Database Reference
In-Depth Information
be applied to solve it. This concept is explored further in Chapter 2, “Data
Analytics Lifecycle.”
•
Design, implement, and deploy statistical models and data
mining techniques on Big Data.
This set of activities is mainly what
people think about when they consider the role of the Data Scientist:
namely, applying complex or advanced analytical methods to a variety of
business problems using data. Chapter 3 through Chapter 11 of this topic
introduces the reader to many of the most popular analytical techniques
and tools in this area.
•
Develop insights that lead to actionable recommendations.
It is
critical to note that applying advanced methods to data problems does not
necessarily drive new business value. Instead, it is important to learn how
to draw insights out of the data and communicate them effectively.
Chapter 12, “The Endgame, or Putting It All Together,” has a brief
overview of techniques for doing this.
Data scientists are generally thought of as having five main sets of skills and
behavioral characteristics, as shown in
Figure 1.13
:
•
Quantitative skill:
such as mathematics or statistics
•
Technical aptitude:
namely, software engineering, machine learning,
and programming skills
•
Skeptical mind-set and critical thinking:
It is important that data
scientists can examine their work critically rather than in a one-sided way.
•
Curious and creative:
Data scientists are passionate about data and
finding creative ways to solve problems and portray information.
•
Communicative and collaborative:
Data scientists must be able to
articulate the business value in a clear way and collaboratively work with
other groups, including project sponsors and key stakeholders.
