Wir fragen ChatGPT!

Wie integriert man Data Science im agilen Workflow?

Wir bei Data Science Mania sind konstant auf der Suche nach neuen Ideen und schrecken dabei auch nicht vor modernsten Methoden zurück.
Heute haben wir #OpenAI's #ChatGPT3 gefragt:

DSMania ❓
"describe an agile workflow for data science"


ChatGPT ❗

"In general, an agile workflow for data science involves iteratively testing and building upon ideas, with the goal of quickly delivering high-quality results. This approach is typically characterized by the following steps:

1. Identify the problem or opportunity that the data science project will address.

2. Define the objectives and success criteria for the project.

3. Develop a high-level plan for the project, including a timeline and a breakdown of tasks.

4. Acquire and prepare the data needed for the project. This may involve cleaning, transforming, and augmenting the data to ensure that it is suitable for analysis.

5. Explore the data to gain insights and identify potential patterns and trends. This may involve using statistical and machine learning techniques to build models and make predictions.

6. Iterate on the data analysis, testing and refining ideas as necessary. This may involve working closely with stakeholders to gather feedback and incorporate their insights.

7. Communicate the results of the data analysis in a clear and effective way, using visualizations and other tools to make the findings accessible to a broad audience.

8. Use the insights gained from the data analysis to inform decision-making and drive action. This may involve implementing solutions, making recommendations, or taking other steps to address the problem or opportunity identified in step 1.

Overall, an agile workflow for data science emphasizes collaboration, flexibility, and the rapid iteration of ideas in order to deliver value and drive progress."

**Was meint ihr dazu? Kann #AI mit dem #workflow in eurer Company mithalten?**