What is the Microsoft's Team Data Science Process?
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 different kinds 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.
Key components of the TDSP
TDSP comprises of the following key components:- A data science lifecycle definition
- A standardized project structure
- Infrastructure and resources for data science projects
- Tools and utilities for project execution
Data science lifecycle
The Team Data Science Process (TDSP) provides a lifecycle to structure the development of your data science projects. The lifecycle outlines the full steps that successful projects follow.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 improvised analytics projects can also benefit from using this process. But in such cases some of the steps described may not be needed.
The lifecycle outlines the major stages that projects typically execute, often iteratively:
- Business Understanding
- Data Acquisition and Understanding
- Modeling
- Deployment
- Customer Acceptance
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