This book presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. The author first covers core descriptive and inferential statistics for analytics and then enhances numerical statistical techniques with symbolic artificial intelligence and machine learning techniques for richer predictive and prescriptive analytics. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.
This book presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. The author first covers core descriptive and inferential statistics for analytics and then enhances numerical statistical techniques with symbolic artificial intelligence and machine learning techniques for richer predictive and prescriptive analytics. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.
Subrata Das is the founder and president of Machine Analytics and also serves as a consulting scientist to other companies. He has many years of experience in industrial, government, and academic research and development. He earned his Ph.D. in computer science and master's in mathematics.
Inhaltsangabe
Analytics Background and Architectures. Mathematical and Statistical Preliminaries. Statistics for Descriptive Analytics. Bayesian Probability and Inference. Inferential Statistics and Predictive Analytics. Artificial Intelligence for Symbolic Analytics. Probabilistic Graphical Modeling. Decision Support and Prescriptive Analytics. Time Series Modeling and Forecasting. Monte Carlo Simulation. Cluster Analysis and Segmentation. Machine Learning for Analytics Models. Unstructured Data and Text Analytics. Semantic Web. Analytics Tools. Appendices. Index.
Analytics Background and Architectures. Mathematical and Statistical Preliminaries. Statistics for Descriptive Analytics. Bayesian Probability and Inference. Inferential Statistics and Predictive Analytics. Artificial Intelligence for Symbolic Analytics. Probabilistic Graphical Modeling. Decision Support and Prescriptive Analytics. Time Series Modeling and Forecasting. Monte Carlo Simulation. Cluster Analysis and Segmentation. Machine Learning for Analytics Models. Unstructured Data and Text Analytics. Semantic Web. Analytics Tools. Appendices. Index.
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