The Life Cycle of Data Science Projects (1)

Introduction

Data Science is?a hype?topic and data scientists have been?a?hiring buzz?for about several?years and are still?in high demand.?Many companies are invested more in it and expected to reap?business profits. However you will find many?data scientists do not know what a common data science?project?life cycle looks like.?Some?actually play an analyst’s?role and only?do a small portion?of?data science like?generating?analytics report or developing predictive models.

Figure 1: Data Science Life Cycle

As Domino?[3] describes,?Data science is in the throes of a transition from a niche capability to a core capability. What was once a “nice to have” has become a survival imperative.Managing data science projects is still an emerging discipline.?And many companies have?not developed?a?standard process to follow through?and measure the real?impacts.

The artist,?Arshile Gorky, has said,?“Abstraction allows man to see with his mind what he cannot see physically with his eyes....Abstract art enables the artist to perceive beyond the tangible, to extract the infinite out of the finite. It is the emancipation of the mind. It is an exploration into unknown areas.”??Similarly, in practicing data science for efficiency and effectiveness, we also need to apply abstraction and explore the infinite values from the limited information and resources.

With many readings from peers’ articles?and?years of real workexperience in career, I thought through those processes and summarized the six major components in?Figure 1?as a reference to define and develop standards.?In this series, I will introduceand discuss each of the components.