Challenge   I am an analytical research scientist at a biotechnology company that collects large amounts of   data . At present only a fraction of this data is used to derive biological insights , while the   remainder is stored but not analyzed . In an internal research & development ( R&D ) project , a   member of the Data Science team and I were challenged to create a new software workflow that   would mine this unused data and report key features . Within our collaboration , I understand the   data , how it is generated and how to interpret it , while my collaborator is an expert in algorithms   and software . The challenge was that , because the new software is intended as an internal R&D   tool rather than a customer - facing product , we were given only a broad mandate for what it   should do , without specific details such as feature requirements for the software , criteria for   success , or tools to use in accomplishing those aims .      Selection   We were able to construct a prototype for the new software by working from first principles and   without taking much time to reflect on the overall structure of the project . However , once we   began to process real data and iteratively improve the software , it became clear that we needed   a more structured approach to plan and prioritize the work remaining to be done . This task fell to   me because I have a fuller understanding of the project ’s scientific context and overall goals ,   while my collaborator is focused on execution . To this end , I chose the Visualization tool   because , as mentioned in module 1 , it would help to make our ideas concrete and show us   options for further development of the software .      Application   In my implementation of the Visualization tool , I created a flowchart - like process map for our   software because it was a natural way to represent the path that data would take from input   ( raw data ) to output ( key features ) . Each software process or algorithm was represented as a   node in the flowchart , color - coded by status , such as complete , in - progress , or new .   Conceptually similar processes were grouped together . Altogether , the tool was effective   because it provided a useful way to visualize the project ’s structure and the status of various   work items , not only for my collaborator and me , but also for our managers , who previously had   only a general understanding of the software ’s features and had trouble keeping track of the   specific details .      Insight   One of the immediate insights I drew from the visualization exercise was to determine which of   the existing software components were “ good enough for now ” while others needed further   refinement and/or testing . I also generated at least one new idea simply by mapping out the   software ’s raw data inputs and realizing that one of them was completely unutilized . But   perhaps the most surprising insight came from mapping out the software ’s outputs . Although I   thought I already knew what those outputs would be , being forced to spell out this information in   the process map led to another , critical realization – specifically , that implementing a novel type   of intelligent database search was the key next step for getting to the desired outputs . We then   focused on developing that search for the ensuing 4 - 6 weeks , and it led to a quantum leap in   our ability to process new features , which in turn attracted attention from our company ’s   leadership team and won us additional resources for experimental testing .      Approach   Having experienced firsthand the power of the Visualization tool not only in organizing existing   ideas , but also in generating new ones , I strongly feel that it would be helpful to apply   Visualization at an earlier stage of project planning . I believe that , if we had done so in this   particular project , we may have saved time and allocated effort more efficiently : for example , we 

 could have identified and focused on critical components earlier on in the software development   process , and we could have avoided rewriting parts of the code to accommodate new features if   we had anticipated the need for those features from the start . 


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: Challenge   I am an analytical research scientist at a biotechnology company that collects large amounts of   data . At present only a fraction of this data is used to derive biological insights , while the   remainder is stored but not analyzed . In an internal research & development ( R&D ) project , a   member of the Data Science team and I were challenged to create a new software workflow that   would mine this unused data and report key features . Within our collaboration , I understand the   data , how it is generated and how to interpret it , while my collaborator is an expert in algorithms   and software . The challenge was that , because the new software is intended as an internal R&D   tool rather than a customer - facing product , we were given only a broad mandate for what it   should do , without specific details such as feature requirements for the software , criteria for   success , or tools to use in accomplishing those aims .      Selection   We were able to construct a prototype for the new software by working from first principles and   without taking much time to reflect on the overall structure of the project . However , once we   began to process real data and iteratively improve the software , it became clear that we needed   a more structured approach to plan and prioritize the work remaining to be done . This task fell to   me because I have a fuller understanding of the project ’s scientific context and overall goals ,   while my collaborator is focused on execution . To this end , I chose the Visualization tool   because , as mentioned in module 1 , it would help to make our ideas concrete and show us   options for further development of the software .      Application   In my implementation of the Visualization tool , I created a flowchart - like process map for our   software because it was a natural way to represent the path that data would take from input   ( raw data ) to output ( key features ) . Each software process or algorithm was represented as a   node in the flowchart , color - coded by status , such as complete , in - progress , or new .   Conceptually similar processes were grouped together . Altogether , the tool was effective   because it provided a useful way to visualize the project ’s structure and the status of various   work items , not only for my collaborator and me , but also for our managers , who previously had   only a general understanding of the software ’s features and had trouble keeping track of the   specific details .      Insight   One of the immediate insights I drew from the visualization exercise was to determine which of   the existing software components were “ good enough for now ” while others needed further   refinement and/or testing . I also generated at least one new idea simply by mapping out the   software ’s raw data inputs and realizing that one of them was completely unutilized . But   perhaps the most surprising insight came from mapping out the software ’s outputs . Although I   thought I already knew what those outputs would be , being forced to spell out this information in   the process map led to another , critical realization – specifically , that implementing a novel type   of intelligent database search was the key next step for getting to the desired outputs . We then   focused on developing that search for the ensuing 4 - 6 weeks , and it led to a quantum leap in   our ability to process new features , which in turn attracted attention from our company ’s   leadership team and won us additional resources for experimental testing .      Approach   Having experienced firsthand the power of the Visualization tool not only in organizing existing   ideas , but also in generating new ones , I strongly feel that it would be helpful to apply   Visualization at an earlier stage of project planning . I believe that , if we had done so in this   particular project , we may have saved time and allocated effort more efficiently : for example , we 

 could have identified and focused on critical components earlier on in the software development   process , and we could have avoided rewriting parts of the code to accommodate new features if   we had anticipated the need for those features from the start .
### Output: 