Planning is an important aspect of all projects to ensure that the strategic objectives of the project are accomplished. It is critical to breakdown a project into manageable phases or tasks to monitor progress and ensure timely completion. As a data scientist, it is important to have a working and well-thought plan of the end-to-end execution of a project to encourage yourself and have tangible outputs to show to stakeholders. Tools like Jira and Microsoft DevOps can be used to break down a data science project into different tasks for your team and it also gives team members visibility and progress updates. It makes collaboration much easier and code sharing to eliminate people solving the same problem. In one of my previous roles, Jira made collaboration easier because everyone with access to the project could raise issues/user stories and explain what and how they want the issue to be addressed. Also, it is good practice to write acceptance criteria in user stories in order ...
Data Science Consultancy with Expertise in Machine Learning