Journals

  • 05/05/2023 (Group Meeting)
    • Finished up all documentations
    • Added backend tests
    • Finished the hand-off plan
  • 04/25/2023 (Group Meeting)
    • Added frontend tests
  • 04/24/2023 (Coach Meeting)
    • Talked about the handoff plan
    • Talked about the final presentation
  • 04/23/2023 (Group Meeting)
    • Finishing up final presentation sliders
    • Finishing up final presentation demo
    • Fix some error on our simulation app
  • 04/21/2023 (Group Meeting)
    • Distribute final presentation tasks to each member
  • 04/20/2023 (Client Meeting)
    • Show our simulation app to Mr. Hughes
    • Show our template to client
    • Demonstrate the simulation input template to our client
  • 04/19/2023 (Group Meeting)
    • Create a scoreboard to show the quarterly win and loss.
    • Decide to use the alertify.js package for error handling.
  • 04/09/2023 (Group Meeting)
    • Finished up the Tech Talk presentation slides.
    • Practice the presentation together.
    • Finished up the documentation plan assignment.
  • 04/07/2023 (Group Meeting)
    • Discuss the role for each member in the Tech Talk.
    • Create the google slides for the Tech Talk.
  • 04/06/2023 (Group Meeting)
    • Set up the Nivo package.
    • Graph the data fetch back from the Lambda Function using Nivo Package.
  • 04/05/2023 (Group Meeting)
    • Finished up the AWS Lambda Function.
    • Now our frontend can call the lambda function and get the response of data.
  • 04/04/2023 (Group Meeting)
    • Configure the AWS Lambda Function.
    • Host the application using AWS Amplify.
  • 04/03/2023 (Coach Meeting)
    • Discuss the parsing csv file with coach.
    • Review the test plan with coach.
  • 04/02/2023 (Group Meeting)
    • Meeting as a group to discuss the parsing of uploaded csv file.
    • Finished the parsing code in JS to parse csv file to different arrays.
    • Next step is to format the data into JSON in order to input it to the AWS Lambda function
  • 03/30/2023 (Client Meeting)
    • Meeting with AWS people and get clarification for AWS architecture.
  • 03/26/2023 (Group Meeting)
    • Discussed the values and impacts of our project
    • Finished the APPLES Reflection and published into our website
  • 03/23/2023 (Client Meeting)
    • Send some questions to client about AWS:
      • If we use Amplify to connect to a DynamoDB database, what is the best way to store user specific variables? Would we need to set up Amazon Cognito to do this?
      • Can we define a new API that takes user input from the React front-end and calculates the simulation output using Amplify? (Possibly using the “amplify add api” command)
      • If so, what is the best way to incorporate the existing Python code that does the calculation?
  • 03/20/2023 (Coach Meeting)
    • Discuss simplifying UI design because of time constraints
    • Show demo of simple AWS app
    • Discuss solutions to testing problem (need to set random numbers to be consistent in excel)
  • 03/20/2023 (Group Meeting)
    • Talk about simple AWS Amplify app created to gain familiarity with AWS technologies and set-up
    • Discuss how we want to structure code and combine front end (React) with the Python simulation
    • Go over potential problems with setting the Excel seed for testing (cannot do this in Excel directly, but may be able to use Jupyter notebook)
  • 03/06/2023(Coach Meeting)
    • Discuss visual representation of model
    • Need to update y-axis to be consistent from week to week in simulation output
    • Talk about potential architecture changes involving code optimization by baking model into front end, but this may not be necessary
  • 03/02/2023(Client Meeting)
    • Demonstrate model with graph output and discuss potential changes
    • Discuss implementation of sliders
    • Meet with AWS team to discuss cloud hosting and database storage for the application
  • 02/27/2023(Coach Meeting)
    • Discuss model updates, code formatting, as well as the Ethics assignment
  • 02/26/2023 (Group Meeting)
    • Practice for midterm presentation
  • 02/20/2023 (Coach Meeting)
    • Discuss the model demo with Louie
    • Show the model input/output flowchart to Louie
  • 02/20/2023 (Group Meeting)
    • Explore the model in Python
    • Add more documentations in user stories
    • Midterm presentation coming up
  • 02/16/2023 (Client Meeting)
    • Mr. Hughes verified the flow chart with us
    • Show Mr. Hughes our draft model on Pycharm
    • Clarify the the input file format of the model
  • 02/16/2023 (Group Meeting)
    • Discuss the next step for replicating the model
      • Allowing more input variables into the model
    • Discuss how to read the input file into our model
    • Discuss the upcoming platform selection assignment
    • Decide what programming languages, libraries, framework to use in our project
      • Languages: Python (Client Preference), JavaScript (Recommended by Client)
      • Frameworks: React
      • Libraries: NumPy, Plotly
      • Hosting: AWS (Client Preference)
      • Database: AWS (Client Preference)
  • 02/06/2023 (Coach Meeting)
    • Show the Excel model to coach
    • Recommend to draw a flow chart of the model
    • Need to add low-level user stories on our website
    • First priority is to understand the data flow of the Excel model
  • 02/02/2023 (Client Meeting & Followed be a Group Meeting)
    • Met with Mr. Hughes at the new Launch Chapel Hill.
    • Mr. Hughes presented his go-to market simulation model to us on Excel. He explained how the model works and what is his expectations of the MVP software.
    • We introduced our February plan to Mr. Hughes (https://docs.google.com/document/d/154e-Iy9etBTzYXAUiN1DbzGwKw6T1V_S2fGvKKX6u2E/edit?usp=sharing)
      • Replicate the Excel model in Python (Pycharm?)
        1. Variables, changeable opportunities…
        2. Use fake data
        3. Push to Github for now
      • Test Plan: Perform unit tests for the model, ask professors to verify the python model with us
      • Possibly need to do data transfer. How do clients give their data? In what format?
        1. Logs, csv, excel, json files?
        2. Create a structural object to save this data (SQL).
      • Test Plan: Test the python model with real data.
    • We decided our next step is to replicate the model functionalities in Pycharm.
  • 01/30/2023 (Coach Meeting)
    • Talks about our progress with coach Louie, our project is very straightforward and has a list of tasks to do.
    • Achieved the plan for last week, finished User Stories, finished updating our website.
    • The next step we need to do is to ask Mr. Hughes to present his Excel model with us.
  • 01/26/2023 (First In-Person Client Meeting)
    • Meet with our client Mr. David Hughes
    • Mr. Hughes introduces his kinetik project to us
    • Weekly in-person client meeting at Launch Chapel Hill on Thursday at 3:30 pm
    • Define 10 technical sprints for the project
      • Upload file to AWS through kinetik.solutions
      • Convert Figma design to AWS Amplify interface
      • Validate forecasting model with UNC Statistics professor or grad student
      • Review excel model and replicate functionality in Python (test Jeff’s code)
      • Publish code to AWS from GitHub
      • Meet with Amazon Development team weekly
      • Meet with K-F MBA team to hear market feedback
      • Natural Language Processing model for sales and marketing issues, best practices and optimization methods
      • Recruit technical mentors (Data Science, Statistics / Monte Carlo, AWS)
      • Determine JavaScript vs. TypeScript (https://www.toptal.com/typescript/typescript-vs-javascript-guide)
    • Created an iMessage group chat with Mr. Hughes
  • 01/20/2023
    • Change one of our project prefrences
  • 01/18/2023 (Group Meeting 2)
    • Discuss team rules
    • Assign team roles
    • Discuss every member’s skill stack
    • Read through all projects together and select projects 6 project prefrences

  • 01/13/2023 (Group Meeting 1)
    • First group meeting!
    • Created our group website
    • Discuss every member’s availability and assign weekly meeting time