Big Data, Analytics, and Data Science Life Cycle - Getting Started with Greenplum for Big Data Analytics - page 15

Data science
Data analytics discussed in the previous section forms an important step in a data
science project. In this section, we will explore the philosophy of data science and the
standard life cycle of a data science project.
Data science is all about turning data into products. It is analytics and machine learn-
ing put into action to draw inferences and insights out of data. Data science is per-
ceived to be an advanced step to business intelligence that considers all aspects of
Big Data.
Data science life cycle
The following diagrams shows the various stages of data science life cycle that in-
cludes steps from data availability/loading to deriving and communicating data in-
sights until operationalizing the process.
Phase 1 - state business problem
This phase is all about discovering the current problem in hand. The problem state-
ment is analyzed and documented in this phase.
In this phase, we identify the key stakeholders and their interests, key pain points,
goals for the project and failure criteria, success criteria, and key risks involved.
Initial hypotheses needs to be formed with the help of domain experts/key stakehold-
ers; this would be the basis against which we would validate the available data. There