CompTIA Data+ DA0-001 Exam Cram

Length: 528 pages
Edition: 1
Language: English
Publisher: Pearson IT Certification
Publication Date: 2023-01-29
ISBN-10: 0137637292
ISBN-13: 9780137637294
Sales Rank: #3389458 (See Top 100 Books)

CompTIA® Data+ DA0-001 Exam Cram is an all-inclusive study guide designed to help you pass the CompTIA Data+ DA0-001 exam. Prepare for test day success with complete coverage of exam objectives and topics, plus hundreds of realistic practice questions. Extensive prep tools include quizzes, Exam Alerts, and our essential last-minute review CramSheet. The powerful Pearson Test Prep practice software provides real-time assessment and feedback with two complete exams.

Covers the critical information needed to score higher on your Data+ DA0-001 exam!

Understand data concepts, environments, mining, analysis, visualization, governance, quality, and controls
Work with databases, data warehouses, database schemas, dimensions, data types, structures, and file formats
Acquire data and understand how it can be monetized
Clean and profile data so it;s more accurate, consistent, and useful
Review essential techniques for manipulating and querying data
Explore essential tools and techniques of modern data analytics
Understand both descriptive and inferential statistical methods
Get started with data visualization, reporting, and dashboards
Leverage charts, graphs, and reports for data-driven decision-making
Learn important data governance concepts

Cover Page
About This eBook
Title Page
Copyright Page
Pearson’s Commitment to Diversity, Equity, and Inclusion
Credits
Contents at a Glance
Table of Contents
About the Author
Dedication
Acknowledgments
About the Technical Editor
We Want to Hear from You!
Reader Services
Introduction
What Is Data?
The Importance of Data
What Is the Importance of Data?
What Are the Sources of Data?
Data Terminology
Target Audience
About the CompTIA Data+ Certification
About This Book
Chapter Format and Conventions
Additional Elements
The Hands-on Approach
Goals for This Book
Chapter 1. Understanding Databases and Data Warehouses
Databases and Database Management Systems
Data Warehouses and Data Lakes
OLTP and OLAP
What Next?
Chapter 2. Understanding Database Schemas and Dimensions
Schema Concepts
Star and Snowflake Schemas
Slowly Changing Dimensions, Keeping Historical Information, and Keeping Current Information
What Next?
Chapter 3. Data Types and Types of Data
Introduction to Data Types
Comparing and Contrasting Different Data Types
Categorical vs. Dimension and Discrete vs. Continuous Data Types
Types of Data: Audio, Video, and Images
What Next?
Chapter 4. Understanding Common Data Structures and File Formats
Structured vs. Unstructured Data
Data File Formats
What Next?
Chapter 5. Understanding Data Acquisition and Monetization
Integration
Data Collection Methods
What Next?
Chapter 6. Cleansing and Profiling Data
Profiling and Cleansing Basics
What Next?
Chapter 7. Understanding and Executing Data Manipulation
Data Manipulation Techniques
What Next?
Chapter 8. Understanding Common Techniques for Data Query Optimization and Testing
Query Optimization
What Next?
Chapter 9. The (Un)Common Data Analytics Tools
Data Analytics Tools
What Next?
Chapter 10. Understanding Descriptive and Inferential Statistical Methods
Introduction to Descriptive and Inferential Analysis
Inferential Statistical Methods
What Next?
Chapter 11. Exploring Data Analysis and Key Analysis Techniques
Process to Determine Type of Analysis
Types of Analysis
What Next?
Chapter 12. Approaching Data Visualization
Business Reports
What Next?
Chapter 13. Exploring the Different Types of Reports and Dashboards
Report Cover Page and Design Elements
Documentation Elements
Dashboard Considerations, Development, and Delivery Process
What Next?
Chapter 14. Data-Driven Decision Making: Leveraging Charts, Graphs, and Reports
Types of Data Visualizations
Reports
What Next?
Chapter 15. Data Governance Concepts: Ensuring a Baseline
Access and Security Requirements
Storage Environment Requirements
Use and Entity Relationship Requirements
Data Classification, Jurisdiction Requirements, and Data Breach Reporting
What Next?
Chapter 16. Applying Data Quality Control
Data Quality Dimensions and Circumstances to Check for Quality
Data Quality Rules and Metrics, Methods to Validate Quality, and Automated Validation
What Next?
Chapter 17. Understanding Master Data Management (MDM) Concepts
Processes
Circumstances for MDM
What Next?
Chapter 18. Getting Ready for the CompTIA Data+ Exam
Getting Ready for the CompTIA Data+ Exam
Tips for Taking the Real Exam
Beyond the CompTIA Data+ Certification
Index
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Tearcard
Code Snippets