Paczkowski, Walter R.

Business analytics : data science for business problems - Cham : Springer, 2021 - xxxviii, 387 p. ; ill., 25 cm

Include bibliographic references and index.

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.


Decision making
Mathematical models
Strategic planning
Business intelligence
Accuracy report
Autoregressive model(AR)
Cross-sectional data
Decision trees
Disturbance term
Fit method
Frequency table
Gaussian distribution
Hypothesis testing
K-means clustering
Label Encoding
Mosaic graph
Naive Bayes
Panel data set
Training data set
Unrestricted model
Spatial data
Time senses analysis

658.4033 / PAC

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