🔹🔹 A Holistic Framework for Managing Data Analytics Projects

Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.

🔻The Data Science Delivery Process

Data science initiatives are project-oriented, so they have a defined start and end. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a high-level, extensible process that is an effective framework for data science projects.

Although the steps are shown in the general order in which they are executed, it is important to note that CRISP-DM, like the Agile software development process, is an iterative process framework. Each step can be revisited as many times as needed to refine problem understanding and results.
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https://www.kdnuggets.com/2020/05/framework-managing-data-analytics-projects.html

#Agile #CRISP_DM #Data_Analytics #Data_Management #Data_Mining #datascience #Decision_Management, #Development #Software Engineering

👆🏻👆🏻 A Holistic Framework for Managing Data Analytics Projects

🔻 The six CRISP-DM steps are:

1. Business Understanding
2. Data Understanding
3. Data Preparation
4. Modeling
5. Evaluation
6. Deployment
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📌Via: @cedeeplearning
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link: https://www.kdnuggets.com/2020/05/framework-managing-data-analytics-projects.html

#data_management #datamining
#datascience #machinelearning
#preprocessing #agile #project