Workflow

What is a Data Science Workflow?

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A data science workflow defines the phases (or steps) in a data science project. Using a well-defined data science workflow is useful in that it provides a simple way to remind all data science team members of the work to be done to do a data science project. One way to think about the benefit […]

Coordination Framework

10 Ways to Manage a Data Science Project – Part IV: Emerging Approaches

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TDSP

So are there new emerging approaches that are data science native? Microsoft’s Team Data Science Process (TDSP), Domino Data Lab’s Data Science Life Cycle, and the Data Science Process Alliance’s Data Driven Scrum (DDS) are approaches that are both data science native and agile. There are pros and cons specific to each approach but they share some fundamental principles.

Team

CRISP-DM for Data Science Teams: 5 Actions to Consider

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While there is no standard process for a team to use when working on a data science project, CRISP-DM (CRoss-Industry Standard Process for Data Mining) is one framework that is often considered for data science projects. Perhaps because of this, there are lots of web sites describing the 6 phases of a CRISP-DM project, and […]

Workflow

Data Science Project Roadmap Example

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Example Data Science Project Roadmap

Various process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Lifecycle, or Data Driven Scrum describe how to execute a data science project. While useful, such models do not explicitly explain how to communicate with stakeholders on what they care most about: what deliverables will they get through a project lifecycle. In pre-project […]

Agile

Vertical vs Horizontal Slicing Data Science Deliverables

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layered cake

Traditional software approaches favor developing software layer-by-layer (horizontal slicing) while software agilists strive to deliver software by thin end-to-end value streams (vertical slicing). …but what makes sense for data science? Consider a churn project… Imagine that you are tasked to pro-actively minimize customer churn at a telecom company. The retention department has requested the following […]

Team

Lessons from 20 Data Science Teams

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Data science life cycle per Domino Data Labs

What can you learn if you observe data science teams across 20 large companies? I asked Mac Steele, Director of Product at Domino Data Lab, to find out. Mac combined the lessons he learned from observing data science teams with concepts from CRISP-DM and agile to create the Domino Data Science Lifecycle. It is defined in a 25-page whitepaper, The […]

Data Science Process Choices

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Most IT project management sites focus on software engineering. To our knowledge, there is no comprehensive guide focused on data science. So gathering information from interviews, our industry experience, and third-party sources, we have developed a project management guide dedicated to data science with the goal to arm practitioners with a better understanding of various […]