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Business decisions in any context—operational, tactical, or strategic—can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty.
With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action—what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future.
While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. Author Walter R. Paczkowski, Ph.D. shows you:
The distinction among descriptive, predictive, and prescriptive analytics
How predictive analytics produces a menu of action options
How prescriptive analytics narrows the menu of action options
The forms of prescriptive analytics: eight prescriptive methods
Two broad classes of these methods: non-stochastic and stochastic
How to develop prescriptive analyses for action recommendations
Ways to use an appropriate tool-set in Python
I divided this book into four parts:
Part I, “Introduction and Background”
Part II, “Essential Background Material”
Part III, “Non-Stochastic Prescriptive Analytic Methods”
Part IV, “Stochastic Prescriptive Analytic Methods”
Part I sets the stage for all that will follow. Part II contains two chapters on essential material on Python and probabilities needed for the rest of this book. Part III covers non-stochastic methods for Prescriptive Analytics: Mathematical Programming and Decision Trees. The former is prominent in the Prescriptive Analytics literature, and the latter is a key tool in the Operations Research literature. Part IV introduces stochastic Prescriptive Analytics. The major one is simulations. It is also concerned with two critical parts of a decision menu: the menu options and the probability distribution for the paths leading to the expected value calculations. Finally, an implicit assumption is examined: sequential decisions.
The Book’s Audience:
This book is intended for people involved in demand measurement and forecasting predictive modeling pricing analytics including elasticity estimation customer satisfaction assessment market and advertisement research new product development and research capital investment decisions and any place where these analyses are input into major decisions at the operational, tactical, and strategic levels.
This book will provide background for Prescriptive Analytics by explaining the intuition underlying analytic concepts developing the necessary mathematical and statistical analytic principles demonstrating concepts using Python in JupyterLab notebooks and illustrating analytical concepts with use-cases.
It is also suitable for another audience: students in colleges and universities offering courses and certifications in business data analytics, data sciences, and market research. It could be used as a major or supplemental textbook.