CompTIA Data+ (DA0-002) Cert Prep
7h 32mIntermediate2025-07-03
Authors
Mike Chapple
Teaching Professor at the University of Notre Dame
Course details
In this comprehensive course, expert Mike Chapple helps you prepare for the CompTIA Data+ DA0-002 certification. Mike walks you through the essential skills needed to analyze, prepare, and visualize data, ensuring you’re ready to tackle real-world business challenges. Discover how to transform raw data into actionable insights, create impactful visualizations, and adhere to data governance and compliance standards. Along the way, Mike covers key concepts such as statistical analysis, data quality assurance, and visualization techniques, helping you master the skills needed for certification success. By the end of the course, you’ll be well-equipped to confidently pass the Data+ exam and excel in data-driven roles.
Learning objectives
Prepare for the CompTIA Data+ exam.
Translate business requirements in support of data-driven decisions by acquiring, preparing, and transforming data.
Use industry-standard tools and emerging technologies to create appropriate reports and visualizations.
Apply basic statistical methods and analyze complex data sets while adhering to governance and quality standards throughout the entire data life cycle.
Learning objectives
Prepare for the CompTIA Data+ exam.
Translate business requirements in support of data-driven decisions by acquiring, preparing, and transforming data.
Use industry-standard tools and emerging technologies to create appropriate reports and visualizations.
Apply basic statistical methods and analyze complex data sets while adhering to governance and quality standards throughout the entire data life cycle.
Skills covered
Data VisualizationData AnalysisCert PrepData ScienceBusiness Analysis and StrategyBusiness Software and Tools
Concepts
0. Introduction
- 01 - About the data+ exam
1. The Data+ Exam
- 02 - The Data+ exam
- 03 - Careers in analytics
- 04 - The value of certification
- 05 - Study resources
2. Inside the Data+ Exam
- 06 - In-person exam environment
- 07 - At-home testing
- 08 - Data+ question types
- 09 - Passing the Data+ exam
- 10 - Exam tips
3. Domain 1 - Data Concepts and Environments
- 11 - Overview of the data concepts and environments domain
4. Understanding Data
- 12 - The value of data
- 13 - Structured vs unstructured data
- 14 - Tabular data
- 15 - Key-value pairs
5. Data Types
- 16 - Numeric data
- 17 - Boolean data
- 18 - String data
- 19 - Discrete vs. continuous data
- 20 - Dates and times
- 21 - Multimedia data
- 22 - Spatial data
- 23 - Unique identifiers
6. Data File Formats
- 24 - Text files
- 25 - JSON data
- 26 - HTML data
- 27 - XML data
- 28 - Binary data
7. Data Storage
- 29 - Relational databases
- 30 - OLTP and OLAP
- 31 - Non-relational databases
- 32 - Data schemas
8.Data Sources
- 33 - Public databases
- 34 - Application programming interfaces (APIs)
- 35 - Web scraping
- 36 - Files and logs
9. Cloud Computing
- 37 - What is the cloud
- 38 - Drivers for cloud computing
- 39 - Cloud providers
- 40 - Cloud deployment models
- 41 - Virtualization
- 42 - Containerization
- 43 - Cloud storage options
10. Data Analysis Tools
- 44 - Data analysis tools
- 45 - Microsoft excel
- 46 - Programming languages
- 47 - Coding environments
- 48 - Statistics packages
- 49 - Business intelligence software
- 50 - SQL and database tools
11. Artificial Intelligence
- 51 - AI and analytics
- 52 - Generative AI
- 53 - Robotic process automation
12. Domain 2 - Data Acquisition and Preparation
- 54 - Overview of the data acquisition and preparation domain
13. Data Acquisition and Integration
- 55 - ETL and ELT processes
- 56 - Surveys and observation
- 57 - Sampling
14. SQL Queries
- 58 - Structured query language
- 59 - SELECT statement
- 60 - Sorting results
- 61 - Filtering data
- 62 - NULL values
- 63 - Aggregating data
- 64 - Grouping data
- 65 - String manipulation
- 66 - Working with dates
- 67 - Derived values
- 68 - Set operations
- 69 - Join operations
- 70 - Inner joins
- 71 - Joining multiple tables
- 72 - Outer joins
- 73 - Nested queries
15. Query Optimization
- 74 - Indexing
- 75 - Record subsets
- 76 - Query execution plans
- 77 - Parameterization
16. Identifying Data Inconsistencies
- 78 - Duplicate and redundant data
- 79 - Missing data and completeness
- 80 - Invalid data and outliers
- 81 - Handling outliers
17. Data Transformation and Cleansing
- 82 - Combining datasets
- 83 - Conversion, standardization, and scaling
- 84 - Regular expressions
- 85 - Binning and clustering
- 86 - Data augmentation
- 87 - Exploding data
18. Domain 3 - Data Analysis
- 88 - Overview of the data analysis domain
19. Communicating Data Analyses
- 89 - Communicating to your audience
- 90 - Design considerations
20. Statistics
- 91 - Statistical methods
- 92 - Mean, median, and mode
- 93 - Range and distribution
- 94 - Variance and standard deviation
- 95 - Function types
21. Troubleshooting Analysis
- 96 - Common issues
- 97 - Troubleshooting techniques
22. Domain 4 - Visualization and Reporting
- 98 - Overview of the visualization and reporting domain
23. Visual Elements of Reporting
- 99 - Reporting requirements
- 100 - Cover pages
- 101 - Design elements
- 102 - Documentation elements
24. Visualization Types
- 103 - Line chart
- 104 - Scatterplots and bubble charts
- 105 - Pie charts
- 106 - Bar charts and histograms
- 107 - Waterfall charts
- 108 - Heat maps and geographic maps
- 109 - Tree maps
- 110 - Word clouds and infographics
- 111 - Pivot tables
25. Reports and Dashboards
- 112 - Reports
- 113 - Dashboards
- 114 - Dashboard design
- 115 - Dashboard development
- 116 - Dashboard delivery
26. Troubleshooting Reports
- 117 - Reporting Issues
- 118 - Report validation
27. Domain 5 - Data Governance
- 119 - Overview of the data governance domain
28. Data Governance Programs
- 120 - What is data stewardship
- 121 - Exploring data stewardship roles
- 122 - Qualities of a good data steward
- 123 - Data stewardship responsibilities
29. Database Documentation
- 124 - Data dictionaries
- 125 - Data flow documentation
- 126 - Hierarchy structure
- 127 - Transparency and trust
- 128 - Data versioning
- 129 - Metadata
30. Regulatory Compliance
- 130 - Today's regulatory landscape
- 131 - Health insurance portability and accountability act (HIPAA)
- 132 - Family educational rights and privacy act (FERPA)
- 133 - Gramm leach bliley act (GLBA)
- 134 - Data breach notification laws
- 135 - International data transfers
31. Preserving Individual Privacy
- 136 - Privacy program development
- 137 - Generally accepted privacy principles
- 138 - Data anonymization
- 139 - Data obfuscation
- 140 - Data sharing and transfers
- 141 - Data ethics
32. Protecting Data Security
- 142 - Goals of information security
- 143 - Preserving data confidentiality
- 144 - Building an access management program
- 145 - Authentication, authorization, and accounting
- 146 - Managing the data lifecycle
- 147 - Data classification
- 148 - Control and risk frameworks
- 149 - Audits and assessments
33. Maintaining Data Quality
- 150 - Implementing master data management
- 151 - Developing data definitions
- 152 - Protecting data quality
- 153 - Validating data quality
- 154 - Testing
- 155 - Source control
What's Next
- 156 - Preparing for the exam