Life Cycle Archives - Page 2 of 3 - Data Science Process Alliance

March 21, 2021Last Updated: January 19, 2023Life CycleBy Nick Hotz

KDD and Data Mining

What Is the KDD Process? Dating back to 1989, the namesake Knowledge Discovery in Database (KDD) represents the overall process of collecting data and methodically refining it. The KDD Process is a classic data science life cycle that aspires to purge the ‘noise’ (useless, tangential outliers) while establishing a phased […]

February 28, 2021Last Updated: September 5, 2022Life CycleBy Nick Hotz

What is a Data Science Life Cycle?

A data science life cycle is an iterative set of data science steps you take to deliver a project or analysis. Because every data science project and team are different, every specific data science life cycle is different. However, most data science projects tend to flow through the same general […]

November 30, 2020Last Updated: May 2, 2022Life CycleBy Jeff Saltz

CRISP-DM is Still the Most Popular Framework for Executing Data Science Projects

During the past few months, we conducted a poll to see what project management framework teams used to help execute their data science projects. Based on our survey of 109 respondents, nearly half of the respondents most commonly use CRISP-DM. This was followed by Scrum, Kanban and “My Own”. See results below. […]

October 22, 2020Last Updated: September 5, 2022Life CycleBy Jeff Saltz

What is a Data Science Workflow?

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 […]

Data science life cycle per Domino Data Labs

August 6, 2020Last Updated: May 2, 2022Agile, Life Cycle, Project ManagementBy Nick Hotz

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

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.

data science research and development process

July 26, 2020Last Updated: May 2, 2022Life Cycle, Project ManagementBy Nick Hotz

10 Ways to Manage a Data Science Project – Part III: Hybrid Approaches

Can you mix and match elements of multiple project management approaches? Of course! This post explores two such general hybrid approaches for data science:agile-waterfall and research and development.

June 30, 2020Last Updated: August 1, 2022Life Cycle, Project ManagementBy Nick Hotz

10 Ways to Manage a Data Science Project – Part I: Traditional Approaches

How do you manage data science projects? Is it software? Is it research? Or maybe, simply magic? This four-part post is an overview 10 ways projects are or could be managed. To start, we’ll explore ad hoc project management, waterfall, and CRISP-DM.

May 29, 2020Last Updated: August 1, 2022Life CycleBy Jeff Saltz

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

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 […]

December 27, 2019Last Updated: February 15, 2023Life Cycle, Project ManagementBy Nick Hotz

Data Science Project Roadmap Example

Various process models and frameworks such as CRISP-DM, TDSP, Domino Data Labs Life Cycle, 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 […]

June 25, 2019Last Updated: May 2, 2022Agile, Life CycleBy Nick Hotz

Vertical vs Horizontal Slicing Data Science Deliverables

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 […]