Synopsis
Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.
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
About the Authors
Vikas Khare, PhD, is an associate professor in the School of Technology, Management and Engineering, NMIMS, India, and is a certified energy manager in the Bureau of Energy Efficiency in India. He is a fellow member of Scholars Academic and Scientific Society, India. Dr. Khare’s main research interests are renewable energy systems, data analysis, artificial intelligence, optimization techniques, game theory, and big data.
Sanjeet Kumar Dwivedi, PhD, is an adjunct professor at Curtin University, Perth, Australia and is working as a scrum master and senior R&D engineer in control engineering, application, and motor control for Danfoss Power Electronics, Graasten, Denmark since 2008, where he is conducting research on new control techniques in power electronics and motor drives. He has authored more than 40 technical papers and holds 3 international patents.
Monica Bhatia, PhD, is a faculty member at the School of Business Management, NMIMS, India. She holds an MBA and a PhD and is certified by NET, NCFM, and IRDA. Dr. Bhatia has extensive professional experience in training and business development within
both corporate and educational sectors, covering a wide range of areas such as renewable energy, operational management, data analysis, cognitive science, career development, marketing management, communication skills, interpersonal relations, and workplace professionalism. She has conducted life skills training for students and trainers from various nationalities and with diverse accents, and has been involved in consulting projects on e-commerce, product strategy, marketing strategy, and business processes.
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