Getting familiar with a data science project life cycle

A long-term data science project is somehow never finished. It has its own complete life cycle. This virtuous cycle includes the following steps:

1. Identify the business problem
2. Use data mining and machine learning techniques to Transform the data into actionable information
3. Act on the information
4. Measure the result

Data science is not a product. Data science gives you a platform for continuous learning on how to improve your business. In order to learn how to exploit data mining maximally, you need to measure the results of your actions based on the information extracted with data mining. Measurement provides the feedback for continuously improving results. You can see the life cycle in the following diagram:

Figure 4.1: Data science project life cycle

Let me give you an example. For credit card issuers and online banking, fraud detection is quite a common task. You want to identify fraudulent transactions as quickly as possible to minimize...