AI Data Analyst

Course overview

Data is present in many professions, but often there is not enough time to analyze it. This training course teaches the working methods of an AI data analyst: preparing, analyzing, and presenting data with the support of artificial intelligence, without the need for programming. Knowledge of analysis and the confident use of tools are always taught together. Understanding and Preparing Data Every analysis begins with an honest assessment of the data situation. - Identify data sources in the work environment and assess their quality - Clean and structure tables and lists with AI support - Recognize and document gaps, outliers, and contradictions Conducting Analyses with AI Support The tool provides speed, while the quality of the questions is provided by the human. - Formulate questions precisely before using tools - Use AI tools for evaluations, comparisons, and summaries, from table analysis to text analysis - Identify patterns, trends, and anomalies in data - Formulate hypotheses and verify them with further evaluations Visualizing and Communicating Results An analysis only has an impact when others understand it and can derive decisions from it. - Choose appropriate chart types for different statements and avoid typical visualization errors - Create reports and presentations with AI support - Tailor key messages to the audience without distorting data Interpretation and Limitations - Critically examine AI-generated evaluations instead of adopting them uncritically - Distinguish between correlation and causation and avoid fallacies - Make the significance and uncertainty of results transparent Data Protection and Responsibility in Data Work - Recognize personal data and handle it in compliance with GDPR before it enters tools - Adhere to rules for handling internal data in AI tools Efficiently Managing Recurring Evaluations The greatest time savings lie in evaluations that occur regularly. - Standardize reporting routines and accelerate them with AI support - Build reusable templates for regular evaluations - Establish plausibility checks as a fixed work step - Document evaluation paths to ensure results remain traceable Upon completion, participants will be able to independently evaluate data from their work area with AI support and create reliable decision-making bases from it.

Upcoming dates

• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Herzogenrath · Hybrid Learning
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Leinfelden-Echterdingen · Hybrid Learning
• 2026-11-03T23:00:00.000Z · 2027-07-03T22:00:00.000Z · Frankfurt am Main · Hybrid Learning