Comptia Data+ Study Guide - Mike Chapple

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Résumé :

Contents

Introduction xvii

Data 2

Storage 3

Computing Power 5

Careers in Analytics 5

The Analytics Process 6

Data Acquisition 7

Cleaning and Manipulation 7

Analysis 7

Visualization 8

Reporting and Communication 9

Analytics Techniques 10

Descriptive Analytics 10

Inferential Analytics 10

Predictive Analytics 10

Prescriptive Analytics 11

Machine Learning, Arti_ cial Intelligence, and Deep Learning 11

Generative AI 12

Robotic Process Automation 13

Data Governance 13

Analytics Tools 14

Summary 16

Chapter 2 Data Analytics Tools 17

Spreadsheets 18

Microsoft Excel 19

Programming Languages 21

R 21

Python 23

Scala 24

SAS 24

Databases and SQL 26

Business Intelligence Software 29

Power BI 29

Tableau 29

Looker 31

Cloud Infrastructure 32

Drivers for Cloud Computing 32

Cloud Service Concepts 33

Cloud Deployment Models 35

Public Cloud Providers 36

Summary 37

Exam Essentials 37

Review Questions 39

Chapter 3 Understanding Data 43

Exploring Data Types 44

Structured Data Types 46

Unstructured Data Types 58

Categories of Data 63

Common Data Structures 66

Structured Data 66

Unstructured Data 68

Semi-structured Data 69

Common File Formats 70

Text Files 70

JavaScript Object Notation 72

Extensible Markup Language (XML) 74

Hypertext Markup Language (HTML) 75

Summary 76

Exam Essentials 77

Review Questions 78

Chapter 4 Databases and Data Acquisition 83

Exploring Databases 84

The Relational Model 85

Relational Databases 88

Nonrelational Databases 94

Database Use Cases 97

Online Transactional Processing 97

Online Analytical Processing 100

Schema Concepts 101

Data Acquisition Concepts 107

Integration 107

Data Sources and Collection Methods 109

Working with Data 120

Data Manipulation 121

Query Optimization 136

Summary 139

Exam Essentials 140

Review Questions 141

Chapter 5 Data Quality 145

Data Inconsistencies 146

Data Duplication 146

Data Redundancy 147

Missing Values 151

Invalid Data 152

Nonparametric Data 153

Data Outliers 153

Speci_ cation Mismatch 154

Data Type Validation 155

Data Completeness 156

Data Transformation Techniques 156

String Manipulation 156

Conversion 158

Augmentation 160

Scaling 160

Grouping Techniques 162

Reduction 163

Aggregation 166

Transposition 167

Exploding 168

Standardization 168

Imputation 171

Parsing 172

Merging 174

Appending 175

Recoding Data 176

Derived Variables 177

Deletion 178

Data Blending 178

Managing Data Quality 180

Circumstances to Check for Quality 180

Automated Validation 182

Data Quality Dimensions 183

Data Quality Rules and Metrics 185

Methods to Validate Quality 188

Summary 190

Exam Essentials 191

Review Questions 192

Chapter 6 Data Analysis and Statistics 197

Communication Approaches 198

Audience 198

Mock-Up 201

Accessibility 201

Statistical Functions and Measures 204

Mathematical 205

Logical 223

Date 226

String 227

Troubleshooting 229

Issues 229

Tools and Methods 232

Analysis Techniques 234

Determine ...

Biographie:

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....

Sommaire:

Introduction xxi

Assessment Test xxviii

Chapter 1 Today's Data Analyst 1

Chapter 2 Data Analytics Tools 17

Chapter 3 Understanding Data 43

Chapter 4 Databases and Data Acquisition 83

Chapter 5 Data Quality 145

Chapter 6 Data Analysis and Statistics 197

Chapter 7 Data Visualization with Reports and Dashboards 245

Chapter 8 Data Governance 299

Appendix 349

Index 365