Analyzing Data and Building Reports and Dashboards with AI

• Translating a business question into a data requirement
• Structured, semi-structured, and unstructured information
• CSV, JSON, spreadsheets, text exports, and other common formats
• Data quality versus data quantity
• Preparing different data modalities for AI-assisted analysis

Hands-on Lab: Data Inventory and Conversion — Inspect mixed-format source files and use AI assistance to convert them into one consistent structured dataset while verifying that records remain accurate.

• Accuracy, completeness, consistency, timeliness, and relevance
• Detecting duplicates and formatting inconsistencies
• Missing-data handling
• Standardizing fields and categories
• Validation rules and reconciliation
• Maintaining a data-cleaning change log

Hands-on Lab: Clean a Messy Dataset with AI — Use AI-enabled spreadsheet or analysis tools to correct multiple data-quality problems, document every transformation, and validate the final dataset.

• Mean versus median
• Spread and distributions
• Outliers
• Sample size and representativeness
• Aggregation errors and misleading summaries
• Percentages and small bases
• Correlation versus causation
• Expressing uncertainty in analytical results
• Detecting unsupported claims produced by AI

• Protecting confidential information during analysis
• Using AI assistants and spreadsheet copilots with datasets
• Asking analytical questions in natural language
• Summaries, comparisons, trends, and breakdowns
• Sentiment and theme analysis
• Detecting analytical errors generated by AI
• Verifying AI results with formulas and pivot tables

Hands-on Lab: Ask, Analyze, and Verify — Answer business questions with AI, independently reproduce the answers using spreadsheet calculations or pivot tables, and compare AI-generated interpretations against the source data.

• Selecting the correct chart for the analytical question
• Creating visualizations with AI assistance
• Avoiding misleading charts
• Designing performance dashboards
• Selecting and displaying KPIs
• Writing data-backed interpretations of results
• Configuring batch versus real-time data sources
• Automating recurring reports and refresh processes
• Reviewing refreshed outputs before distribution

Hands-on Lab: Build a KPI Dashboard — Create a working dashboard containing business KPIs and visualizations, then use AI to draft a written interpretation and manually verify every claim.

Produce a complete analytical package that includes cleaned and documented data, verified analysis, a dashboard, a written interpretation of the findings, and instructions for refreshing the report in the future. The capstone is assessed against a skills-based rubric and must be successfully completed to earn the CERT4TECH Certificate in AI-Assisted Data Analysis and Reporting.