October 2024 – June 2025
1. June 2024
Teaching dates (subject to change)
10.09.24 (Kickoff Zoom call), 13.09.24, 27.09.24, 11.10.24, 25.10.24, 08.11.24, 22.11.24
Learn to make complex data readable and to discover the unexpected in your data set
Visual analytics combines automated analysis techniques with interactive visualizations for an effective understanding, reasoning and decision making based on very large and complex data sets. Following this two-sided approach to data analysis, this course offers a practical introduction to Visual Analytics in three parts – each corresponding to one of the three single subjects, the course consists of:
- the automated data analytics techniques – especially supervised and unsupervised learning,
- the interactive visualization techniques – especially for uni-, bi-, and multivariate data.
- the effective combination of both in a practical visual analytics system as a course project.
Participants develop familiarity with key concepts in data mining, visualization methods, and their mutual integration into visual analytics.
Practical skills include understanding relevant terminology, utilizing state-of-the-art libraries and toolkits, and evaluating visual analytics solutions. The course emphasizes analytical and conceptual proficiency, process knowledge, and effective communication of requirements and results.
NB: This course is taught in English. PhD students will participate in parts of the course. Finally, be aware that the course is still under development. Therefore, we reserve the right to make changes to the description of the course.
Watch the video about Visual Analytics
This course is relevant for:
Developers of business intelligence (BI) solutions seeking to learn the intricacies of developing a complete BI solution.
Business intelligence architects aiming to acquire a profound understanding of the technologies and thereby design optimal solutions for specific business needs.
Data Scientists or Data analysts, who analyze data on a daily basis, but want to acquire new knowledge about how the underlying technology functions.
Specific admission requirements for Visual Analytics
Participants are expected to have a basic understanding of functions, distance measures, vector and matrix operations, probabilities, descriptive statistics (mean, std. deviation, etc.), and should be familiar with Python and basic web programming.
Based on individual assessment, exemptions from the admission requirements may be granted if it is deemed that you have equivalent educational prerequisites to complete the program. Exemption from the requirement of two years of relevant professional experience after completing the qualifying education is not possible.
If you do not meet the formal admission requirements, please contact Continuing Education for further guidance.
Read more about the general admission to the Master in IT, specialization in Software Construction here.
The course, Visual Analytics, consists of three single subjects:
- Data Analytics
- Foundations of Visualization
- Research and Development Project in Visual Analytics
Questions regarding the curriculum:
Associate Professor Hans-Jörg Schulz
Questions about admission, enrollment, and billing inquiries:
Continuing Education, Administration Centre Nat-Tech
Admission officer, Sabine Louisa Haxen
Phone number: 9352 2803
“The course Visual Analytics gives the participants a unique skill-set combination. Usually, as a Data Scientist you either learn how to crunch and manage the data and in data visualization, you learn how to visualize and tailor the data for human consumption. But in this course, you learn to work on both sides, to integrate them and then to be able to solve problems, that can neither be solved automatically nor manually.
Visual Analytics is particularly helpful for exploratory data analyses. You learn to look for data, to confirm the expected but also to discover the unexpected and it is exactly what it does. You have a certain expectation about what the data looks like, and the data visualization helps you to confirm that the data actually is how it should be, and on the other hand, if there is something out of place, it also helps you to discover something new that otherwise would stay hidden.
At the course you will meet Davide Mottin, who is the data mining expert, and me, who is the data visualization expert. During the course, we do not drive deep into math or deep into coding, the focus is instead about understanding how the data mining and the visualization part work together, and how and why to select one method over another.”
Associate Professor Hans-Jörg Schulz, course lead for Visual Analytics.