CompTIA Data+ Study Guide: Exam DA0-001

Description

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 opportunities100% coverage of objectives for the NEW CompTIA Data+ examAccess 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. Cover Title Page Copyright Page Contents at a Glance Contents Introduction The Data+ Exam Taking the Exam After the Data+ Exam What Does This Book Cover? Study Guide Elements Interactive Online Learning Environment and Test Bank Exam DA0-001 Exam Objectives DA0-001 Certification Exam Objective Map Assessment Test Answers to Assessment Test Chapter 1 Today’s Data Analyst Welcome to the World of Analytics Data Storage Computing Power Careers in Analytics The Analytics Process Data Acquisition Cleaning and Manipulation Analysis Visualization Reporting and Communication Analytics Techniques Descriptive Analytics Predictive Analytics Prescriptive Analytics Machine Learning, Artificial Intelligence, and Deep Learning Data Governance Analytics Tools Summary Chapter 2 Understanding Data Exploring Data Types Structured Data Types Unstructured Data Types Categories of Data Common Data Structures Structured Data Unstructured Data Semi-structured Data Common File Formats Text Files JavaScript Object Notation Extensible Markup Language (XML) HyperText Markup Language (HTML) Summary Exam Essentials Review Questions Chapter 3 Databases and Data Acquisition Exploring Databases The Relational Model Relational Databases Nonrelational Databases Database Use Cases Online Transactional Processing Online Analytical Processing Schema Concepts Data Acquisition Concepts Integration Data Collection Methods Working with Data Data Manipulation Query Optimization Summary Exam Essentials Review Questions Chapter 4 Data Quality Data Quality Challenges Duplicate Data Redundant Data Missing Values Invalid Data Nonparametric data Data Outliers Specification Mismatch Data Type Validation Data Manipulation Techniques Recoding Data Derived Variables Data Merge Data Blending Concatenation Data Append Imputation Reduction Aggregation Transposition Normalization Parsing/String Manipulation Managing Data Quality Circumstances to Check for Quality Automated Validation Data Quality Dimensions Data Quality Rules and Metrics Methods to Validate Quality Summary Exam Essentials Review Questions Chapter 5 Data Analysis and Statistics Fundamentals of Statistics Common Symbols in Statistics Descriptive Statistics Measures of Frequency Measures of Central Tendency Measures of Dispersion Measures of Position Inferential Statistics Confidence Intervals Hypothesis Testing Simple Linear Regression Analysis Techniques Determine Type of Analysis Types of Analysis Exploratory Data Analysis Summary Exam Essentials Review Questions Chapter 6 Data Analytics Tools Spreadsheets Microsoft Excel Programming Languages R Python Structured Query Language (SQL) Statistics Packages IBM SPSS SAS Stata Minitab Machine Learning IBM SPSS Modeler RapidMiner Analytics Suites IBM Cognos Power BI MicroStrategy Domo Datorama AWS QuickSight Tableau Qlik BusinessObjects Summary Exam Essentials Review Questions Chapter 7 Data Visualization with Reports and Dashboards Understanding Business Requirements Understanding Report Design Elements Report Cover Page Executive Summary Design Elements Documentation Elements Understanding Dashboard Development Methods Consumer Types Data Source Considerations Data Type Considerations Development Process Delivery Considerations Operational Considerations Exploring Visualization Types Charts Maps Waterfall Infographic Word Cloud Comparing Report Types Static and Dynamic Ad Hoc Self-Service (On-Demand) Recurring Reports Tactical and Research Summary Exam Essentials Review Questions Chapter 8 Data Governance Data Governance Concepts Data Governance Roles Access Requirements Security Requirements Storage Environment Requirements Use Requirements Entity Relationship Requirements Data Classification Requirements Jurisdiction Requirements Breach Reporting Requirements Understanding Master Data Management Processes Circumstances Summary Exam Essentials Review Questions Appendix Answers to the Review Questions Chapter 2: Understanding Data Chapter 3: Databases and Data Acquisition Chapter 4: Data Quality Chapter 5: Data Analysis and Statistics Chapter 6: Data Analytics Tools Chapter 7: Data Visualization with Reports and Dashboards Chapter 8: Data Governance Index EULA