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AI230: Introduction to Data Visualization

Training: Artificial Intelligence

Participants learn the fundamentals of data visualization with scatter plots, bar charts, and violin plots, as well as the use of Python libraries such as matplotlib, seaborn, and plotly. They discover how LLMs can be leveraged for dynamic SQL generation and how Power BI and open-source tools can be used for integration with AI models. In addition, the course covers dos and don’ts for creating effective visualizations.

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Possible course languages: German

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Agenda:

1. Introduction: Importance of data visualization

Why data visualization is crucial for analysis and machine learning
Examples of visual data representations

2. Fundamentals of visualization

Introduction to different visualization types: Scatter Plots, Bar Charts, Violin Plots
Dos and Don'ts of visualization

3. Working with Python Libraries

matplotlib, seaborn and plotly in detail
Hands-on exercises for chart creation

4. Using LLMs for SQL generation

Dynamic generation of SQL queries using LLMs
Use cases

5. Integration of data visualization tools

Power BI and open-source solutions (e.g. Metabase, Apache Superset)
Integration with AI models for dynamic data analysis

6. Best practices and principles of compelling visualization

Principles of effective visualizations
Error prevention and tips for successful presentation

7. Practical applications and closing discussion

Hands-on exercise for integrating LLMs into visualization workflows
Questions and participant exchange

Objectives:

Learn data visualization fundamentals, get to know various visualization types, use visualization libraries in Python, be able to clearly present complex matters.

Target audience:

Developers
IT Professionals

Prerequisites:

AI200 Introduction to Python for Data Science and AI (alternatively basic knowledge in Python)
AI220 Introduction to Machine Learning

Description:

The workshop AI230 Introduction to Data Visualization teaches the fundamentals of data visualization using various comprehensible examples. Participants learn different visualization types such as Scatter Plots, Bar Charts and Violin Plots and work with Python libraries like matplotlib, seaborn and plotly. Furthermore, participants are taught how LLMs are used for dynamic SQL generation and how tools like Power BI and open-source solutions can be used for connecting to AI models to enable dynamic visualizations. The fundamentals of appealing visualizations, such as dos and don'ts, as well as their significance for data analysis are also covered.