Overview

• Course-Number: INF-21230

• Language of Instructions: Englisch

Topics

In this hands-on course, you will learn how to become a data scientist! At the hand of challenging datasets with hidden insights, together with your group, you will create an interactive visualization tool for the VAST Challenge, a data analysis competition held annually as part of the IEEE VIS conference. Besides getting a grip on visual data analysis, you can win awards and maybe even present your creative solutions to a large audience.

In the course, we will together explore and implement the whole Knowledge Discovery in Databases pipeline, from initial data analysis over data preprocessing to a final Visual Analytics prototype. We will have frequent visits by experts in data analysis and visualization during this process, giving you direct and valuable feedback on your ideas. During the course, you will work in groups of 3-4 people and iteratively develop a tool for solving the VAST competition's challenges. At every event, we will discuss everyone's progress and plan how to proceed.

In the end, you will have learned how to solve complex data analysis tasks using Visual Analytics approaches. You will develop an understanding of how to approach data analysis tasks. You will learn to use data analysis software such as KNIME or TABLEAU, and you will understand why these tools are often not enough to answer a complicated question. Instead, you will put together your own Visual Analytics prototype, where you are free to combine powerful machine learning and AI techniques with custom visualization solutions.

Contents of the course:

• Applied Methods of Information Visualization
• Applied Methods of Data Mining
• Interactive integration of automatic and visual methods
• Application and adaption of recent research work to important application scenarios such as Exploration of Social Web data, or security-related applications (e.g., disease control, criminal investigations, and homeland security)
• Processing and exploration of different data types like geo-coordinates, gene sequences, text documents, data from social networks and internet traffic, each with respect to temporal developments.
• Participation in an international challenge, e.g., IEEE VAST Challenge.

Recommended and mandatory Prerequisites

• Practical Programming Experience (mandatory)

• Data Visualization Lectures (mandatory)

• Data Mining Lectures (mandatory)