Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Walter R. Paczkowski, Ph.D. earned his Ph.D. in Economics at Texas A&M University and has worked at AT&T's Analytical Support Center, Market Analysis and Forecasting Division, and Business Research Division. He was also a Member of the Technical Staff at AT&T Bell Labs before founding Data Analytics Corp., a statistical consulting and data modeling company, in 2001. Dr. Paczkowski is a part-time lecturer in the Department of Economics and the Department of Statistics at Rutgers University. He published six books in, what he refers to as, his Analytics Series. His latest are Business Analytics: Data Science for Business Problems (Springer, 2021), Modern Survey Analysis: Using Python for Deeper Insights (Springer, 2022), and Predictive and Simulation Analytics: Deeper Insights for Better Business Decisions (Springer, 2023).
"About this title" may belong to another edition of this title.
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