CompTIA Data+ Study Guide

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.

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. 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