CompTIA Data+ Study Guide

Informationen zum Autor ABOUT THE AUTHORS Mike Chapple, PhD, is Teaching Professor of IT, Analytics, and Operations at the University of Notre Dame. He's a technology professional and educator with over 20 years of experience. Mike provides certification resources at his website, CertMike.com. Sharif Nijim is Assistant Teaching Professor of IT, Analytics, and Operations in the Mendoza College of Business at the University of Notre Dame. He teaches undergraduate and graduate courses on cloud computing, business analytics, and information technology. Klappentext Build a solid foundation in data analysis skills and pursue a coveted Data+ certification with this intuitive study guideCompTIA Data+ Study Guide: Exam DA0-001 delivers easily accessible and actionable instruction for achieving data analysis competencies required for the job and on the CompTIA Data+ certification exam. You'll learn to collect, analyze, and report on various types of commonly used data, transforming raw data into usable information for stakeholders and decision makers.With comprehensive coverage of data concepts and environments, data mining, data analysis, visualization, and data governance, quality, and controls, this Study Guide offers: All the information necessary to succeed on the exam for a widely accepted, entry-level credential that unlocks lucrative new data analytics and data science career opportunities 100% coverage of objectives for the NEW CompTIA Data+ exam Access to the Sybex online learning resources, with review questions, full-length practice exam, hundreds of electronic flashcards, and a glossary of key termsIdeal for anyone seeking a new career in data analysis, to improve their current data science skills, or hoping to achieve the coveted CompTIA Data+ certification credential, CompTIA Data+ Study Guide: Exam DA0-001 provides an invaluable head start to beginning or accelerating a career as an in-demand data analyst. Zusammenfassung Build a solid foundation in data analysis skills and pursue a coveted Data+ certification with this intuitive study guideCompTIA Data+ Study Guide: Exam DA0-001 delivers easily accessible and actionable instruction for achieving data analysis competencies required for the job and on the CompTIA Data+ certification exam. You'll learn to collect, analyze, and report on various types of commonly used data, transforming raw data into usable information for stakeholders and decision makers.With comprehensive coverage of data concepts and environments, data mining, data analysis, visualization, and data governance, quality, and controls, this Study Guide offers: All the information necessary to succeed on the exam for a widely accepted, entry-level credential that unlocks lucrative new data analytics and data science career opportunities 100% coverage of objectives for the NEW CompTIA Data+ exam Access to the Sybex online learning resources, with review questions, full-length practice exam, hundreds of electronic flashcards, and a glossary of key termsIdeal for anyone seeking a new career in data analysis, to improve their current data science skills, or hoping to achieve the coveted CompTIA Data+ certification credential, CompTIA Data+ Study Guide: Exam DA0-001 provides an invaluable head start to beginning or accelerating a career as an in-demand data analyst. Inhaltsverzeichnis Introduction xvAssessment Test xxiiChapter 1 Today's Data Analyst 1Welcome to the World of Analytics 2Data 2Storage 3Computing Power 4Careers in Analytics 5The Analytics Process 6Data Acquisition 7Cleaning and Manipulation 7Analysis 8Visualization 8Reporting and Communication 8Analytics Techniques 10Descriptive Analytics 10Predictive Analytics 11Prescriptive Analytics 11Machine Learning, Artificial Intelligence, and Deep Learning 11Data Governance 13Analytics Tools 13Summary 15Chapter 2 Under...

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ABOUT THE AUTHORS

Mike Chapple, PhD, is Teaching Professor of IT, Analytics, and Operations at the University of Notre Dame. He's a technology professional and educator with over 20 years of experience. Mike provides certification resources at his website, CertMike.com. Sharif Nijim is Assistant Teaching Professor of IT, Analytics, and Operations in the Mendoza College of Business at the University of Notre Dame. He teaches undergraduate and graduate courses on cloud computing, business analytics, and information technology.

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Build a solid foundation in data analysis skills and pursue a coveted Data+ certification with this intuitive study guide CompTIA Data+ Study Guide: Exam DA0-001 delivers easily accessible and actionable instruction for achieving data analysis competencies required for the job and on the CompTIA Data+ certification exam. You'll learn to collect, analyze, and report on various types of commonly used data, transforming raw data into usable information for stakeholders and decision makers. With comprehensive coverage of data concepts and environments, data mining, data analysis, visualization, and data governance, quality, and controls, this Study Guide offers: All the information necessary to succeed on the exam for a widely accepted, entry-level credential that unlocks lucrative new data analytics and data science career opportunities 100% coverage of objectives for the NEW CompTIA Data+ exam * Access to the Sybex online learning resources, with review questions, full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms Ideal for anyone seeking a new career in data analysis, to improve their current data science skills, or hoping to achieve the coveted CompTIA Data+ certification credential, CompTIA Data+ Study Guide: Exam DA0-001 provides an invaluable head start to beginning or accelerating a career as an in-demand data analyst.

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Introduction xv Assessment Test xxii Chapter 1 Today's Data Analyst 1 Welcome to the World of Analytics 2 Data 2 Storage 3 Computing Power 4 Careers in Analytics 5 The Analytics Process 6 Data Acquisition 7 Cleaning and Manipulation 7 Analysis 8 Visualization 8 Reporting and Communication 8 Analytics Techniques 10 Descriptive Analytics 10 Predictive Analytics 11 Prescriptive Analytics 11 Machine Learning, Artificial Intelligence, and Deep Learning 11 Data Governance 13 Analytics Tools 13 Summary 15 Chapter 2 Understanding Data 17 Exploring Data Types 18 Structured Data Types 20 Unstructured Data Types 31 Categories of Data 36 Common Data Structures 39 Structured Data 39 Unstructured Data 41 Semi-structured Data 42 Common File Formats 42 Text Files 42 JavaScript Object Notation 44 Extensible Markup Language (XML) 45 HyperText Markup Language (HTML) 47 Summary 48 Exam Essentials 49 Review Questions 51 Chapter 3 Databases and Data Acquisition 57 Exploring Databases 58 The Relational Model 59 Relational Databases 62 Nonrelational Databases 68 Database Use Cases 71 Online Transactional Processing 71 Online Analytical Processing 74 Schema Concepts 75 Data Acquisition Concepts 81 Integration 81 Data Collection Methods 83 Working with Data 88 Data Manipulation 89 Query Optimization 96 Summary 99 Exam Essentials 100 Review Questions 101 Chapter 4 Data Quality 105 Data Quality Challenges 106 Duplicate Data 106 Redundant Data 107 Missing Values 110 Invalid Data 111 Nonparametric data 112 Data Outliers 113 Specification Mismatch 114 Data Type Validation 114 Data Manipulation Techniques 116 Recoding Data 116 Derived Variables 117 Data Merge 118 Data Blending 119 Concatenation 121 Data Append 121 Imputation 122 Reduction 124 Aggregation 126 Transposition 127 Normalization 128 Parsing/String Manipulation 130 Managing Data Quality 132 Circumstances to Check for Quality 132 Automated Validation 136 Data Quality Dimensions 136 Data Quality Rules and Metrics 140 Methods to Validate Quality 142 Summary 144 Exam Essentials 145 Review Questions 146 Chapter 5 Data Analysis and Statistics 151 Fundamentals of Stati…