CompTIA Data+ Exam DA0-001 - Appendix A Summary Notes

Proprietary and Open Source Languages

  • Each language is different and uniquely used to deal with data.
  • Interaction methods:
    • Directly coding.
    • Working with software that leverages them.
  • Open source: Freely available for use.
  • Proprietary: Custom to the vendor.
  • Capabilities:
    • Working with large data sets.
    • Applying transformations.
    • Coding graphical outcomes.
    • Performing statistical analysis.
  • Transact-SQL (TSQL): A Microsoft and Sybase proprietary extension of SQL.
  • Open source languages communicate via middleware.
  • Commonly used open source languages:
    • SQL
    • R
    • Python

Data Transformation Tools

  • Dedicated to transforming data to meet business requirements.
  • Tools:
    • Microsoft Excel: Expanded to support data analysts.
      • Row count increased from 65,000 to one million.
      • Power Query for both Power BI and Excel.
    • Tableau Prep: Enables more data preparation.
  • Uses:
    • Mine data.
    • Explore data.
    • Expand data.
    • Link to other data sets.
    • Build models.
  • Commonly used tools:
    • Microsoft Excel
    • Tableau Prep
    • Microsoft Power BI
    • Rapid Miner

Visualization Tools

  • Dedicated to building dashboard representations of data.
  • Any tool can be used for this purpose.
  • Common visual elements:
    • Charts
    • Graphs
    • Tables
    • Filters
  • Tool selection depends on organizational purchases.
  • Popular tools:
    • Tableau
    • Microsoft Power BI
    • Qlik
    • ArcGIS
    • AWS QuickSight

Statistical Tools

  • Software involves some form of statistical analysis.
  • Dedicated tools offer more test capability and features for data preparation.
  • Capabilities:
    • Tests for parametric and nonparametric data.
    • Prediction and regression models.
    • Statistical analysis like fixed effects and mixed effects.
    • Statistical analysis outcomes and visualization.
  • Designed with statistical analysis in mind.
  • Accepted by researchers and scholars.
  • Popular tools:
    • SAS
    • IBM SPSS
    • IBM SPSS Modeler
    • Stata
    • Minitab

Paginated Reporting Tools

  • Used for reports printed over multiple pages.
  • Reports with lines of data unsuitable for dashboarding.
  • Common tools:
    • SQL Reporting Services (SSRS)
    • Crystal Reports
    • Power BI Report Builder

Platform Tools

  • Suites of tools combined for organizational use.
  • Capabilities:
    • Share data
    • Create reports
    • Build dashboards with organizational data
  • Data professionals may work within multiple cloud platforms.
  • Popular platforms:
    • Business Objects, MicroStrategy, Apex, IBM Cognos, Dataroma, Cloudera, Alteryx, Oracle Analytics, Domo, Microsoft Power Platform, RapidMiner

Review Activity: Common Data Analytics Tools

  1. Deep research and statistical analysis require flexible tools.
  2. Dashboards deliver real-time call center information to operations.
  3. Proprietary software: Vendor owns the code and does not share it publicly. Freely available software is open source.
  4. Software suites provide many options for an organization.