CompTIA Data Analysis Essentials
CompTIA Data Analysis Essentials introduces practical data analysis for professionals who work with reports, spreadsheets, and business data. Using Microsoft Excel , guided examples, and hands-on activities, participants learn to define analysis objectives, import and structure data, clean and combine datasets, use lookups, build formulas, create pivot tables, and present findings with charts and dashboards.
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Description
CompTIA Data Analysis Essentials introduces practical data analysis for professionals who work with reports, spreadsheets, and business data.
Using Microsoft Excel, guided examples, and hands-on activities, participants learn to define analysis objectives, import and structure data, clean and combine datasets, use lookups, build formulas, create pivot tables, and present findings with charts and dashboards.
The course also covers data validation, privacy, ethics, and sharing results.
After completing the assessment, learners can earn the CompTIA Competency Certificate, CompCert.
What You Will Learn
Module 1: Data Analysis Foundations and Project Setup
Define the purpose of a data analysis project and turn business questions into clear analysis objectives.
Identify success criteria, key metrics, expected outputs, and the audience for reports or dashboards.
Set up a simple analysis workflow using spreadsheet-based tools such as Microsoft Excel.
Module 2: Importing, Structuring, and Preparing Data
Import data from common file types, including spreadsheet and delimited text files.
Organize raw data into structured tables with clear headers, consistent records, and correct data types.
Apply basic formatting for text, numbers, dates, currency, and percentages.
Module 3: Cleaning, Reshaping, and Combining Data
Find and correct duplicates, blank values, inconsistent categories, and text formatting issues.
Reshape datasets by splitting, merging, and creating calculated fields.
Combine related datasets using lookup methods and common identifiers.
Module 4: Calculations and Core Analysis
Use aggregate and descriptive calculations such as SUM, AVERAGE, COUNT, MIN, and MAX.
Apply conditional logic with formulas such as IF, AND, OR, SUMIF, and COUNTIF.
Use relative and absolute references, named ranges, and formula checks to reduce errors.
Module 5: Summaries, Visuals, and Sharing Results
Create Pivot Tables, standard charts, and simple dashboards with slicers.
Validate results, protect sensitive data, document assumptions, and prepare outputs for sharing.
After completion, participants can clean datasets, perform basic analysis, present findings, and support workplace decisions with evidence.
Certification & Exam
This course prepares learners to earn the CompTIA Data Analysis Essentials CompCert, a competency certificate awarded after successful completion of the course assessment. It is not the same as the higher-level CompTIA Data+ certification exam.
The assessment focuses on applied data analysis skills, using performance-based tasks rather than a traditional multiple-choice certification exam. Learners should expect a short practical assessment, typically around 30 minutes, covering skills such as importing and cleaning data, using lookups, applying formulas, creating pivot tables and charts, and checking data integrity.
CompTIA does not publish a fixed question count or public numeric passing score for this CompCert assessment.
Reference: official CompTIA Data Analysis Essentials page.
What You Will Achieve
After completing CompTIA Data Analysis Essentials, participants will be able to:
Define a clear analysis objective by identifying the business question, required data, intended audience, and expected output.
Import and structure data from common sources using spreadsheet tools such as Microsoft Excel and Google Sheets.
Clean and prepare datasets by correcting formatting issues, handling missing values, removing duplicates, and checking data accuracy.
Analyze datasets using basic statistical methods, summaries, calculations, and spreadsheet functions to identify patterns and trends.
Create clear charts, graphs, tables, and simple reports that communicate findings to business users and team members.
Present analysis results with supporting evidence, practical conclusions, and recommendations that relate to the original objective.
Apply basic data ethics, privacy, and security practices when collecting, reviewing, sharing, and reporting data.
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