Example reflection – Learning Launch 

 Challenge 

 I am a lead data scientist in an elderly care project . The aim of this project is to provide an IOT   technology driven complementary healthcare system to assist the daily work of healthcare   providers , and improve the quality of life for elderly patients . There are three main stakeholders in   this project , namely the elderly patients and their family members , the healthcare providers ( i.e. care managers of the patients ) , and IT technicians ( i.e. software engineers , data scientists , IT architects ) . 

 The challenge of this   project is that there are lots of variations of the design of potential system ,   from sensor selection , IT architecture design , data collection , analytic objectives , and results   display . Especially , if we rely on one set of system selection and go through the whole loop to   collect real data and test the system . The cost of failure could be huge and unrecoverable . 

 Selection 

 User experience is crucial for the success of the project . Therefore we decide to take a agile   approach to communication with stakeholders through frequent feedback delivery loops . We split   the IT system into three main components , i.e. sensor selection and deployment strategy , data   analysis pipeline , and results visualization . Based on our experience in relevant technique domains ,   we proposed a set of sensor kit options , and data analysis prototypes , as well as data visualization   designs . We set learning launch targets at different stages of the project to collect user feedback to   test our hypothesis and adjust the system design . 

 Application 

 For our first learning launch , we aimed at narrowing down the sensor kit selections and result   visualization design , to understand the users ’ preference and needs . We recruited 20 elderly patients   who are potential users of the system , and elderly who are simply curious . The end product has to   be a compromise between technique complexity and the usability ( user interaction required , and the price ) . We grouped the selected sensor kits into different categories accordingly to the amount of   user interaction required , and their capabilities . We create result visualization interface based on   public dataset and mock data that we created . During this session , we have gain valuable   information in terms of sensor selection , and what information ( result visualization ) to feed to the   users . 

 Our second learning launch focused on testing several prototype IT structure , and data analysis   pipeline based on real data collected from volunteer patients . The aim is to get ready for a first hand user experience of the running system . During this session , we were able to communicate with users about how an edge based system could offer , and what we could achieve with a powerful cloud- based service , as well as what service is necessary for a real - time feedback , while another could   afford a delay . 

 We launched several more rounds of learning launch afterwards . At each round we tested hypothesis that were set beforehand . Through these launches , we were able to get closer and closer to a system   prototype that offers users the best experience , and a backend IT system that offers   robust and   seamless service to the elderly patients . 

 Insight 

 By applying the learning launch tool in an agile way , our team were able to efficiently communicate with the target users and narrow down the hypothesis of various technological tool kit options .   Additionally , the process has brought the stack holders in a closer healthy relationship , by taking   care of users ’ feedback in the design loop , which has largely increased the confidence of the elderly   patients into using our system . 

 Approach 

 Given the success of applying the learning launch tool in this project , I would like to continue   practising it in future projects . However , various types of project may require different degrees of   efforts to launch from the technicians as well the end users . Therefore , carefully set hypothesis ,   goals , and reasonable time line is very crucial to a successful project , as well as to build a healthy   relationship among stakeholders . I would like to pay extra attention in these factors in the future . 


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: Example reflection – Learning Launch 

 Challenge 

 I am a lead data scientist in an elderly care project . The aim of this project is to provide an IOT   technology driven complementary healthcare system to assist the daily work of healthcare   providers , and improve the quality of life for elderly patients . There are three main stakeholders in   this project , namely the elderly patients and their family members , the healthcare providers ( i.e. care managers of the patients ) , and IT technicians ( i.e. software engineers , data scientists , IT architects ) . 

 The challenge of this   project is that there are lots of variations of the design of potential system ,   from sensor selection , IT architecture design , data collection , analytic objectives , and results   display . Especially , if we rely on one set of system selection and go through the whole loop to   collect real data and test the system . The cost of failure could be huge and unrecoverable . 

 Selection 

 User experience is crucial for the success of the project . Therefore we decide to take a agile   approach to communication with stakeholders through frequent feedback delivery loops . We split   the IT system into three main components , i.e. sensor selection and deployment strategy , data   analysis pipeline , and results visualization . Based on our experience in relevant technique domains ,   we proposed a set of sensor kit options , and data analysis prototypes , as well as data visualization   designs . We set learning launch targets at different stages of the project to collect user feedback to   test our hypothesis and adjust the system design . 

 Application 

 For our first learning launch , we aimed at narrowing down the sensor kit selections and result   visualization design , to understand the users ’ preference and needs . We recruited 20 elderly patients   who are potential users of the system , and elderly who are simply curious . The end product has to   be a compromise between technique complexity and the usability ( user interaction required , and the price ) . We grouped the selected sensor kits into different categories accordingly to the amount of   user interaction required , and their capabilities . We create result visualization interface based on   public dataset and mock data that we created . During this session , we have gain valuable   information in terms of sensor selection , and what information ( result visualization ) to feed to the   users . 

 Our second learning launch focused on testing several prototype IT structure , and data analysis   pipeline based on real data collected from volunteer patients . The aim is to get ready for a first hand user experience of the running system . During this session , we were able to communicate with users about how an edge based system could offer , and what we could achieve with a powerful cloud- based service , as well as what service is necessary for a real - time feedback , while another could   afford a delay . 

 We launched several more rounds of learning launch afterwards . At each round we tested hypothesis that were set beforehand . Through these launches , we were able to get closer and closer to a system   prototype that offers users the best experience , and a backend IT system that offers   robust and   seamless service to the elderly patients . 

 Insight 

 By applying the learning launch tool in an agile way , our team were able to efficiently communicate with the target users and narrow down the hypothesis of various technological tool kit options .   Additionally , the process has brought the stack holders in a closer healthy relationship , by taking   care of users ’ feedback in the design loop , which has largely increased the confidence of the elderly   patients into using our system . 

 Approach 

 Given the success of applying the learning launch tool in this project , I would like to continue   practising it in future projects . However , various types of project may require different degrees of   efforts to launch from the technicians as well the end users . Therefore , carefully set hypothesis ,   goals , and reasonable time line is very crucial to a successful project , as well as to build a healthy   relationship among stakeholders . I would like to pay extra attention in these factors in the future .
### Output: 