The Team data science Process (TDSP) is an agile, iterative data science methodology to deliver predictive analytics solutions and intelligent applications efficiently. TDSP helps improve team collaboration and learning. It contains a distillation of the best practices and structures from Microsoft and others in the industry that facilitate the successful implementation of data science initiatives. The goal is to help companies fully realize the benefits of their analytics program.
This article provides an overview of TDSP and its main components. We provide a generic description of the process here that can be implemented with a variety of tools. A more detailed description of the project tasks and roles involved in the lifecycle of the process is provided in additional linked topics. Guidance on how to implement the TDSP using a specific set of Microsoft tools and infrastructure that we use to implement the TDSP in our teams is also provided.
TDSP comprises of the following key components:
The Team Data Science Process (TDSP) provides a lifecycle to structure the development of your data science projects. The lifecycle outlines the steps, from start to finish, that projects usually follow when they are executed.
If you are using another data science lifecycle, such as CRISP-DM, KDD or your organization's own custom process, you can still use the task-based TDSP in the context of those development lifecycles. At a high level, these different methodologies have much in common.
This lifecycle has been designed for data science projects that ship as part of intelligent applications. These applications deploy machine learning or artificial intelligence models for predictive analytics. Exploratory data science projects or ad hoc analytics projects can also benefit from using this process. But in such cases some of the steps described may not be needed.
The TDSP lifecycle is composed of five major stages that are executed iteratively:
Here is a visual representation of the Team Data Science Process lifecycle.
The goals, tasks, and documentation artifacts for each stage of the lifecycle in TDSP are described in the Team Data Science Process lifecycle topic. These tasks and artifacts are associated with project roles:
The following diagram provides a grid view of the tasks (in blue) and artifacts (in green) associated with each stage of the lifecycle (on the horizontal axis) for these roles (on the vertical axis).
Having all projects share a directory structure and use templates for project documents makes it easy for the team members to find information about their projects. All code and documents are stored in a version control system (VCS) like Git, TFS, or Subversion to enable team collaboration.
