Visualization 

 1 - Challenge :     The last years witnessed a continued growth of the amount of data . The data analysis and 

 exploration has become more and more difficult . And personally I have that big problem of   understanding and remembering all the data so I have a less concentration in my course   So , it seems   important to find means to visually represent this flood of data which can help any user to get and   understand information efficiently and implicate him / her in the data mining process . 

 2 - Selection : 

 I choose the first tool : The visualization domain who proposes a large number of information 

 visualization techniques which have been developed over the last decade to support the 

 exploration of large data sets and to achieve a shared understanding of a problem space 

 among a diverse group of stakeholders before attempting to identify and define possible 

 solutions to the problem so The visualization is more powerful and useful for example 

 Comparing numbers presented in a flat table to find the top value is extremely difficult and 

 takes a long time . This is where visualizing your data through graphical representation can 

 make your life easier . If you plot the data pictorially using widgets , finding the top value from 

 a year ’s worth of data is possible in a few seconds , maybe even a fraction of a second . 

 3 - Application : 

 Personally I apply a visualization in my studies for example the visualization for the physical 

 sciences . ie introduce novel classification schemes based on application area , data dimensionality and 

 main challenge addressed , and apply these classifications to each contribution from the literature . my 

 survey helps in understanding the status of current research and serves as a useful starting point for 

 those interested in visualization for the physical sciences . 

 4 - Insight & Approach : 

 A primary purpose of a visualization tool is to provide domain - relevant insight into the data . 

 Ideally , any user wants maximum information in the least possible time . In this paper we 

 identify several distinct characteristics of insight that enable us to recognize and quantify it . 

 Based on this , we empirically evaluate five popular microarray visualization tools . Our 

 conclusions can guide biologists in selecting the best tool for their data , and computer 

 scientists in developing and evaluating visualizations 

 And this is What I have learned from conducting a visualization : 

 •   Better analysis . 

 •   Quick action . 

 •   Identifying patterns . 

 •   Finding errors . 

 •   Understanding . 

 •   Exploring business insights . 

 •   Grasping the Latest Trends .     Since I discovered that the most valuable aspect is the shared insights and learning , next time I would   use the visualization activity as a starting point for a series of design thinking activities in many   domains 


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: Visualization 

 1 - Challenge :     The last years witnessed a continued growth of the amount of data . The data analysis and 

 exploration has become more and more difficult . And personally I have that big problem of   understanding and remembering all the data so I have a less concentration in my course   So , it seems   important to find means to visually represent this flood of data which can help any user to get and   understand information efficiently and implicate him / her in the data mining process . 

 2 - Selection : 

 I choose the first tool : The visualization domain who proposes a large number of information 

 visualization techniques which have been developed over the last decade to support the 

 exploration of large data sets and to achieve a shared understanding of a problem space 

 among a diverse group of stakeholders before attempting to identify and define possible 

 solutions to the problem so The visualization is more powerful and useful for example 

 Comparing numbers presented in a flat table to find the top value is extremely difficult and 

 takes a long time . This is where visualizing your data through graphical representation can 

 make your life easier . If you plot the data pictorially using widgets , finding the top value from 

 a year ’s worth of data is possible in a few seconds , maybe even a fraction of a second . 

 3 - Application : 

 Personally I apply a visualization in my studies for example the visualization for the physical 

 sciences . ie introduce novel classification schemes based on application area , data dimensionality and 

 main challenge addressed , and apply these classifications to each contribution from the literature . my 

 survey helps in understanding the status of current research and serves as a useful starting point for 

 those interested in visualization for the physical sciences . 

 4 - Insight & Approach : 

 A primary purpose of a visualization tool is to provide domain - relevant insight into the data . 

 Ideally , any user wants maximum information in the least possible time . In this paper we 

 identify several distinct characteristics of insight that enable us to recognize and quantify it . 

 Based on this , we empirically evaluate five popular microarray visualization tools . Our 

 conclusions can guide biologists in selecting the best tool for their data , and computer 

 scientists in developing and evaluating visualizations 

 And this is What I have learned from conducting a visualization : 

 •   Better analysis . 

 •   Quick action . 

 •   Identifying patterns . 

 •   Finding errors . 

 •   Understanding . 

 •   Exploring business insights . 

 •   Grasping the Latest Trends .     Since I discovered that the most valuable aspect is the shared insights and learning , next time I would   use the visualization activity as a starting point for a series of design thinking activities in many   domains
### Output: 