Learning Launch - Module 4 

 Challenge 

 As a process engineer in my organization , one of the key responsibilities is to provide insights from a   subject matter point of view into new digitalization project . One of these project started earlier this year   is creation of a platform which is connected to all required databases and can be used to do both data   collection and analytics for yield improvement . After various ethnographic studies and even traditionally   carrying out projects we understood the flow required , data required , statistical needs , advance   analytical options and insight gathering that is required to drive a yield improvement project . Currently   we are a stage to develop the first prototype of the platform to people , the challenge is continuous   feedback , user accessibility , interface and the overall acceptance of the platform over traditional .   Methods . 

 Tool used 

 I have selected the learning launch too , since we shall be deploying a under progress prototype which   will be iterated based on feedback from the users . 

 Application 

 We started out with first survey to people and asked them the below questions : 

 1 . What should be the flow of the platform ? This would help us structure the prototype   2 . What kind of statistical features do you expect to be reflected when baseline data is selected ? 

 This would make data selection much more better and give a statistical summary on the go 

 3 . What advance analytics do you expect to automate ? 

 After receiving the surveys we shall carefully list them out and make a Pareto of them for each question   about which recommendation is most asked for[what if / what wows ] , which are feasible to be included   from a development stand point of view[what works ] and then developing those into the prototype . 

 After getting a structure the platform shall be rolled out to a selected number of people [ 10 - 12 ] who are   currently assigned their yield improvement projects at respective sites . They shall be trained in depth   with hands on examples so they familiarise themselves with the platform . Then they shall be asked to   start their project on the platform and write their feedbacks weekly as to . What can be done to make it   better . 

 After all their feedbacks are taken into account , they shall be asked to finally rate the platform for usage   from a scale of 1 to 10 . If the average score rated is to be greater than 8 , then it shall be deployed   across all people and the very people who tested would be asked to volunteer to provide training   sessions which will be recorded and available on our learning library to be accessed anytime . For a usage   acceptance , 3 projects from each site will be required to be done using the platform and they shall be   asked for updates uptil completion . Regular feedbacks and improvement interactions will be a part of   the project . 

 Insights 

 We gathered insights about the roll out structure which was as mentioned below 

 1)Data selection(in a dashboard style)→ categorising the data into good bad average->advance analytics   ( linear regression and random forest modelling)->graphical insights->accepting the parameters and   printing a report 

 2)On the dashboard itself average , min , max , standard deviation and quartiles included 

 3 ) Classification model was one of the most used model at our organization thus categorising was   included along with regression model . 

 A total of 42 feedback points was gathered from users with real time deployment out of which 36 were   implemented , 6 left apart due to technical capabilities but parked aside for future . 

 An average score of more than 8 was achieved and now the platform is deployed across the whole   organization and has completed more than 20 projects on it . 

 Approach 

 I would include a weekly update review with all stakeholders as there were many cross functional issues   faced during execution due to communication gap . 


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: Learning Launch - Module 4 

 Challenge 

 As a process engineer in my organization , one of the key responsibilities is to provide insights from a   subject matter point of view into new digitalization project . One of these project started earlier this year   is creation of a platform which is connected to all required databases and can be used to do both data   collection and analytics for yield improvement . After various ethnographic studies and even traditionally   carrying out projects we understood the flow required , data required , statistical needs , advance   analytical options and insight gathering that is required to drive a yield improvement project . Currently   we are a stage to develop the first prototype of the platform to people , the challenge is continuous   feedback , user accessibility , interface and the overall acceptance of the platform over traditional .   Methods . 

 Tool used 

 I have selected the learning launch too , since we shall be deploying a under progress prototype which   will be iterated based on feedback from the users . 

 Application 

 We started out with first survey to people and asked them the below questions : 

 1 . What should be the flow of the platform ? This would help us structure the prototype   2 . What kind of statistical features do you expect to be reflected when baseline data is selected ? 

 This would make data selection much more better and give a statistical summary on the go 

 3 . What advance analytics do you expect to automate ? 

 After receiving the surveys we shall carefully list them out and make a Pareto of them for each question   about which recommendation is most asked for[what if / what wows ] , which are feasible to be included   from a development stand point of view[what works ] and then developing those into the prototype . 

 After getting a structure the platform shall be rolled out to a selected number of people [ 10 - 12 ] who are   currently assigned their yield improvement projects at respective sites . They shall be trained in depth   with hands on examples so they familiarise themselves with the platform . Then they shall be asked to   start their project on the platform and write their feedbacks weekly as to . What can be done to make it   better . 

 After all their feedbacks are taken into account , they shall be asked to finally rate the platform for usage   from a scale of 1 to 10 . If the average score rated is to be greater than 8 , then it shall be deployed   across all people and the very people who tested would be asked to volunteer to provide training   sessions which will be recorded and available on our learning library to be accessed anytime . For a usage   acceptance , 3 projects from each site will be required to be done using the platform and they shall be   asked for updates uptil completion . Regular feedbacks and improvement interactions will be a part of   the project . 

 Insights 

 We gathered insights about the roll out structure which was as mentioned below 

 1)Data selection(in a dashboard style)→ categorising the data into good bad average->advance analytics   ( linear regression and random forest modelling)->graphical insights->accepting the parameters and   printing a report 

 2)On the dashboard itself average , min , max , standard deviation and quartiles included 

 3 ) Classification model was one of the most used model at our organization thus categorising was   included along with regression model . 

 A total of 42 feedback points was gathered from users with real time deployment out of which 36 were   implemented , 6 left apart due to technical capabilities but parked aside for future . 

 An average score of more than 8 was achieved and now the platform is deployed across the whole   organization and has completed more than 20 projects on it . 

 Approach 

 I would include a weekly update review with all stakeholders as there were many cross functional issues   faced during execution due to communication gap .
### Output: 