Within
the framework of this module, students will acquire knowledge and basic
competencies in the field of organizational change management. Through
sociological and psychological impulses, a thematic focus is placed on
corporate purpose, sustainability, healthy work cultures and equity of
opportunities. Experiential learning from a systemic, hypnosystemic and
solution-focused view is key.
Teaching methods: Presentation, self-study, exercises, exchange of experiences, discussion, case studies
Course Contents: Types of change, Phases of change, Barriers to change, Success factors for change, Reasons why change efforts fail, Change management, Corporate Purpose
After passing this course successfully, you can ...The
course will be held face to face in class. One session will take place
in self-study mode. The final presentation will take place face to face.
- distinguish types of change
- anticipate internal and external barriers to successful change
- identify success factors for change
- plan change management processes
- define the most important steps and measures for a concrete change
- understand reasons for resistance to change
- understand the company in and its interactions with the society
- LektorIn: Hoormann Lorena
- LektorIn: Dolezal Dominik
- LektorIn: Frank Michael
Computer aided data analysis
Introduction to the methods, tools and procedure models of computer-assisted data analysis based on selected chapters.
The course outlines the topic and conveys approaches for situational deepening of individual knowledge.
Content
- Introduction to Python, tools, software libraries and visualisation
- Process models, references and further information
- Regression
- Classification
- Clusters
- Decision Trees
- Time Series
- LektorIn: Rogner Wolfgang
Machine Learning
Overview of the state of the art in selected research topics as well as introduction to the basics, methods and process models of machine learning. Solutions are developed experimentally based on student-selected questions.
The course provides approaches to deepening individual knowledge based on specific occasions.
Content
- Tour d'horizon on the state of the art in machine learning and AI research.
- Introduction to Python, Tensorflow and Spacy (text analysis and processing)
- Procedure models, references and further information
- Analysis of exemplary applications
- Lab project, implementation of self-selected task using ML
- LektorIn: Rogner Wolfgang
- LektorIn: Kainrath Martin
- LektorIn: Felleitner-Goll Katharina
- LektorIn: Paier Martin
- LektorIn: Zihr Georg
- LektorIn: Netek Alf