About

CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. Equip yourself with skills to better analyze and interpret data, communicate insights, and demonstrate competency. CompTIA Data+ validates that certified professionals have the skills required to facilitate data-driven business decisions.

Though this CompTIA Data+ Training Course you will learn how to:

Identify basic concepts of data schemas and dimensions and understand the differences between common data structures and file formats to build a strong foundation in data concepts and environments.

Apply data acquisition, cleansing, profiling, and manipulation techniques to enhance data mining skills.

Use appropriate descriptive statistical methods and summarize types of analysis and critical analysis techniques for effective data analysis.

Translate business requirements into meaningful visualizations by creating reports or dashboards.

Summarize key data governance concepts and apply data quality control techniques to ensure accuracy and compliance.

How does CompTIA Data+ compare to other data certifications?

CompTIA Data+ stands out with its vendor-neutral, entry-level focus, covering essential data analytics skills like mining, analysis, and visualization.

It’s globally recognized, ISO/ANSI National Accreditation Board (ANAB)-accredited, and cost-effective. Unlike specialized certifications, it provides comprehensive, real-world knowledge applicable across industries, making it an ideal starting point for aspiring data professionals.

CompTIA Data+ is a globally recognized certification that validates skills in data analysis, data visualization, and data governance. CompTIA certifications, including Data+, are widely accepted by employers and organizations around the world as they adhere to industry standards and best practices.

Why is CompTIA Data+ Important?

Data+ ensures professionals have foundational skills to better analyze and interpret data, communicate insights, and demonstrate competency. CompTIA Data+ can help you develop and refine these skills and prove you have the hands-on experience employers seek.

Modules

Data mining (25%)

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, normalizing, and parsing.
Query optimization: explaining filtering, sorting, date functions, logical functions, aggregate functions, indexing, temporary tables, and execution plans.

Data concepts and environments (15%)

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 analysis (23%)

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: summarizing trend analysis, performance analysis, exploratory analysis, and link analysis.

Visualization (23%)

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.
Visualization 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 (14%)

Data governance: summarizing 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.