Data visualization is the graphic representation of data. It involves  producing images that communicate relationships among the  represented data to viewers of the images. This communication is  achieved through the use of a systematic mapping between graphic  marks and data values in the creation of the visualization. This  mapping establishes how data values will be represented visually,  determining how and to what extent a property of a graphic mark,  such as size or color, will change to reflect changes in the value of a  datum.

To  communicate  information  clearly  and  efficiently,  data

visualization uses statistical graphics, plots, information graphics and  other tools. Numerical data may be encoded using dots, lines, or  bars, to visually communicate a quantitative message.[1] Effective  visualization helps users analyze and reason about data and  evidence. It makes complex data more accessible, understandable  and usable. Users may have particular analytical tasks, such as  making comparisons or understanding causality, and the design  principle of the graphic (i.e. showing comparisons or showing  causality) follows the task. Tables are generally used where users will  look up a specific measurement, while charts of various types are  used to show patterns or relationships in the data for one or more  variables.

Data visualization is both an art and a science. It is viewed as a  branch of descriptive statistics by some, but also as a grounded  theory development tool by others. Increased amounts of data  created by Internet activity and an expanding number of sensors in  the environment are referred to as "big data" or Internet of things.  Processing, analyzing and communicating this data present ethical  and analytical challenges for data visualization. The field of data  science and practitioners called data scientists help address this  challenge.[3] In the commercial space, this increased amount of data  generation has resulted in a push for workers to have a greater visual  literacy than previously needed.

This module offers an introduction to visualization, what it is, how it  works, and what ethical considerations are involved in its design. It  also teaches how to prepare data before visualizing it, which will be  covered.

This module covers how visualization can be used to explore and  discover features that often hide behind data. The materials will  show you how to use software tools that will allow you to import a  data set and then visualize it in multiple ways to reveal patterns and  exceptions to those patterns.

This module explains how visualization can be used to communicate  with the public. The video classes will show you how to use a Google  tool called Flourish, to design static and interactive maps and charts,  and then put them together in sequential narratives.

This module is focused on designing a visualization based on a topic  and data sets of your choice

