Data Science projects are used to discover patterns in the data that drive a business. Good planning is required to successfully carry out a Data Science project and any other project. Below we describe the next 6 stages that will help in structuring Data Science projects and will lead your project to an effective end.
1. Understanding business
At this stage, we focus on understanding project goals and requirements from a business perspective, and then transforming this knowledge into a definition of the data science problem.
2. Understanding the data
The goal of the stage is to prepare data and assess their suitability. The step begins with data collection and then identifying data quality problems, discovering the insight from data and interesting subsets to formulate hypotheses regarding hidden information.
3. Data preparation
The data preparation phase includes all activities aimed at building the final data set for modeling stage from the initial raw data.
Statistical models are built, selected and checked during this stage. Because some techniques, such as neural networks, have specific data requirements, you may need to go back to the data preparation stage.
Once you’ve built one or more high-quality models based on your chosen features, test them to make sure they are generalized and standardized and that all key business issues have been sufficiently addressed. The end result is the selection of the most relevant model (s).
Essentially, this will mean the implementation of the model code into the operating system in order to assess or categorize new unknown data as it arises, and to create a mechanism to use this new information to solve the original business problem. Importantly, the code must also include all the stages of data preparation leading to modeling, so that the model treats new raw data in the same way as when developing the model.
See real example how we use this methodology for one of the global retail companies to optimize advertising processes in internet.