CompTIA DataX Study Guide : Exam DY0-001

CompTIA DataX Study Guide Exam DY0-001

Demonstrate your Data Science skills by earning the brand-new CompTIA DataX credential In CompTIA DataX Study Guide: Exam DY0-001, data scientist and analytics professor, Fred Nwanganga, delivers a practical, hands-on guide to establishing your credentials as a data science practitioner and succeedi...

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Detalles Bibliográficos
Autor principal: Nwanganga, Fred (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Colección:Sybex Study Guide Series
Materias:
Information technology > Examinations > Study guides.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009841201106719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Acknowledgments
  • About the Author
  • About the Technical Editor
  • Contents at a Glance
  • Contents
  • Introduction
  • About the DataX Certification
  • How This Book Is Organized
  • Interactive Online Learning Environment and Test Bank
  • How to Contact the Publisher
  • Assessment Test
  • Answers to Assessment Test
  • Chapter 1 What Is Data Science?
  • Data Science
  • Data Science, Machine Learning, and Artificial Intelligence
  • Common Applications of Data Science
  • Data Science Best Practices
  • Data Science Workflow Models
  • Common Tools and Techniques
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 2 Mathematics and Statistical Methods
  • Calculus
  • Derivatives
  • Integrals
  • Probability Distributions
  • Continuous Probability Distributions
  • Discrete Probability Distributions
  • Inferential Statistics
  • Estimating Population Parameters
  • Hypothesis Testing
  • Linear Algebra
  • Vectors
  • Matrices
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 3 Data Collection and Storage
  • Common Data Sources
  • Generated Data
  • Synthetic Data
  • Commercial or Public Data
  • Data Ingestion
  • Data Ingestion Methods
  • Infrastructure Requirements
  • Data Ingestion Pipeline
  • Data Storage
  • Structured Storage
  • Unstructured Storage
  • Semi-Structured Storage
  • Compressed Formats
  • Managing the Data Lifecycle
  • Data Lineage
  • Refresh Cycles
  • Archiving
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 4 Data Exploration and Analysis
  • Exploratory Data Analysis
  • Quantitative Variables
  • Qualitative Variables
  • Univariate Analysis
  • Bivariate Analysis
  • Multivariate Analysis
  • Choosing an Exploratory Data Analysis Method
  • Common Data Quality Issues
  • Structural Issues
  • Temporal Issues
  • Completeness Issues
  • Summary
  • Exam Essentials.
  • Review Questions
  • Chapter 5 Data Processing and Preparation
  • Data Transformation
  • Encoding
  • Scaling and Normalization
  • Transformation Functions
  • Structural Transformation
  • Feature Extraction
  • Data Enrichment and Augmentation
  • Ground Truth Labeling
  • Feature Engineering
  • Merging and Combining Data
  • Data Cleaning
  • Identifying Data Errors
  • Handling Inconsistent Data
  • Addressing Duplicate Data
  • Resolving Missing Data
  • Dealing with Outliers
  • Handling Class Imbalance
  • Undersampling
  • Oversampling
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 6 Modeling and Evaluation
  • Types of Models
  • Regressors
  • Classifiers
  • Temporal Models
  • Model Design Concepts
  • The Holdout Method
  • The Bias-Variance Trade-off
  • Feature Selection
  • Cross-Validation
  • Bootstrapping
  • Hyperparameter Tuning
  • Model Evaluation
  • Regressor Performance Metrics
  • Classifier Performance Metrics
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 7 Model Validation and Deployment
  • Model Validation
  • Performance Metrics
  • Inference Performance
  • Design Constraints
  • Business Requirements Alignment
  • Benchmarking
  • Communicating Results
  • Data
  • Visuals
  • Stakeholders
  • Ethics
  • Accessibility
  • Documentation
  • Model Deployment
  • Containerization
  • Virtualization
  • Cluster Deployment
  • Cloud Deployment
  • On-Premises Deployment
  • Hybrid Deployment
  • Edge Deployment
  • Machine Learning Operations (MLOps)
  • Automation
  • Versioning
  • Testing
  • Monitoring
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 8 Unsupervised Machine Learning
  • Association Rules
  • Identifying Strong Rules
  • Clustering
  • Centroid-Based Clustering
  • Connectivity-Based Clustering
  • Density-Based Clustering
  • Dimensionality Reduction
  • Feature Extraction
  • Recommender Systems
  • Collaborative Filtering.
  • Content-Based Filtering
  • Hybrid Filtering
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 9 Supervised Machine Learning
  • Linear Regression
  • Regularization
  • Logistic Regression
  • Discriminant Analysis
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Naive Bayes
  • Decision Trees
  • Ensemble Methods
  • Bagging
  • Boosting
  • Stacking
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 10 Neural Networks and Deep Learning
  • Artificial Neural Networks
  • Network Topology
  • Activation Function
  • Training Algorithm
  • Deep Neural Networks
  • Common Deep Learning Architectures
  • Common Deep Learning Frameworks
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 11 Natural Language Processing
  • Natural Language Processing
  • Text Analysis
  • Language Understanding
  • Language Generation
  • Text Preparation
  • Tokenization
  • Stemming
  • Lemmatization
  • Removing Stop Words
  • Part-of-Speech (POS) Tagging
  • Spelling Normalization
  • Data Augmentation (Augmenters)
  • Text Representation
  • Vectorization
  • Vector Space Model
  • Word Embeddings
  • Summary
  • Exam Essentials
  • Review Questions
  • Chapter 12 Specialized Applications of Data Science
  • Optimization
  • Decision Variables
  • Objective Function
  • Constraints
  • Constrained Optimization
  • Unconstrained Optimization
  • Computer Vision
  • Image Acquisition
  • Image Preprocessing
  • Feature Extraction
  • Summary
  • Exam Essentials
  • Review Questions
  • Appendix Answers to Review Questions
  • Chapter 1: What Is Data Science?
  • Chapter 2: Mathematics and Statistical Methods
  • Chapter 3: Data Collection and Storage
  • Chapter 4: Data Exploration and Analysis
  • Chapter 5: Data Processing and Preparation
  • Chapter 6: Modeling and Evaluation
  • Chapter 7: Model Validation and Deployment
  • Chapter 8: Unsupervised Machine Learning.
  • Chapter 9: Supervised Machine Learning
  • Chapter 10: Neural Networks and Deep Learning
  • Chapter 11: Natural Language Processing
  • Chapter 12: Specialized Applications of Data Science
  • Index
  • EULA.

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