless, there are a number of challenges in data visualization that need to be addressed, such as visualizing data with many variables, tools constraints (e.g. types of visualization and volume supported), and selecting the most suitable visualization type, among others. In this chapter, we review some of the most important types of visualization as well as the most widely used visualization tools. We also provide guides to select the most appropriate visualization type and visualization tool according to the volume and type of data. Finally, we point out some issues that need further research regarding data visualization in data science.