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Prescriptive Analytics: Python and Excel Approaches

Data Analytics

ISBN #978-1-959142-82-9

Prescriptive Analytics: Python and Excel Approaches

Mark Keith, PhD
Randy Bulriss, PhD

Optimize decisions by leveraging data-driven insights to proactively determine the best actions for future success. Build upon your introduction to programming (in Python), statistics, data cleaning, and automating workflows from prior courses. This resource advances analytics skills from distilling descriptive analytics from historical data and anticipating future trends with predictive analytics. Master applying advanced algorithms, decision optimization while minimizing risks with prescriptive analytics using both Excel and Python models.

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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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