Problems in Data Science
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Understanding the problem
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“the five-whys”
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Different questions dramatically effect the tools and techniques used to solve the problem.
Data Science as a Process
- More Science than Engineering
- High risk
- High reward
- Difficult
- Unpredictable
By Kenneth Jensen CC BY-SA 3.0, via Wikimedia Commons
Impacts of Data Science
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What is the purpose of the project? Who is affected?
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Which parts of the business are affected? Do we need help?
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You must think about the human concerns.
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You need buy-in from the business; the business will be affected.
Conclusions
Business goals: make money, save money or save time. Data Science generates profit.
Project justifications - you now know how they are judged:
- Alignment with Business Goals
- A well defined, testable requirement
- A robust plan
- Data Understanding
- Data Preparation
- Modelling
- Evaluation
- Deployment
- Iteration of the above
- Buy-in and integration with other parts of the business
However, there are more philanthropic, scientific reasons for undertaking a project too. So these arguments may not directly apply to charitable causes or academia.