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CompTIA DA0-001 Exam Syllabus Topics:

Topic	Details
Data Concepts and Environments - 15%
Identify basic concepts of data schemas and dimensions.	- Databases Relational Non-relational - Data mart/data warehousing/data lake Online transactional processing (OLTP) Online analytical processing (OLAP) - Schema concepts Snowflake Star - Slowly changing dimensions Keep current information Keep historical and current information
Compare and contrast different data types.	- Date - Numeric - Alphanumeric - Currency - Text - Discrete vs. continuous - Categorical/dimension - Images - Audio - Video
Compare and contrast common data structures and file formats.	- Structures Structured - Defined rows/columns - Key value pairs Unstructured - Undefined fields - Machine data - Data file formats Text/Flat file - Tab delimited - Comma delimited JavaScript Object Notation (JSON) Extensible Markup Language (XML) Hypertext Markup Language (HTML)
Data Mining - 25%
Explain data acquisition concepts.	- Integration Extract, transform, load (ETL) Extract, load, transform (ELT) Delta load Application programming interfaces (APIs) - Data collection methods Web scraping Public databases Application programming interface (API)/web services Survey Sampling Observation
Identify common reasons for cleansing and profiling datasets.	- Duplicate data - Redundant data - Missing values - Invalid data - Non-parametric data - Data outliers - Specification mismatch - Data type validation
Given a scenario, execute data manipulation techniques.	- Recoding data Numeric Categorical - Derived variables - Data merge - Data blending - Concatenation - Data append - Imputation - Reduction/aggregation - Transpose - Normalize data - Parsing/string manipulation
Explain common techniques for data manipulation and query optimization.	- Data manipulation Filtering Sorting Date functions Logical functions Aggregate functions System functions - Query optimization Parametrization Indexing Temporary table in the query set Subset of records Execution plan
Data Analysis - 23%
Given a scenario, apply the appropriate descriptive statistical methods.	- Measures of central tendency Mean Median Mode - Measures of dispersion Range Max Min Distribution Variance Standard deviation - Frequencies/percentages - Percent change - Percent difference - Confidence intervals
Explain the purpose of inferential statistical methods.	- t-tests - Z-score - p-values - Chi-squared - Hypothesis testing Type I error Type II error - Simple linear regression - Correlation
Summarize types of analysis and key analysis techniques.	- Process to determine type of analysis Review/refine business questions Determine data needs and sources to perform analysis Scoping/gap analysis - Type of analysis Trend analysis - Comparison of data over time Performance analysis - Tracking measurements against defined goals - Basic projections to achieve goals Exploratory data analysis - Use of descriptive statistics to determine observations Link analysis - Connection of data points or pathway
Identify common data analytics tools.	- Structured Query Language (SQL) - Python - Microsoft Excel - R - Rapid mining - IBM Cognos - IBM SPSS Modeler - IBM SPSS - SAS - Tableau - Power BI - Qlik - MicroStrategy - BusinessObjects - Apex - Dataroma - Domo - AWS QuickSight - Stata - Minitab
Visualization - 23%
Given a scenario, translate business requirements to form a report.	- Data content - Filtering - Views - Date range - Frequency - Audience for report Distribution list
Given a scenario, use appropriate design components for reports and dashboards.	- Report cover page Instructions Summary - Observations and insights - Design elements Color schemes Layout Font size and style Key chart elements - Titles - Labels - Legends Corporate reporting standards/style guide - Branding - Color codes - Logos/trademarks - Watermark - Documentation elements Version number Reference data sources Reference dates - Report run date - Data refresh date - Frequently asked questions (FAQs) - Appendix
Given a scenario, use appropriate methods for dashboard development.	- Dashboard considerations Data sources and attributes - Field definitions - Dimensions - Measures Continuous/live data feed vs. static data Consumer types - C-level executives - Management - External vendors/stakeholders - General public - Technical experts - Development process Mockup/wireframe - Layout/presentation - Flow/navigation - Data story planning Approval granted Develop dashboard Deploy to production Delivery considerations Subscription Scheduled delivery Interactive (drill down/roll up) - Saved searches - Filtering - Static - Web interface - Dashboard optimization - Access permissions
Given a scenario, apply the appropriate type of visualization.	- Line chart - Pie chart - Bubble chart - Scatter plot - Bar chart - Histogram - Waterfall - Heat map - Geographic map - Tree map - Stacked chart - Infographic - Word cloud
Compare and contrast types of reports.	- Static vs. dynamic reports Point-in-time Real time - Ad-hoc/one-time report - Self-service/on demand - Recurring reports Compliance reports (e.g., financial, health, and safety) Risk and regulatory reports Operational reports [e.g., performance, key performance indicators (KPIs)] - Tactical/research report
Data Governance, Quality, and Controls - 14%
Summarize important data governance concepts.	- Access requirements Role-based User group-based Data use agreements Release approvals - Security requirements Data encryption Data transmission De-identify data/data masking - Storage environment requirements Shared drive vs. cloud based vs. local storage - Use requirements Acceptable use policy Data processing Data deletion Data retention - Entity relationship requirements Record link restrictions Data constraints Cardinality - Data classification Personally identifiable information (PII) Personal health information (PHI) Payment card industry (PCI) - Jurisdiction requirements Impact of industry and governmental regulations - Data breach reporting Escalate to appropriate authority
Given a scenario, apply data quality control concepts.	- Circumstances to check for quality Data acquisition/data source Data transformation/intrahops - Pass through - Conversion Data manipulation Final product (report/dashboard, etc.) - Automated validation Data field to data type validation Number of data points - Data quality dimensions Data consistency Data accuracy Data completeness Data integrity Data attribute limitations - Data quality rule and metrics Conformity Non-conformity Rows passed Rows failed - Methods to validate quality Cross-validation Sample/spot check Reasonable expectations Data profiling Data audits
Explain master data management (MDM) concepts.	- Processes Consolidation of multiple data fields Standardization of data field names Data dictionary - Circumstances for MDM Mergers and acquisitions Compliance with policies and regulations Streamline data access

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To prepare for the CompTIA DA0-001 exam, candidates can take advantage of various study resources, such as online courses, study guides, and practice exams. It is important to have a solid understanding of data management concepts and principles, as well as practical experience working with data, before attempting the exam. With proper preparation and study, candidates can increase their chances of passing the exam and achieving the CompTIA Data+ Certification.

To earn the CompTIA DA0-001 Certification, candidates must pass a single exam that covers various topics. These topics include data analysis methodologies and techniques, data visualization and reporting, data quality management, and statistical analysis. Candidates must demonstrate knowledge of these topics and practical skills required for effective data analysis in various sectors such as healthcare, finance, and education.

Preparing for the CompTIA DA0-001 exam requires a combination of study and practice. Candidates can use a variety of resources, including books, online courses, and practice exams, to prepare for the exam. It is also recommended that candidates gain hands-on experience working with data in a real-world environment to reinforce their understanding of the concepts covered in the exam.

Reference: https://www.comptia.org/training/books/data-da0-001-study-guide