Guide for Project Design

This guide covers topics related to effective project planning and management.

Before starting a project, data scientists need to understand what their research questions are, what are the possible outcomes, who are their users and target audience, what resources are available and what possible constraints exist in the project. Project design starts with these crucial questions. Then comes the planning. The scoping of the project in terms of ethics and usability of their outcome, expected minimum viable product of this project, synergies with other projects, similarities or differences compared to other projects, a measure of success, and the overall impact of this project. Project design also includes aspects such as time, budget, risks, expectations, people, resources and timeline management, and preregistration of statistical protocols.

A group of people collaboratively developing a project plan by writing on a giant canvas with a giant pencil to signify its importance in our work

Fig. 51 The Turing Way project illustration by Scriberia. Used under a CC-BY 4.0 licence. DOI: 10.5281/zenodo.3332807.

In this guide, we compile best practices and guidance on project designing by including different aspects of project management and agile development practices derived from academia and industry. We hope you will learn from different case studies for small, mid-size, and large projects spanning both short-term and long-term plans. If you are brave enough, your examples of failed projects will be incredibly valuable for understanding how to avoid making the same (totally understandable!) mistakes again.

When designing a team-based project, it is important to think about all the skills required for the project and the resources needed to access those skills. With this in mind, we welcome chapters defining the requirements in a project in terms of up-skilling, supporting and improving accessibility of different stakeholders. Check out our contributing guidelines to get involved.