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AI and Data Analysis in Business
From Raw Data to Data-Driven Decision-Making with AI, Python, and Modern Analytics Tools
Online
3 days
German
€ 1.890,-
plus VAT.
€ 2.249,10
incl. VAT.
Booking number
42914
Venue
Online
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4 dates
€ 1.890,-
plus VAT.
€ 2.249,10
incl. VAT.
Booking number
42914
Venue
Online
4 dates
plus VAT.
incl. VAT.
Booking number
Venue
0 Dates
Become a certified
Machine Learning Engineer
Machine Learning Engineer
This course is part of the certified Master Class "Machine Learning Engineer". If you book the entire Master Class, you save over 15 percent compared to booking this individual module.
To the Master Class
In-house training
In-house training just for your employees - exclusive and effective.
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In cooperation with
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Data is considered one of the most important resources for modern businesses—but its value is only realized through proper analysis and interpretation. At the same time, new AI tools like Gemini and modern analytics platforms make it significantly easier to gain data-driven insights. But how does a business idea or research question translate into a robust analysis that actually enables better decisions?
In this hands-on training the entire analysis process—from formulating a business question through exploratory data analysis to visualizing and communicating results. You’ll work with Google Colab, Gemini, Python, Tableau, and Power BI, and gain a clear introduction to modern data analytics and AI workflows.
The focus is not on programming, but on data literacy, understanding analysis, and confidently working with data-driven insights. Using a consistent real-world example, you’ll learn to critically evaluate data, apply AI effectively, and present analysis results in a way that’s tailored to your target audience.
Contents
- Fundamentals of Data-Driven Decision-Making
- The four levels of analysis: descriptive, diagnostic, predictive, and prescriptive.
- Understand data science, AI, machine learning, and large language models.
- CRISP-DM as a process model for data projects.
- From a business issue to a question that can be analyzed.
- Data Literacy and Understanding Data
- Data Sources, Data Quality, and Common Challenges.
- Understanding and evaluating datasets.
- Data profiling and initial analyses.
- Data quality as the foundation for reliable results.
- 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 analysis.
- AI support from Gemini for analysis, code, and interpretation.
- Exploratory Data Analysis (EDA)
- Analyze key figures, distributions, and relationships.
- Filter, group, and aggregate data.
- Identify correlations and anomalies.
- Evaluate analysis results critically.
- AI-powered data analysis and machine learning
- Potential 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 real-world examples.
- Data Visualization and Dashboarding
- The Basics of Good Data Visualization.
- Select appropriate chart types.
- Create dashboards using Tableau and/or Power BI.
- Presenting key figures in an understandable way.
- Data Storytelling and Communicating Results
- Present analysis results in a way that is tailored to the target audience.
- From Findings to Recommendations for Action.
- Communicate uncertainties and risks transparently.
- Storytelling for Management and Departmental Decisions.
- Application in Business Practice
- Identify data and AI use cases.
- Evaluate the business value of data-driven projects.
- Prioritizing analytics and AI initiatives.
- Develop a roadmap for implementation within your own company.
Python is deliberately 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.
Your benefit
- You understand the entire process of modern data analysis—from the business question to the decision recommendation.
- You'll learn to critically evaluate data and draw reliable conclusions from analyses.
- You'll get a hands-on introduction to Google Colab, Gemini, Python, and modern dashboard and BI tools such as Tableau and Power BI.
- You can use AI specifically to support analysis and evaluation tasks.
- You'll learn how to visualize analysis results in a way that's easy to understand and communicate them persuasively.
trainer
Amir Rahbaran
Dr.
Methods
- Technical Insights and Live Demonstrations
- Hands-on exercises in Google Colab
- Working with a Continuous Practical Example
- Group Projects and Case Studies
- Dashboard and Storytelling Workshops
- Discussions and Sharing of Experiences
Final examination
Recommended for
- Specialists and managers
- Business analysts
- project managers
- Consultants
- Employees from departments related to 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.
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