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Prescriptive Analytics: Python and Excel Approaches

Business analytics

ISBN:

978-1-959142-82-9

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Prescriptive Analytics: Python and Excel Approaches

Mark Keith, PhD Randy Bulriss, PhD

Predicting what will happen is only half the job. Prescriptive analytics tells decision makers what to do about it. This resource, which builds on introductory Python and statistics, takes students from retrieving and analyzing data through forecasting, simulation, classification, clustering, and text analytics, then into the optimization models at the heart of prescriptive work: linear programming, distribution and network models, and decision making under uncertainty. Students build their models in both Excel and Python, applying them to real problems in supply chain, finance, marketing, healthcare, and more. They finish with a final project, ready to recommend the best action and minimize risk.

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Table of Contents

Chapter 1: Foreword Chapter 2: Retrieving Data: Web Service APIs Chapter 3: Univariate Statistics Chapter 4: Univariate: Visualizations Chapter 5: Automation: Univariate Analyses Chapter 6: Automation: Bivariate Analyses Chapter 7: Forecasting Chapter 8: Simulation Chapter 9: Classification Chapter 10: Clustering Chapter 11: Text Analytics: Linguistic Features Chapter 12: Text Analytics: Topic Modeling Chapter 13: Linear Programming Chapter 14: Distribution and Network Models Chapter 15: Decision Making Chapter 16: Final Project Supplement: Python: Functions Supplement: Python: Lambda Functions Supplement: Multiple Linear Regression

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