Artificial Intelligence and Machine Learning: AI Superpowers and Human+Machine a Visionary Revolution in Finance, Medicine and Business. Find out Top Influent People of the Era with a Modern Approach

Develop the analytical skills that are in high demand in businesses today. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals clearly illustrate data and results. Step-by-step instructions guide you through using Excel, Tableau, R or the Python-based Orange data mining software to perform advanced analytics. Practical, relevant problems at all levels of difficulty let you apply what you've learned. Cover Brief Contents Contents About the Authors Preface Chapter 1: Introduction to Business Analytics 1.1 Decision Making 1.2 Business Analytics Defined 1.3 A Categorization of Analytical Methods and Models 1.4 Big Data, the Cloud, and Artificial Intelligence 1.5 Business Analytics in Practice 1.6 Legal and Ethical Issues in the Use of Data and Analytics Summary Glossary Problems Chapter 2: Descriptive Statistics 2.1 Overview of Using Data: Definitions and Goals 2.2 Types of Data 2.3 Exploring Data in Excel 2.4 Creating Distributions from Data 2.5 Measures of Location 2.6 Measures of Variability 2.7 Analyzing Distributions 2.8 Measures of Association between Two Variables Summary Glossary Problems Case Problem 1: Heavenly Chocolates Web Site Transactions Case Problem 2: African Elephant Populations Chapter 3: Data Visualization 3.1 Overview of Data Visualization 3.2 Tables 3.3 Charts 3.4 Specialized Data Visualizations 3.5 Visualizing Geospatial Data 3.6 Data Dashboards Summary Glossary Problems Case Problem 1: Pelican Stores Case Problem 2: Movie Theater Releases Chapter 4: Data Wrangling: Data Management and Data Cleaning Strategies 4.1 Discovery 4.2 Structuring 4.3 Cleaning 4.4 Enriching 4.5 Validating and Publishing Summary Glossary Problems Case Problem 1: Usman Solutions Chapter 5: Probability: An Introduction to Modeling Uncertainty 5.1 Events and Probabilities 5.2 Some Basic Relationships of Probability 5.3 Conditional Probability 5.4 Random Variables 5.5 Discrete Probability Distributions 5.6 Continuous Probability Distributions Summary Glossary Problems Case Problem 1: Hamilton County Judges Case Problem 2: McNeil's Auto Mall Case Problem 3: Gebhardt Electronics Chapter 6: Descriptive Data Mining 6.1 Dimension Reduction 6.2 Cluster Analysis 6.3 Association Rules 6.4 Text Mining Summary Glossary Problems Case Problem 1: Big Ten Expansion Case Problem 2: Know Thy Customer Chapter 7: Statistical Inference 7.1 Selecting a Sample 7.2 Point Estimation 7.3 Sampling Distributions 7.4 Interval Estimation 7.5 Hypothesis Tests 7.6 Big Data, Statistical Inference, and Practical Significance Summary Glossary Problems Case Problem 1: Young Professional Magazine Case Problem 2: Quality Associates, Inc. Chapter 8: Linear Regression 8.1 Simple Linear Regression Model 8.2 Least Squares Method 8.3 Assessing the Fit of the Simple Linear Regression Model 8.4 The Multiple Linear Regression Model 8.5 Inference and Linear Regression 8.6 Categorical Independent Variables 8.7 Modeling Nonlinear Relationships 8.8 Model Fitting 8.9 Big Data and Linear Regression 8.10 Prediction with Linear Regression Summary Glossary Problems Case Problem 1: Alumni Giving Case Problem 2: Consumer Research, Inc. Case Problem 3: Predicting Winnings for NASCAR Drivers Chapter 9: Time Series Analysis and Forecasting 9.1 Time Series Patterns 9.2 Forecast Accuracy 9.3 Moving Averages and Exponential Smoothing 9.4 Using Linear Regression Analysis for Forecasting 9.5 Determining the Best Forecasting Model to Use Summary Glossary Problems Case Problem 1: Forecasting Food and Beverage Case Problem 2: Forecasting Lost Sales Appendix 9.1: Using the Excel Forecast Sheet Chapter 10: Predictive Data Mining: Regression Tasks 10.1 Regression Performance Measures 10.2 Data Sampling, Preparation, and Partitioning 10.3 k-Nearest Neighbors Regression 10.4 Regression Trees 10.5 Neural Network Regression 10.6 Feature Selection Summary Glossary Problems Case Problem: Housing Bubble Chapter 11: Predictive Data Mining: Classification Tasks 11.1 Data Sampling, Preparation, and Partitioning 11.2 Performance Measures for Binary Classification 11.3 Classification with Logistic Regression 11.4 k-Nearest Neighbors Classification 11.5 Classification Trees 11.6 Neural Network Classification 11.7 Feature Selection Summary Glossary Problems Case Problem: Grey Code Corporation Chapter 12: Spreadsheet Models 12.1 Building Good Spreadsheet Models 12.2 What-If Analysis 12.3 Some Useful Excel Functions for Modeling 12.4 Auditing Spreadsheet Models 12.5 Predictive and Prescriptive Spreadsheet Models Summary Glossary Problems Case Problem: Retirement Plan Chapter 13: Monte Carlo Simulation 13.1 Risk Analysis for Sanotronics LLC 13.2 Inventory Policy Analysis for Promus Corp 13.3 Simulation Modeling for Land Shark Inc. 13.4 Simulation with Dependent Random Variables 13.5 Simulation Considerations Summary Glossary Problems Case Problem 1: Four Corners Case Problem 2: Ginsberg's Jewelry Snowfall Promotion Appendix 13.1 Common Probability Distributions for Simulation Chapter 14: Linear Optimization Models 14.1 A Simple Maximization Problem 14.2 Solving the Par, Inc. Problem 14.3 A Simple Minimization Problem 14.4 Special Cases of Linear Program Outcomes 14.5 Sensitivity Analysis 14.6 General Linear Programming Notation and More Examples 14.7 Generating an Alternative Optimal Solution for a Linear Program Summary Glossary Problems Case Problem 1: Investment Strategy Case Problem 2: Solutions Plus Chapter 15: Integer Linear Optimization Models 15.1 Types of Integer Linear Optimization Models 15.2 Eastborne Realty, an Example of Integer Optimization 15.3 Solving Integer Optimization Problems with Excel Solver 15.4 Applications Involving Binary Variables 15.5 Modeling Flexibility Provided by Binary Variables 15.6 Generating Alternatives in Binary Optimization Summary Glossary Problems Case Problem 1: Applecore Children's Clothing Case Problem 2: Yeager National Bank Chapter 16: Nonlinear Optimization Models 16.1 A Production Application: Par, Inc. Revisited 16.2 Local and Global Optima 16.3 A Location Problem 16.4 Markowitz Portfolio Model 16.5 Adoption of a New Product: The Bass Forecasting Model 16.6 Heuristic Optimization Using Excel's Evolutionary Method Summary Glossary Problems Case Problem: Portfolio Optimization with Transaction Costs Chapter 17: Decision Analysis 17.1 Problem Formulation 17.2 Decision Analysis Without Probabilities 17.3 Decision Analysis with Probabilities 17.4 Decision Analysis with Sample Information 17.5 Computing Branch Probabilities with Bayes' Theorem 17.6 Utility Theory Summary Glossary Problems Case Problem 1: Property Purchase Strategy Case Problem 2: Semiconductor Fabrication at Axeon Labs Multi-Chapter Case Problems Appendix A: Basics of Excel Appendix B: Database Basics with Microsoft Access Index ''Present the full range of analytics -- from descriptive and predictive to prescriptive analytics -- with Camm/Cochran/Fry/Ohlmann's market-leading Business analytics 5E. Clear, step-by-step instructions teach students how to use Excel, Tableau, R or the Python-based Orange data mining software to solve more advanced analytics concepts. As instructor, you choose your preferred software for teaching concepts. Extensive solutions to problems and cases save grading time while providing students with critical practice. Updates throughout this edition cover topics beyond the traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's analytical problem solving. In addition, MindTap and WebAssign customizable online learning platforms offer an interactive eBook, auto-graded exercises, algorithmic practice problems and Exploring Analytics visualizations to strengthen students' understanding.''-- Provided by publisher