Database Reference
In-Depth Information
Summary
This chapter described the Data Analytics Lifecycle, which is an approach to
managing and executing analytical projects. This approach describes the process in
six phases.
1. Discovery
2. Data preparation
3. Model planning
4. Model building
5. Communicate results
6. Operationalize
Through these steps, data science teams can identify problems and perform
rigorous investigation of the datasets needed for in-depth analysis. As stated in the
chapter, although much is written about the analytical methods, the bulk of the time
spent on these kinds of projects is spent in preparation—namely, in Phases 1 and 2
(discovery and data preparation). In addition, this chapter discussed the seven roles
needed for a data science team. It is critical that organizations recognize that Data
Science is a team effort, and a balance of skills is needed to be successful in tackling
Big Data projects and other complex projects involving data analytics.
