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Visual Data Analysis - M

Kurs ID
Art des Kurses
MSc Kurs
HS 2022
Dr. Tobias Keller
Bitte beachten Sie, dass AustauschstudentInnen im BSc-Programm der WHU eine höhere Anzahl an Credits erwerben als hier aufgeführt. Für weitere Informationen wenden Sie sich bitte direkt an das [International Relations Office].

A. Objectives and Focus of the course

“One Picture is Worth Ten Thousand Words” –this proverb is especially true in the case of Data Analysis. In business contexts, complex quantitative relationships are often ineffectively communicated because analysts underestimate the value of good data visualizations. In times of Big Data, almost every business professional, especially in management and consulting, is involved in Data Analysis.

This course aims at enabling students to visually analyze data and to effectively communicate their analytical results. In the first part of the course, examples from practical analytical use cases and scientific insights about visual perception and design lay solid foundations of Visual Data Analysis. Secondly, during a hands-on workshop, students learn how to use a professional Visual Data Analysis software. Finally, students apply what they have learned in a group project and practice their communication skills when presenting their results.

B. Structure of the course

1. Introduction

  • Data Science and the importance of Visual Data Analysis
  • Examples of actual use cases for Visual Data Analysis in practice

2. Foundations of Visual Data Analysis

  • Requirements for Visual Data Analysis: skills, data, software
  • Types of quantitative relations within data and how to best visualize them: time-series, distribution, correlation, etc.
  • Visual perception: and what we can learn from science on how to communicate using visualizations
  • When and how to effectively use visual attributes (length, position, size, color, shape, …)
  • Analytical relationships and patterns

3. Workshop: Visual Data Analysis using Tableau

This partis a practical workshop using the software Tableau. Students learn how to effectively use a professional Data Visualization software. This includes the following topics:

  • Connecting with your data
  • Analytical interaction and navigation
  • Analyzing typical relationships and patterns (using, for example, bar charts, line charts, geographic heatmaps, scatter plots, …)
  • Best practices for Visual Design
  • Calculations
  • Building interactive dashboards

4. Case studies / group work

In the final part of the course, students work in groups on different case studies. The groups create Dashboards for Visual Data Analysis using the software Tableau applying the theoretical and practical skills from the first two parts of the lecture. The results are presented and discussed in the final session.

Date Time
Friday, 11.11.2022 08:00 - 15:15
Friday, 18.11.2022 08:00 - 15:15
Friday, 25.11.2022 08:00 - 15:15
Saturday, 10.12.2022 09:45 - 17:00
Foundations of Visual Data AnalysisRequired: Few, S. (2009).Now You See it: Simple Visualization Techniques for Quantitative Analysis(1st ed.). Oakland (CA): Analytics Press.Optional: Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.). Cheshire (CT): Graphics press.Workshop: Visual Data Analysis using TableauThere is no required reading for this section. The online help for Tableau and the Tableau Forum are typically good places to start if you have a question regarding Tableau. However, if you would like to have a comprehensive resource at hand, the following book covers the basics (and some more advanced topics):Optional: Loth, A. (2019). Visual Analytics with Tableau (1st ed.). Wiley.Optional: Murray, D. (2016). Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software (2nd ed.). Wiley.
During contact time, the module is a combination of lectures, exercises, and group-work presentations. Furthermore, project work and self-study are also important teaching and learnings methods of the module.

Grading is based on the following components:

  • Mid-term exam: 30%
  • Group work / presentation: 70% (with peer feedback)
Mid-term exam, presentation
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