Data Science Methodology
- Problem Formulation – First, identify the problem to be solved. This step is easily overlooked. However, many dollars and hours have been spent solving the wrong problems.
- Obtain The Data – Next, collect new data and/or gather the data that already exists. In almost all cases, this data will need to be transformed and cleansed. It is important to note that this stage does not always involve big data or a data lake.
- Analysis – This is the part of the process where insight is to be extracted
from the data. Commonly, this step will involve creating and optimizing statistical/machine learning models for prediction, but that is not always necessary. Sometimes, the analysis only contains graphs, charts, and basic descriptions of the data. - Data Product – The end goal of data science is a data product. The insight from the Analysis phase needs to be conveyed to an end user. The data product might be as simple as a slideshow; more commonly it is a website dashboard, a message, an alert, or a recommendation.
Can you think of anything the methodology is missing?
Note: This post is similar to the Data Scientific Method which I blogged about nearly 2 years ago.