Database Reference
In-Depth Information
Chapter 3. Advanced
Analytics
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Paradigms, Tools, and Techniques
Welcome to the world of analytics!
In this chapter, we will learn and recap important analytic techniques or methods that
data scientists employ and practice as a part of a data science project implement-
ation. For each of the analytic techniques, we will set the context for its application
and detail the expected outcome. Additionally, we will learn how to apply R, Weka,
MADlib, and Hadoop frameworks and tools for analytics in general as well as in the
context of Greenplum.
The following topics are covered in this chapter:
• Introduction to standard analytic paradigms: descriptive, predictive, and pre-
scriptive analytics
• Dive deep into important analytical methods: simulations, clustering, data min-
ing, text analytics, decision trees, association rules, linear and logistic regres-
sion, and so on
• Technology and tools:
• R programming
• Weka
• In-database analytics using MADlib
Analytic paradigms
The following figure depicts the journey of data from being mere data to bringing busi-
ness insights for competitive advantage and various analytic paradigms driving the
transformation:
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