Practise communicative and cultural skills and competences in German on level A1 - A2 (cf. CEFR descriptors).

Interactive teaching

Develop German language competence on level A1 - A2 (cf. CEFR descriptors), develop cultural competence

• listen to, understand, and react to simple texts and dialogues
• read and understand simple texts and dialogues
• know and use basic grammar and vocabulary
• write simple texts
• use learning strategies successfully
• develop cultural competence

Develop German language competence on level A1 - A2 (cf. CEFR descriptors), develop cultural competence

• listen to, understand, and react to simple texts and dialogues
• read and understand simple texts and dialogues
• know and use basic grammar and vocabulary
• write simple texts
• use learning strategies successfully
• develop cultural competence

Available on Moodle

Grading

Parts of assessment
A: written and oral assessment during the semester
B: written test

Attendance: minimum 80%

Grading

Parts of assessment
A: written and oral assessment during the semester
B: written test

Attendance: minimum 80%

cross-faculty course:
Rules for registration: www.uni.li/cross-faculty

Human-Centred Design is an approach that places people at the core of every decision point throughout the design process. Identifying, understanding and fulfilling people’s needs, desires, wishes, and goals are imperative in human-centred design. The approach is relevant to any design endeavour that aims to deliver useful products, services, and combinations of both to people as the end-users. The same applies to the design of software, mobile applications, collaboration platforms, and other information systems.

This course is designed with Information Systems students’ needs and goals in mind. Students are guided through their journey in understanding the basics of human cognition and human behaviour that are relevant to the design of information systems. They also learn several methods of human-centred design that are applicable in their projects.

• The module involves interactive lectures with exercises to integrate theoretical knowledge with critical analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Case studies are used to discuss the course contents.
• Contemporary scientific publications from Information Systems and Human-Centred Design are discussed in class.

After successful completion of the course, students will:

• understand the basics of human cognition and human behaviour that are relevant to the design of information systems
• understand different human-centred design methods
• be able to apply the understanding and the design methods into their own design projects or illustrative cases

Written exam (60min)

Emerging IT Topics addresses recent technological trends and developments in research and business, so its content can be adapted quickly to the job market’s emerging needs. Accordingly, the course content changes from
semester to semester.

• The course involves interactive lectures that integrate theoretical knowledge with analysis skills.
• Contemporary scientific publications from the fields of Information Systems and Management are discussed in class.

After successful completion of the course, students will:

Professional competence

• have profound knowledge of current topics and recent technologies in IT
• be able to assess the usefulness and potential applications of emerging IT technologies
• be current on recent scientific results on emerging IT topics
• understand latest trends, opportunities, risks, and also regulations
• be familiar with and understand the key principles of emerging IT topics

Methodological competence

• understand theories and models in the field of innovation and technology diffusion
• know different tools, which help to plan and integrate emerging IT topics in organisations
• be able to identify potentials for improvement in organisations through the use of the applications and methods discussed
• apply the methods dealt with in the lecture to solve exercises

Social competence

• develop their social skills in solving small exercises and adapt these skills to optimise teamwork
• listen carefully to the lecturer and their fellow students and actively participate in the lecture
• support each other in lectures and self-study and help each other with questions

Personal competence

• tolerate the opinions of other students, even if they contradict the own understanding
• independently and reliably take care of practicing the lecture contents (especially exercise sheets, slides and study of the provided literature)

Technological competence

• be familiar with latest technologies in various IT areas
• can apply advanced web search to find and analyse trending IT topics

Presentation (50%), written exam (50%), attendance is mandatory (80%)

Digital Entrepreneurship covers the intersection between digital technology and new venture creation, i.e., com-pany start-up activity. It addresses venture creation of digital artefacts as the core market offering (e.g., software, hardware, smart devices), digital technology as enablers of new venture creation (e.g., 3D printing, crowdfunding, platforms such as appStore), and venture creation in technology-intensive contexts (e.g., BioTech, IT Healthcare, FinTech). The course covers six primary topics:

• Forms and processes of entrepreneurship
• Business planning for new ventures
• Digital technologies as enablers and triggers for entrepreneurial activity
• Digital technologies as market offerings of emergent ventures
• Start-up activity in technology-intensive sectors

• The module combines interactive lectures with case studies and exercises to integrate theoretical knowledge with practical start-up and business planning skills.
• The e-learning platform Moodle will be used throughout the course to disseminate course material and for information and discussion.
• Case studies will be used to discuss and illustrate course contents.
• Contemporary scientific publications from Information Systems and Entrepreneurship will be discussed in class.

After successful completion of the course, students will:

• understand the fundamentals of entrepreneurship
• understand the unique challenges and opportunities of digital entrepreneurship
• understand peculiarities of digital technologies with respect to new venture emergence processes
• assess and evaluate the role of digital technologies in different phases of entrepreneurship

Written exam (60min)

Data Visualisation covers techniques for creating effective data visualisations based on principles from statistics, cognitive science, and graphic design to help analysts and decision-makers understand and explore big data. The course covers eight primary topics:

• Visualising univariate and multivariate numerical data
• Visualising time series data
• Visualising geospatial data
• Visualising networked data
• Visualising high-dimensional data
• Visualising textual data
• Interactive dashboards
• Animations

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Real-life examples are used to show how the course content can be applied in practice.

After successful completion of the course, students will:

• understand the main concepts, theories, and methods of data visualisation
• recognise the typical challenges of visualising large and complex data sets
• be able to create graphs like bar charts, scatterplots, line charts, and heatmaps to represent various types of data sets visually
• be able to use data-visualisation methods to analyse business problems, generate possible solutions, and compare these solutions in terms of their effectiveness and efficiency

Written exam (60min)

Data Management covers the modern data-management cycle, from the collection of data from diverse sources to the preparation of data for data-driven applications. Students learn how to handle various data formats, how to assess and improve data quality, and how to store and process data using SQL, NoSQL, and Hadoop technologies. The course covers eight primary topics:

• Modern data-management requirements
• Database system architecture
• Diagnosing and handling data quality problems
• Relational databases (SQL)
• Hands-on labs with MySQL
• Concurrency control techniques
• NoSQL databases (e.g., MongoDB)
• Apache Hadoop (HDFS, MapReduce)

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course, students will:

• understand the basic concepts and methods of modern data management
• be able to collect and prepare data for data-driven applications
• be able to select and apply appropriate technologies for building data-driven applications

Written exam (60min)

Business Statistics covers statistical methods that are used to support decision-making in business contexts, so it also provides a methodological foundation for the students' master's thesis projects. The course builds on the basic concepts of statistical testing and estimation theory that are usually taught in bachelor’s programmes. The course covers five primary topics:

• Graphic and numeric characterizations of random variables and their distributions
• Framework and basic applications for testing hypotheses and estimating parameters
• The ordinary least squares (OLS) method
• Simple linear regression, including parameter estimation, diagnostic plots, hypothesis testing, predictions, and model specifications using log-transformations
• Introduction to the software package R

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• Students complete homework assignments after each lecture.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course, students will:

• be able to present the distributions of random variables graphically and to calculate and interpret their moments
• understand the framework of testing hypotheses and estimating parameters
• know the assumptions made in basic testing and estimating procedures when drawing general conclusions
• be able to derive the minimum sample size for basic testing and estimation procedures
• be able to apply the ordinary least squares method to derive estimators and compare their statistical properties
• be able to explain the classic linear model assumptions, run simple linear regressions, check the diagnostics plots, use log-transformations to specify models, and interpret the results correctly

Written exam (60min)
To successfully pass the module, students must collect at least 50 percent of points in a final exam (60 minutes; 30 points in total). During the exam, students may use a self-created “cheat sheet” (DIN A4, double-sided, machine-written or handwritten, any contents) and a calculator of their choice (including programmable calculators).

Practise communicative and cultural skills and competences in German on level A2 (cf. CEFR descriptors).

This is a suggested schedule but, if practicable, changes can be made in accordance with the wishes of the participants.

Information about the placement test is provided on the website of the cross-faculty elective subjects > language courses and by the International Office

Interactive teaching

Develop German language competence on level A2 (cf. CEFR descriptors), develop cultural competence

• listen to, understand, and react to simple texts and dialogues
• read and understand simple texts and dialogues
• know and use basic grammar and vocabulary
• write simple texts
• use learning strategies successfully
• develop cultural competence

Develop German language competence on level A2 (cf. CEFR descriptors), develop cultural competence

• listen to, understand, and react to simple texts and dialogues
• read and understand simple texts and dialogues
• know and use basic grammar and vocabulary
• write simple texts
• use learning strategies successfully
• develop cultural competence

Available on Moodle

Grading

Parts of assessment
A: written/oral assessment during the semester
B: oral test

Attendance: minimum 80% required

Grading

Parts of assessment
A: written/oral assessment during the semester
B: oral test

Attendance: minimum 80% required

cross-faculty course:
Rules for registration: www.uni.li/cross-faculty

Das nachhaltige Agieren innerhalb der historischen Baukultur wird kritisch analysiert und Lehren für die zeitgenössische Architektur und Baukonstruktion entwickelt. Das Essential-Modul gibt einen Überblick über die Geschichte der, für die Architektur und das Bauwesen relevanten technischen Entwicklungen. Das Thema Konstruktionsgeschichte wird anhand der Werkstoffe Holz, Lehm, Mauerwerk, Eisen und Beton systematisch aufbereitet. Grundlegende Fragen der Materialwahl, von Bau- und Konstruktionsweisen werden im Kontext vorhandener Ressourcen, klimatischer Bedingungen und gesellschaftlicher Faktoren wie wirtschaftlicher Verhältnisse und politischer Kontexte behandelt. Es gilt zudem, dieses Wissen und die Zusammenhänge diskursiv zu vermitteln und zu formulieren.
Am Ende des Moduls sollen die Studierenden fähig sein, die sich im geschichtlichen Kontext ändernden Wechselwirkungen von Tragwerk und Konstruktion zu kennen und die konstruktionsgeschichtlichen Grundlagen für ihren eigenen Entwurfsprozess zu nutzen.
Das Modul dient der anwendungsorientierten Horizonterweiterung durch Vermittlung und Aneignung von Wissen. Dem Instrument der Vorlesungen (und Exkursionen) können Übungen im Selbststudium gegenüberstehen.

Mögliche Methoden:
angeleitetes Praktikum, Experiment, Modell, Plan, Projektarbeit, Recherche, Reflexion, Skizze, Übung, Visualisierung, Diskurs, Exzerpieren, Foto, Zeichnung

Modulnote = Lehrveranstaltungsnote die ermittelt wird aus:
Projektentwürfe, Präsentation mit Kritik, Mitarbeit im Unterricht; mögliche Abschlussprüfung; 75% Anwesenheitspflicht, prüfungsimmanent.

Formale und inhaltliche Aspekte der Erstellung eines Exposés für die Bachelor-Thesis

Intensive Diskussionen zur Themenfindung und -präzisierung, Exposé-Erstellung und Präsentation

Voraussetzung für die Anmeldung zum Modul:

• erfolgreicher Abschluss des Moduls Research Methods I

• Exposé
• mündliche Mitarbeit
• Schlusspräsentation