Course
digicode: H42914
AI and Data Analysis in Business
Course facts
Download as PDF- Understanding the entire process of modern data analysis – from the business question to the decision recommendation
- Learning to critically evaluate data and derive reliable insights from analyses
- Gaining a hands-on introduction to Google Colab, Gemini, Python, and modern dashboard and BI tools such as Tableau and Power BI
- Using AI strategically to support analysis and evaluation tasks
- Learning to visualize analysis results clearly and communicate them persuasively
1 Fundamentals of Data-Driven Decision-Making
- The four stages of analysis: descriptive, diagnostic, predictive, and prescriptive
- Understanding the roles of data science, AI, machine learning, and large language models
- CRISP-DM as a process model for data projects
- From a business question to an analyzable research question
2 Data Literacy and Data Understanding
- Data sources, data quality, and common challenges
- Understanding and evaluating datasets
- Data profiling and initial analyses
- Data quality as the foundation for reliable results
3 Data Analysis with Google Colab, Gemini, and Python
- Working with Google Colab as an analysis environment
- Basics of Python for data analysis
- Working with Pandas for data preparation and evaluation
- AI support from Gemini for analysis, coding, and interpretation
4 Exploratory Data Analysis (EDA)
- Analyzing key metrics, distributions, and relationships
- Filtering, grouping, and aggregating data
- Identifying correlations and anomalies
- Critically evaluating analysis results
5 AI-Powered Data Analysis and Machine Learning
- Applications of AI in analytical processes
- Prompts for data analysis and data understanding
- Opportunities and limitations of AI-generated analyses
- Fundamentals of machine learning using practical examples
6 Data Visualization and Dashboarding
- Fundamentals of effective data visualization
- Selecting appropriate chart types
- Creating dashboards with Tableau and/or Power BI
- Presenting key metrics clearly
7 Data Storytelling and Communicating Results
- Presenting analysis results in a way tailored to the target audience
- From insights to recommendations for action
- Communicating uncertainties and risks transparently
- Storytelling to support management and departmental decisions
8 Applying Findings to Business Practice
- Identifying data and AI use cases
- Evaluating the business value of data-driven projects
- Prioritizing analytics and AI initiatives
- Developing a roadmap for implementation within your own company
Python is intentionally not treated as a standalone programming learning objective, but rather as a tool for analysis. A brief introductory notebook explains key syntax elements so that code doesn’t seem like black magic. CRISP-DM serves as a methodological guide throughout the seminar and is applied in a final practical exercise using real-world data and AI use cases.
- Technical insights and live demonstrations
- Hands-on exercises in Google Colab
- Working through a comprehensive practical example
- Group work and case studies
- Dashboard and storytelling workshops
- Discussions and sharing of experiences
- Specialists and Managers
- Business Analysts
- Project Managers
- Consultants
- Employees from departments involved in data or AI
No programming experience is required to participate. An interest in data-driven decision-making and a basic understanding of business processes are a plus.
Requirements for participation:
- your own laptop, a stable internet connection, and a current web browser
- a Google Account (https://accounts.google.com/signup) with access to Google Drive and Colab (alternatively, a local development environment such as Visual Studio Code with GitHub Copilot or a comparable AI-powered programming environment)
- A Tableau Public account (available for free at https://id.tableau.com/register)
- No local Python installation required
We recommend booking at least 14 days before the seminar date so that you can receive any documents by post in good time.