CompTIA Data+ Proficiency (Advanced Level)

  • 37m
  • 37 questions
The CompTIA Data+ Proficiency (Advanced Level) benchmark measures your knowledge of key techniques used in data manipulation, common techniques for query optimization, and the importance of and methods for using descriptive statistics. You will be evaluated on your ability to define the importance and methods of inferential statistics, identify data analysis types and techniques, describe key activities and elements for data analysis reporting, recognize key activities and elements for data analysis dashboards, and identify methods for creating popular charts and graphs used in visualization data. A learner who scores high on this benchmark demonstrates that they have a good level of experience in data analysis and visualization that's required for the CompTIA Data+ certification.

Topics covered

  • create stacked charts using a dataset and chart creation application
  • define ad-hoc and self-service reports
  • demonstrate examples of good and poor data visualization
  • describe the characteristics of data merging and blending and related data manipulation techniques
  • describe the necessary design elements used to create professional reports
  • describe the purpose of frequency distribution, including frequencies and percentages
  • describe the similarities and differences between static and dynamic reports
  • determine data needs and sources
  • develop heatmap charts using a set of statistics and chart software
  • develop word clouds using data and chart software
  • identify best practices for data visualization
  • identify data transposition and its role in data manipulation
  • identify key activities involved in delivering dashboards
  • identify the purpose of exploratory data analysis, including the use of descriptive statistics to determine observations
  • identify the purpose of hypothesis testing and define Type I and Type II errors
  • measure percent change using a dataset
  • name key activities and elements of dashboard development
  • outline best practices for creating reports
  • outline chi-square analysis in data analytics and describe null and alternative hypothesis
  • outline the documentation elements used in professional reports
  • outline the purpose of percent difference and its purpose as a statistical method
  • outline the various design elements implemented to create professional reports
  • outline typical design components for reports, specifically the cover page, instructions, summary, and observations and insights
  • perform a data blend
  • perform a data merge
  • perform a p-value analysis using a dataset
  • perform hypothesis testing using a dataset
  • perform index scans
  • perform regression analysis using a dataset
  • perform z-score analysis using a dataset
  • provide an overview of typical query optimization tools
  • review and refine business questions
  • transpose data in Microsoft Excel
  • use execution plans
  • utilize saved searches in a dashboard
  • work with a dataset and chart creation program to construct waterfall charts
  • work with a set of statistics and software to generate infographics