Top CompTIA Data+ DA0-001 Training and Certification

CompTIA Data+ DA0-001

CompTIA Data+ (DA0-001) Training and Certification Course

Course Overview

The CompTIA Data+ (DA0-001) training course is designed for data professionals who want to gain foundational knowledge in data analytics, data governance, visualization, and data-driven decision making. This course prepares learners to pass the globally recognized CompTIA Data+ certification exam, while also building practical skills that are immediately applicable in real-world business environments.

Offered by our expert-led training center in Coimbatore, this course is perfect for professionals who want to transition into analytics or want to validate their data-handling capabilities in roles related to business intelligence, operations, marketing, or IT.

Who Should Enroll?

  • Aspiring data analysts or junior-level professionals in data-centric roles

  • Business professionals looking to enhance data literacy and insights

  • IT professionals and system admins handling data reporting tasks

  • Students and fresh graduates targeting analytics-focused job roles

  • Anyone preparing for the CompTIA Data+ DA0-001 certification

What You Will Learn

  • Data Concepts and Environments: Understanding structured and unstructured data, database types, and data systems

  • Data Mining and Manipulation: Data cleansing, transformation, and validation using tools like SQL and Excel

  • Data Analysis and Visualization: Applying basic statistical methods, identifying patterns, and visualizing insights using tools like Tableau or Power BI

  • Data Governance, Quality, and Controls: Understanding compliance, data policies, integrity, and privacy standards

  • Data-Driven Decision Making: Using analytics to support business recommendations and performance tracking

Key Course Features

  • Aligned with CompTIA Data+ (DA0-001) official exam blueprint

  • Hands-on labs and real-time scenarios with datasets

  • Practical training on SQL queries, dashboards, and data cleaning

  • Practice exams and mock interviews for certification readiness

  • Conceptual training combined with business case studies

Career Roles You Can Target

  • Data Analyst

  • Business Intelligence Analyst

  • Reporting Analyst

  • Operations Analyst

  • Data Technician

  • Marketing Analyst

  • Database Support Specialist

Why Choose Our Training Center in Coimbatore?

  • Certified trainers with practical experience in data analytics

  • Up-to-date curriculum based on the latest DA0-001 exam objectives

  • Flexible training options – weekdays/weekends/online classes

  • Real-world data projects and business reporting exercises

  • Guidance for exam booking, preparation, and job assistance

Empower your career with CompTIA Data+. Gain a solid foundation in data analytics, build reporting confidence, and become data-driven.

CompTIA Data+ DA0-001 Syllabus

Modules

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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