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 Elasticity Econometrics Fit method Frequency table Gaussian distribution Heatmap Hypothesis testing K-means clustering Label Encoding Mosaic graph Naive Bayes Outliers Panel data set Statsmodels Training data set Unrestricted model Z-transform Spatial data Time senses analysis