Business Analytics By Camm Jeffrey D.Cochran, James J.Fry
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Business Analytics By Camm Jeffrey D.Cochran, James J.Fry

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1,036
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34.99 MB
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About this ebook
Business Analytics by Jeffrey D. Camm, James J. Cochran, Michael J. Fry, and Jeffrey W. Ohlmann covers a comprehensive framework of business analytics divided into three core pillars: descriptive, predictive, and prescriptive analytics. [1]
The primary chapters and analytical themes detailed in the Cengage Business Analytics Outline include: [1]
🗒 Core Topics & Chapter Framework
  • Introduction: Foundations of business analytics and decision-making.
  • Descriptive Statistics: Summarizing, cleaning, and understanding data distributions.
  • Data Visualization: Creating effective charts, dashboards, and visual data stories.
  • Probability: Introduction to modeling uncertainty and risk.
  • Descriptive Data Mining: Clustering, association rules, and pattern recognition.
  • Statistical Inference: Hypothesis testing, estimation, and sampling.
  • Linear Regression: Simple and multiple linear regression models.
  • Time Series Analysis and Forecasting: Moving averages, exponential smoothing, and trend analysis.
  • Predictive Data Mining: Classification tokens, logistic regression, and decision trees.
  • Spreadsheet Modeling: Structuring and building effective analytical models.
  • Monte Carlo Simulation: Simulating risk and probabilistic outcomes.
  • Linear Optimization Models: Maximizing or minimizing variables under constraints.
  • Integer Linear Optimization: Handling discrete, non-fractional linear constraints.
  • Nonlinear Optimization: Managing complex, non-linear business functions.
  • Decision Analysis: Payoff tables, decision trees, and utility theory. [1, 2, 3, 4, 5]
📊 Practical Software Implementations
The textbook includes step-by-step technical guides teaching students how to run these analyses across standard software ecosystems: [1, 2, 3, 4]
  • Microsoft Excel: Foundations, macros, and standard optimization solvers.
  • Tableau & Power BI: Advanced interactive data visualizations and dashboard creation.
  • R & Python: Scripting languages for data wrangling, data mining, and advanced pipelines. [1, 2, 3, 4, 5]
✨ Recent Edition Updates (5th & 6th Editions)
If you are studying from the newer editions featured on the Cengage Core Catalog, the curriculum scales up to include: [1, 2, 3]
  • Data Wrangling: Complex data cleaning, missing value treatments, and restructuring.
  • Machine Learning: Expanded algorithmic predictive models.
  • Generative AI & Artificial Intelligence: Modern implementations of AI inside corporate analytical processes. [1, 2, 3, 4]

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Topics

Data Visualization: Creating effective charts dashboards and visual data stories Data Wrangling Machine Learning Decision Analysis: Payoff tables decision trees and utility theory Descriptive Data Mining: Clustering association rules and pattern recognition Descriptive Statistics: Summarizing cleaning and understanding data distributions Generative AI & Artificial Intelligence Integer Linear Optimization: Handling discrete non-fractional linear constraints Introduction: Foundations of business analytics and decision-making Linear Optimization Models: Maximizing or minimizing variables under constraints Linear Regression: Simple and multiple linear regression models Microsoft Excel Tableau & Power BI R & Python Monte Carlo Simulation: Simulating risk and probabilistic outcomes Nonlinear Optimization: Managing complex non-linear business functions Predictive Data Mining: Classification tokens logistic regression and decision trees Probability: Introduction to modeling uncertainty and risk Spreadsheet Modeling: Structuring and building effective analytical models Statistical Inference: Hypothesis testing estimation and sampling Time Series Analysis and Forecasting: Moving averages exponential smoothing and trend analysis
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