CompTIA Data+

Course subjects

Data concepts and environments  

• Data schemas and dimensions: identifying databases, data marts, data warehouses, data lakes, and slowly changing dimensions. 

• Data types: comparing date, numeric, alphanumeric, currency, text, discrete vs. continuous, categorical/dimension, images, audio, and video. 

• Data structures and file formats: comparing structured and unstructured data and file formats like text/flat files, JavaScript object notation (JSON), extensible markup language (XML), and hypertext markup language (HTML). 

Data mining  

• Data acquisition: explaining integration methods like delta load, extract/load/transform (ELT), and collection methods like web scraping, application programming interfaces (APIs), surveys, sampling, and observation. 

• Data cleansing and profiling: identifying duplicate data, missing values, invalid data, outliers, specification mismatches, and data type validation. 

• Data manipulation techniques: executing techniques like merging, blending, concatenation, appending, imputation, aggregation, transposing, normalising, and parsing. 

• Query optimisation: explaining filtering, sorting, date functions, logical functions, aggregate functions, indexing, temporary tables, and execution plans. 

Data analysis  

• Descriptive statistics: applying measures of central tendency, dispersion, frequencies, percentages, percent change, and confidence intervals. 

• Inferential statistics: explaining t-tests, z-scores, p-values, chi-squared tests, hypothesis testing, regression, and correlation. 

• Analysis techniques: summarising trend analysis, performance analysis, exploratory analysis, and link analysis. 

Visualisation  

• Business requirements: translating requirements into reports using measures of central tendency, dispersion, and percentages. 

• Report and dashboard design: using cover pages, design elements, and documentation. 

• Dashboard development: applying considerations for development processes and delivery. 

• Visualisation types: applying line charts, pie charts, scatter plots, bar charts, histograms, heat maps, geographic maps, tree maps, stacked charts, and word clouds. 

• Report types: comparing static vs. dynamic, ad-hoc, self-service, recurring, and tactical research reports. 

Data governance, quality, and controls  

• Data governance: summarising access, security, storage, use, entity relationships, classification, jurisdiction, and breach reporting. 

• Data quality control: applying validation methods, quality dimensions, rules, metrics, and automated checks. 

• Master data management (MDM): explaining processes and circumstances for MDM.