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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

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

CompTIA Data+ Exam Certification Details:

Duration	90 mins
Exam Price	$239 (USD)
Exam Code	DA0-001
Number of Questions	90
Schedule Exam	Pearson VUE
Exam Name	CompTIA Data+
Sample Questions	CompTIA Data+ Sample Questions
Books / Training	CompTIA Data+ Certification Training
Passing Score	675 / 900

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