The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.
- Demystifies the theory of data analytics using a step-by-step approach
- Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code
- Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools
- Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications
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