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 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: 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
### Output: 