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Prepare data, apply AI algorithms, and make informed decisions through clear analysis
This course provides a comprehensive introduction to AI‑powered data analysis. You will learn how to professionally prepare data, apply suitable AI models, and create meaningful visualizations.
The training offers practical insights into how machine learning identifies patterns and relationships — and how you can apply these insights in your own projects. Case studies from various industries demonstrate how companies successfully use AI to support decision‑making. Finally, you will learn how to define KPIs, measure analysis processes, and continuously optimize them.
Our experienced trainers will show you:
- how AI‑driven data analysis works and what potential it offers
- how to correctly prepare, structure, and clean data
- how to apply decision trees, neural networks, and clustering algorithms
- how to visualize data accurately and present results convincingly
- how to transfer analytical insights into projects and make data‑driven decisions
- how to define KPIs and continuously optimize analysis processes
Your Benefits at a Glance
- Understanding of modern AI algorithms and their use in data analysis
- Higher data quality through professional preparation and cleaning
- Clear, structured visualizations for better decision‑making
- Practical examples and exercises for direct application in your own projects
- Continuous improvement through relevant KPIs and analytical metrics
Seminar Contents
Introduction to AI‑Driven Data Analysis
- Fundamentals and potential of artificial intelligence
- Overview of tools and technologies
Data Preparation & Data Cleaning
- Techniques for professional data preparation
- Structuring and cleaning raw data
- Using tools for data preprocessing
AI Algorithms & Analytical Models
- Decision trees
- Neural networks
- Clustering algorithms
- Applying machine learning models
Data Visualization
- Fundamentals of visualization
- Using visualization tools
- Creating meaningful charts & graphics
Practical Applications
- Case studies from various industries
- Best practices & successful real‑world examples
- Hands‑on exercises in AI‑driven data analysis
Success Measurement & Optimization
- Defining relevant KPIs & metrics
- Reporting & analysis
- Continuous improvement of analytical processes
Target Audience
- Data analysts, domain experts, project managers
- Individuals who want to use AI for data‑driven decision‑making
- Company teams aiming to professionalize their analysis workflows
Prerequisites
- Basic knowledge of data analysis and statistics
- Experience with digital tools is helpful but not required
