Key learning

  • This course covered all the virtues of Software Project management including its various phases and different techniques of project analysis, quality control, Workflow automation etc..
  • This was very practical subject in which I did three remarkable project leveraging various skills of project planning, CI/CD, Machine learning and Cloud computing to implement the role of project manager in putting the large application on production.

Project 1: standard Software project planning using MS Project software

  • Created the software development workflow with the representation of WBS, gantt chart and network diagram
  • successfully assigned the resources for each tasks and calculated the duration for each task with according to the software management assumptions. Also created well understood “Tasks dependencies”
  • Finally implemented the estimation of duration from past project and planned the new project based on that on MS Project

Project 2: Github repoLens: Analysis of Github repo

  • This was the part of the assignment in which more than 160 students collaborated on same repository and created the issues with the specific tages related to various Project management phases to learn and implement github collaboration
  • then, performed the data analysis of the repository by collecting the data through Github API
  • Analyzed various data like pull requests, commits, issues with specific tags which can help the project manager to analyze the whole workflow

Project 3: Deployment of three microservices on GCP

  • Used the Github API to fetch the data of various wellknown repositories and used the data of pull request, commits etc.. and perfomed the forecasting using LSTM and react to show the graphs on website. I used the flask to automatically fetch the Github data based on the request from the react app. (Note: Some of the base code was provided from the professor for this project. Because this project was intended to implement the deployment of multiple micro-services on Cloud)
  • Deployed all three micro-services (forecasting, Flask and React) on GCP (Google cloud Platform) using Docker implementing CI/CD pipeline.