CompTIA Data+ DA0-001

NuxSoftware Training & Certification Solutions in Coimbatore is doing a commendable job in providing top-notch training programs, especially for CompTIA Data+ (DA0-001). The combination of an excellent training center environment, advanced labs infrastructure, and international expert trainers with real-time industry experience makes it an ideal choice for individuals seeking to enhance their skills in data-related domains.

The emphasis on performance-based items in the CompTIA Data+ (DA0-001) certification adds a practical and hands-on aspect to the evaluation process. This approach ensures that professionals are not only equipped with theoretical knowledge but also have the ability to apply their skills in real-world scenarios. This makes the certification a valuable and practical validation of one’s capabilities in data-related roles.

The adaptability of CompTIA certifications, including their regular reinvention by IT experts, ensures that they stay relevant and aligned with the evolving demands of the IT industry. This commitment to staying current and addressing the core skills and abilities required in end point management and technical support roles makes CompTIA certifications trusted indicators of a professional’s competence in these areas.

Course Syllabus

CompTIA Data+ DA0-001 Syllabus

Chapters


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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