CompTIA Data+ Study Guide

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Exam DA0-002

Häftad, Engelska, 2025

Av Mike Chapple, Sharif Nijim, Mike (University of Notre Dame) Chapple, Sharif (University of Notre Dame) Nijim

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Prepare for the CompTIA Data+ exam, as well as a new career in data science, with this effective study guide In the newly revised second edition of CompTIA Data+ Study Guide: Exam DA0-002, veteran IT professionals Mike Chapple and Sharif Nijim provide a powerful, one-stop resource for anyone planning to pursue the CompTIA Data+ certification and go on to an exciting new career in data science. The authors walk you through the info you need to succeed on the exam and in your first day at a data science-focused job. Complete with two online practice tests, this book comprehensively covers every objective tested by the updated DA0-002 exam, including databases and data acquisition, data quality, data analysis and statistics, data visualization, and data governance. You'll also find: Efficient and comprehensive content, helping you get up-to-speed as quickly as possibleBite-size chapters that break down essential topics into manageable and accessible lessonsComplimentary access to Sybex' famous online learning environment, with practice questions, a complete glossary of common industry terminology, hundreds of flashcards, and moreA practical and hands-on pathway to the CompTIA Data+ certification, as well as a new career in data science, the CompTIA Data+ Study Guide, Second Edition, offers the foundational knowledge, skills, and abilities you need to get started in an exciting and rewarding new career.

Produktinformation

Utgivningsdatum2025-10-21
FormatHäftad
SpråkEngelska
SerieSybex Study Guide
Antal sidor416
Upplaga2
FörlagJohn Wiley & Sons Inc
ISBN9781394320912

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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 an Associate Teaching Professor of IT, Analytics, and Operations at Notre Dame’s Mendoza College of Business, where he also serves as the Academic Co-Director of the Master of Science in Business Analytics program.

ContentsIntroduction xviiData 2Storage 3Computing Power 5Careers in Analytics 5The Analytics Process 6Data Acquisition 7Cleaning and Manipulation 7Analysis 7Visualization 8Reporting and Communication 9Analytics Techniques 10Descriptive Analytics 10Inferential Analytics 10Predictive Analytics 10Prescriptive Analytics 11Machine Learning, Arti_ cial Intelligence, and Deep Learning 11Generative AI 12Robotic Process Automation 13Data Governance 13Analytics Tools 14Summary 16Chapter 2 Data Analytics Tools 17Spreadsheets 18Microsoft Excel 19Programming Languages 21R 21Python 23Scala 24SAS 24Databases and SQL 26Business Intelligence Software 29Power BI 29Tableau 29Looker 31Cloud Infrastructure 32Drivers for Cloud Computing 32Cloud Service Concepts 33Cloud Deployment Models 35Public Cloud Providers 36Summary 37Exam Essentials 37Review Questions 39Chapter 3 Understanding Data 43Exploring Data Types 44Structured Data Types 46Unstructured Data Types 58Categories of Data 63Common Data Structures 66Structured Data 66Unstructured Data 68Semi-structured Data 69Common File Formats 70Text Files 70JavaScript Object Notation 72Extensible Markup Language (XML) 74Hypertext Markup Language (HTML) 75Summary 76Exam Essentials 77Review Questions 78Chapter 4 Databases and Data Acquisition 83Exploring Databases 84The Relational Model 85Relational Databases 88Nonrelational Databases 94Database Use Cases 97Online Transactional Processing 97Online Analytical Processing 100Schema Concepts 101Data Acquisition Concepts 107Integration 107Data Sources and Collection Methods 109Working with Data 120Data Manipulation 121Query Optimization 136Summary 139Exam Essentials 140Review Questions 141Chapter 5 Data Quality 145Data Inconsistencies 146Data Duplication 146Data Redundancy 147Missing Values 151Invalid Data 152Nonparametric Data 153Data Outliers 153Speci_ cation Mismatch 154Data Type Validation 155Data Completeness 156Data Transformation Techniques 156String Manipulation 156Conversion 158Augmentation 160Scaling 160Grouping Techniques 162Reduction 163Aggregation 166Transposition 167Exploding 168Standardization 168Imputation 171Parsing 172Merging 174Appending 175Recoding Data 176Derived Variables 177Deletion 178Data Blending 178Managing Data Quality 180Circumstances to Check for Quality 180Automated Validation 182Data Quality Dimensions 183Data Quality Rules and Metrics 185Methods to Validate Quality 188Summary 190Exam Essentials 191Review Questions 192Chapter 6 Data Analysis and Statistics 197Communication Approaches 198Audience 198Mock-Up 201Accessibility 201Statistical Functions and Measures 204Mathematical 205Logical 223Date 226String 227Troubleshooting 229Issues 229Tools and Methods 232Analysis Techniques 234Determine Type of Analysis 235Types of Analysis 235Exploratory Data Analysis 236Summary 237Exam Essentials 239Review Questions 240Chapter 7 Data Visualization with Reports and_Dashboards 245Exploring Visualization Elements 246Charts 246Maps 252Pivot Tables 255Infographic 258Waterfall 259Word Cloud 263Understanding Business Requirements 263Understanding Design Elements 267Cover Page 268Executive Summary 269Branding 269Documentation Elements 277Understanding Dashboard Development Methods 279Consumer Types 279Data Source Considerations 280Data Type Considerations 281Development Process 282Operational Considerations 282Delivery Considerations 283Static and Dynamic Delivery 283Frequency 284Data Versioning Techniques 286Tactical and Research 287Report Validation Techniques 288Issues 288Techniques 289Reviews 289Source Validation 290Data Structures 290Monitoring Alerts 291Summary 291Exam Essentials 293Review Questions 295Chapter 8 Data Governance 299Data Management Concepts 300Integration 301Documentation 302Source of Truth 309Data Versioning 313Metadata 313Data Governance Roles 313Data Compliance Concepts 315National Institute of Standards and Technology (NIST) 315Retention 316Jurisdictional Requirements 316Replication 317Storage 318Data Ethics 318Payment Card Industry (PCI) 319Personal Identi_ able Information (PII) 320Protected Health Information (PHI) 320Audit 323Classi_ cation 323Incident Reporting 325Data Privacy and Protection 325Role-Based Access Control (RBAC) 326Encryption 327Masking 332Anonymization 332Data Usage 333Data Sharing 334Data Quality Assurance Practices 334International Organization for Standardization (ISO) 335Source Control 335Unit Test 336Requirement Testing 337Stress Test 337User Acceptance Testing (UAT) 338Data Health Check 339Automated Data Quality Monitoring 339Data Pro_ ling 341Summary 342Exam Essentials 344Review Questions 345Index 349