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.
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 Statistics 152 Descriptive Statistics 155 Measures of Frequency 155 Measures of Central Tendency 160 Measures of Dispersion 164 Measures of Position 173 Inferential Statistics 175 Confidence Intervals 175 Hypothesis Testing 179 Simple Linear Regression 186 Analysis Techniques 190 Determine Type of Analysis 190 Types of Analysis 191 Exploratory Data Analysis 192 Summary 192 Exam Essentials 194 Review Questions 196 Chapter 6 Data Analytics Tools 201 Spreadsheets 202 Microsoft Excel 203 Programming Languages 205 R 205 Python 206 Structured Query Language (SQL) 208 Statistics Packages 209 IBM SPSS 210 SAS 211 Stata 211 Minitab 212 Machine Learning 212 IBM SPSS Modeler 213 RapidMiner 214 Analytics Suites 217 IBM Cognos 217 Power BI 218 MicroStrategy 219 Domo 220 Datorama 221 AWS QuickSight 222 Tableau 222 Qlik 224 BusinessObjects 225 Summary 225 Exam Essentials 225 Review Questions 227 Chapter 7 Data Visualization with Reports and Dashboards 231 Understanding Business Requirements 232 Understanding Report Design Elements 235 Report Cover Page 236 Executive Summary 237 Design Elements 239 Documentation Elements 244 Understanding Dashboard Development Methods 247 Consumer Types 247 Data Source Considerations 248 Data Type Considerations 249 Development Process 250 Delivery Considerations 250 Operational Considerations 252 Exploring Visualization Types 252 Charts 252 Maps 258 Waterfall 264 Infographic 266 Word Cloud 267 Comparing Report Types 268 Static and Dynamic 268 Ad Hoc 269 Self-Service (On-Demand) 269 Recurring Reports 269 Tactical and Research 270 Summary 271 Exam Essentials 272 Review Questions 274 Chapter 8 Data Governance 279 Data Governance Concepts 280 Data Governance Roles 281 Access Requirements 281 Security Requirements 286 Storage Environment Requirements 289 Use Requirements 291 Entity Relationship Requirements 292 Data Classification Requirements 292 Jurisdiction Requirements 297 Breach Reporting Requirements 298 Understanding Master Data Management 299 Processes 300 Circumstances 301 Summary 303 Exam Essentials 304 Review Questions 306 Appendix Answers to the Review Questions 311 Chapter 2: Understanding Data 312 Chapter 3: Databases and Data Acquisition 314 Chapter 4: Data Quality 315 Chapter 5: Data Analysis and Statistics 317 Chapter 6: Data Analytics Tools 319 Chapter 7: Data Visualization with Reports and Dashboards 322 Chapter 8: Data Governance 323 Index 327
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.
