A traditional business problem customized here to data science.
1. Identify the problem
2. Identify available data sources
3. Identify if additional data sources are needed
4. Statistical Analyses
5. Implementation, development
6. Communicate results
7. Maintenance
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Thanks! I'm about to start my data science group and this is just what I needed.
thank you for sharing. in a high level, typical IT project. How can this process be altered for the Agile process model?
Great to see in data science perspective
Thanks .
@Rana: The more data you have and the better the quality of data the better your models will be. Both quality and quantity of data are important.
Sam - you are absolutely right. Even the stats models themselves need continuous re-assessment/refresh, in some automated way if possible.
An important aspect of the entire lifecycle is being iterative with the steps mentioned above. The solution is only "stable" until business needs and metrics change, which is almost always continuously. Being able to iterate rapidly will show quick ROI to the business, though accuracy may be fairly low to start with.
Good info. Thanks.
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