Cognitive Science, Computational Intelligence, and Data Analytics : Methods and Applications with Python

Cognitive Science, Computational Intelligence, and Data Analytics Methods and Applications with Python

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 idea...

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Detalles Bibliográficos
Autor principal: Khare, Vikas (-)
Otros Autores: Dwivedi, Sanjeet Kumar, Bhatia, Monica
Formato: Libro electrónico
Idioma:Inglés
Publicado: San Diego : Elsevier Science & Technology 2024.
Edición:1st ed
Materias:
Python (Computer program language)
Data mining.
Machine learning.
Artificial intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009825842406719
Tabla de Contenidos:
  • Front Cover
  • Cognitive Science, Computational Intelligence, and Data Analytics
  • Copyright Page
  • Contents
  • Foreword
  • Overview of the book
  • 1 Foundation of analytics
  • Abbreviations
  • 1.1 Introduction
  • 1.2 Concepts of analytics
  • 1.2.1 Descriptive analytics
  • 1.2.1.1 Five steps in descriptive analytics
  • 1.2.1.1.1 State business metrics
  • 1.2.1.1.2 Identify data required
  • 1.2.1.1.3 Extract and prepare data
  • 1.2.1.1.4 Analyze data
  • 1.2.1.1.5 Present data
  • 1.2.1.2 Use case of descriptive analytics
  • 1.2.1.3 Advantages of descriptive analytics
  • 1.2.1.4 Disadvantages of descriptive analytics
  • 1.2.2 Diagnostic analytics
  • 1.2.2.1 Use case of diagnostic analytics
  • 1.2.2.2 Advantages of diagnostic analytics
  • 1.2.2.3 Disadvantages of diagnostic analytics
  • 1.2.3 Predictive analytics
  • 1.2.3.1 Use cases of predictive analytics
  • 1.2.3.2 Advantages of predictive analytics
  • 1.2.3.3 Disadvantages of predictive analytics
  • 1.2.4 Prescriptive analytics
  • 1.2.4.1 Use case of prescriptive analytics
  • 1.2.4.2 Advantages of prescriptive analytics
  • 1.2.4.3 Disadvantages of prescriptive analytics
  • 1.3 Emerging areas of analytics
  • 1.3.1 Data analysis
  • 1.3.1.1 Process of data analysis
  • 1.3.2 Business analytics
  • 1.3.2.1 Common component of business analytics
  • 1.3.3 Web analytics and web scrapping
  • 1.3.3.1 Use case of web analytics
  • 1.3.4 Big data analysis
  • 1.3.4.1 Key big data analytics technologies and tools
  • 1.3.4.2 Use case of big data analysis
  • 1.3.4.3 Advantages of big data analysis
  • 1.3.4.4 Disadvantages of big data analysis
  • 1.4 Value chain analysis
  • 1.4.1 Component of value chain analysis
  • 1.4.2 How to conduct value chain analysis?
  • 1.4.3 Value chain with data analytics
  • 1.5 Conclusion
  • 1.6 Case study
  • 1.6.1 Analytics in performance of Mumbai Indians in the IPL.
  • 1.6.1.1 Data analysis based on knowledge discovery in database process
  • 1.6.1.1.1 Steps involved in knowledge discovery in database process
  • 1.6.1.2 Data collection of Mumbai Indians
  • 1.6.1.3 Data assessment of MI Win%
  • 1.6.1.4 Michaelis-Menten curve of Mumbai Indians data
  • 1.6.1.5 Key findings of Mumbai Indians
  • 1.6.2 Analyzing Starbucks' value chain
  • 1.6.2.1 Starbucks' primary activities
  • 1.6.2.2 Starbucks' support activities
  • 1.6.3 Case study on the big data analysis
  • 1.6.3.1 Walmart
  • 1.6.3.2 Uber
  • 1.6.4 Conclusion
  • 1.7 Exercise
  • 1.7.1 Objective type question
  • 1.7.2 Assessment question
  • Further reading
  • 2 Foundation of cognitive science
  • Abbreviations
  • 2.1 Introduction
  • 2.2 Education
  • 2.3 Philosophy
  • 2.4 Artificial intelligence
  • 2.5 Psychology
  • 2.6 Neuroscience
  • 2.7 Theoretical neuroscience
  • 2.8 Linguistics
  • 2.8.1 Role of linguistics in the cognitive science
  • 2.9 Anthropology
  • 2.9.1 Role of anthropology in the cognitive science
  • 2.10 Cognitive science theories
  • 2.11 Evaluation of cognitive science
  • 2.11.1 Significance of cognitive science in the field of data analysis
  • 2.12 Understanding brain and sensory motor information
  • 2.12.1 Main parts of the brain and their functions
  • 2.12.2 Lobes of the brain and what they control
  • 2.12.3 Cranial system
  • 2.12.4 Sensory motor information
  • 2.13 Language and linguistic knowledge
  • 2.14 Theory of information processing
  • 2.14.1 Origins of information processing theory
  • 2.14.2 Elements of information processing theory
  • 2.14.3 Models of information processing theory
  • 2.14.3.1 Atkinson and Shiffrin model
  • 2.14.3.2 Baddeley and Hitch model of working memory
  • 2.15 Concept of short-term memory
  • 2.16 Conclusion
  • 2.17 Case study
  • 2.17.1 Case study: the cognitive science of learning.
  • 2.17.2 Case study: brain and sensory motor information
  • 2.17.3 Case study: language and linguistic knowledge
  • 2.17.4 A case study on the linguistic profile and self perception of multilingual university students by Johnston S.M., Gar...
  • 2.17.5 Case study: the theory of information processing
  • 2.17.6 The following case study is taken from: the Geography Disciplines Network (GDN) Inclusive Curriculum Project (ICP) C...
  • 2.17.7 Case study: short-term memory
  • 2.17.8 Case study: Butterworth B., Campbell R., Howard D., "The Uses of Short-Term Memory: A Case Study," The Quarterly Jou...
  • 2.18 Exercise
  • 2.19 Assessment question
  • Further reading
  • 3 Data theory and taxonomy of data
  • Abbreviations
  • 3.1 Introduction
  • 3.2 Data as a whole
  • 3.2.1 Structured data
  • 3.2.2 Semistructured data
  • 3.2.3 Unstructured data
  • 3.2.4 Quantitative and qualitative data analysis
  • 3.2.4.1 Qualitative data
  • 3.2.4.1.1 Types of qualitative data
  • 3.2.4.1.2 Importance of qualitative data
  • 3.2.4.1.3 Role of qualitative data in the industry 4.0
  • 3.2.4.1.4 Main approaches to qualitative data analysis
  • 3.2.4.1.5 Steps to qualitative data analysis
  • 3.2.4.1.6 Qualitative data collection methods
  • 3.2.4.2 Quantitative data
  • 3.2.4.2.1 Types of Quantitative Data
  • Discrete data
  • Continuous data
  • Interval data
  • Trend analysis
  • Conjoint analysis
  • TURF analysis
  • 3.2.4.2.2 Quantitative data collection methods
  • Probability sampling
  • Surveys/questionnaires
  • Web-based questionnaire
  • Mail questionnaire
  • Observations
  • Document review in quantitative data collection
  • 3.3 Views of data
  • 3.3.1 Types of statistical analysis
  • 3.3.1.1 Descriptive analysis
  • 3.3.1.2 Inferential analysis
  • 3.3.1.3 Predictive analysis
  • 3.3.1.4 Prescriptive analysis
  • 3.3.1.5 Exploratory data analysis
  • 3.3.1.6 Causal analysis.
  • 3.3.1.7 Different statistical method
  • 3.3.1.7.1 Central tendency
  • Mean
  • Why do not use the mean
  • Median
  • Mode
  • Variance and standard deviation
  • Z-score
  • Quartiles
  • Percentile
  • 3.4 Measurement and scaling concepts
  • 3.4.1 Comparative scales
  • 3.4.1.1 Paired comparison scale
  • 3.4.1.2 Rank order scale
  • 3.4.1.3 Constant sum scale
  • 3.4.1.4 Q-sort scale
  • 3.4.2 Non-comparative scales
  • 3.4.2.1 Continuous rating scale
  • 3.4.2.2 Itemized rating scale
  • 3.4.2.2.1 Likert scale
  • 3.4.2.2.2 Stapel scale
  • 3.4.2.2.3 Semantic differential scale
  • 3.5 Various types of scale
  • 3.5.1 Nominal
  • 3.5.2 Ordinal
  • 3.5.3 Interval
  • 3.5.4 Ratio
  • 3.6 Primary data analysis with Python
  • 3.7 Conclusion
  • 3.8 Case study
  • 3.8.1 Case study: taxonomy of data in a healthcare organization
  • 3.8.2 Case study: taxonomy of data in the automobile industry
  • 3.8.3 Case study on the data theory
  • 3.9 Exercise
  • 3.9.1 Objective type question
  • 3.9.2 Descriptive type question
  • Further reading
  • 4 Multivariate data analytics and cognitive analytics
  • Abbreviations
  • 4.1 Introduction
  • 4.2 Factor analytics
  • 4.3 Principal component analytics
  • 4.4 Cluster analytics
  • 4.4.1 K-means
  • 4.4.1.1 Algorithms
  • 4.4.1.2 K-means clustering
  • 4.1.2.1 Steps of the K-means clustering algorithm
  • 4.1.2.2 Practice problems based on K-means clustering algorithm
  • 4.4.2 Cluster analysis of driverless car dataset
  • 4.4.2.1 Problem
  • 4.5 Linear regression analysis
  • 4.5.1 Mathematical expression for regression analysis
  • 4.5.2 Solved example of linear regression analysis of driverless car
  • 4.5.2.1 Problem
  • 4.5.2.2 Solution
  • 4.6 Logistic regression analysis
  • 4.7 Application of analytics across value chain
  • 4.8 Multivariate data analytics with Python
  • 4.9 Conclusion
  • 4.10 Case study.
  • 4.10.1 Case study: factor analysis for customer satisfaction in a hotel chain
  • 4.10.1.1 Introduction
  • 4.10.1.2 Data collection
  • 4.10.1.3 Data analysis
  • 4.10.2 Case study: regression analysis in real estate market
  • 4.10.2.1 Introduction
  • 4.10.2.2 Background
  • 4.10.2.3 Data collection
  • 4.10.2.4 Regression analysis
  • 4.11 Exercise
  • 4.11.1 Objective type question
  • 4.11.2 Descriptive type question
  • Further reading
  • 5 Artificial intelligence and machine learning application in data analysis
  • Abbreviations
  • 5.1 Introduction
  • 5.2 Fundamentals of artificial intelligence
  • 5.2.1 Natural language processing
  • 5.2.2 Robotics
  • 5.2.3 Expert systems
  • 5.3 Spectrum of artificial intelligence
  • 5.3.1 Reactive machines
  • 5.3.2 Limited memory
  • 5.3.3 Theory of mind
  • 5.3.4 Self-aware
  • 5.4 Knowledge representation of artificial intelligence
  • 5.4.1 Symbolic representation
  • 5.4.2 Semantic networks
  • 5.4.3 Frames
  • 5.4.4 Ontologies
  • 5.4.5 Types of knowledge
  • 5.4.5.1 Declarative knowledge
  • 5.4.5.2 Heuristic knowledge
  • 5.4.5.3 Procedural knowledge
  • 5.4.5.4 Structural knowledge
  • 5.4.5.5 Meta knowledge
  • 5.4.6 Knowledge cycle of artificial intelligence
  • 5.5 Constraint satisfaction problem
  • 5.5.1 Map coloring with constraint satisfaction problems
  • 5.5.2 Job shop scheduling with constraint satisfaction problem
  • 5.5.3 Crypt arithmetic
  • 5.5.4 The Wumpus world
  • 5.6 Cognitive analysis with artificial intelligence
  • 5.7 Conclusion
  • 5.8 Case study
  • 5.8.1 Case study: knowledge representation in artificial intelligence-healthcare diagnosis
  • 5.8.1.1 Introduction
  • 5.8.1.2 Problem statement
  • 5.8.1.3 Knowledge representation approach
  • 5.8.1.4 Rule-based systems
  • 5.8.1.5 Probabilistic models
  • 5.8.1.6 Ontologies
  • 5.8.1.7 Knowledge graphs
  • 5.8.1.8 Case-based reasoning
  • 5.8.1.9 Benefits and outcomes.
  • 5.8.1.10 Conclusion.

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