The Introduction Week introduces you to living in Liechtenstein and studying at the University of Liechtenstein.

By the end of the week you will have
- met other international students and the International Office team
- become familiar with university departments and support services
- familiarised yourself with the local area and important Liechtenstein institutions
- built a foundation level of passive and active German language skills
- received information about Liechtenstein as a country and place to study
- experienced culture and lifestyle in Liechtenstein and the surrounding area

The German lessons and afternoon activities are key elements of the introduction week. Afternoon activities promote the practical application of language skills introduced in the German lessons and will help you practise using German for communication in real-life situations.

To obtain 2 ECTS you will need to:
- Participate actively in all lectures and events of the introduction week
- Do outside of class work as required (see schedule)

After the Introduction Week, German lessons continue on a weekly basis (German as a foreign language elementary / intermediate / advanced level, 2 teaching units and 3 ECTS credit points each). Please note that you need to register for this class within the registration period.

This course is designed to give first year PhD Students an aid for their academic endeavour. Just like in Research Design, the focus lies on methodological competences. At the same time, however, this course also aims at techniques rather than design strategies. The objective is to provide core compentences on how to craft a scientific text properly. Due to the concept of peer-monitoring applied in this course also social competencies will be trained.

During the first year students will be working on their academic writing style, they will be made familiar with normative writing styles and ways to publish tackling various kinds of genres, and they will help and learn from each other through peer-monitoring activities. As a base sample texts will be used and the texts students will be producing will be worked on. The course is built on three pillars:

• Knowledge Management:
  Working with databases, literature management softwares, etc.
• Publishing:
  How to write and publish various genres: abstracts, research papers, articles, data commentaries, reviews, project proposals, formatting, etc.
• Peer-Mentoring:
  Giving and receiving feed-back, presenting and reviewing, considering peer-feedback, joint writing activities, etc.

Workshops, one-on-one and think-pair-share sessions, individual and guided e-learning.

Students will be acquainted with principles of academic writing, normative writing, publishing, and peer-mentoring.

By the end of the course they will be able to make use of academic vocabulary, they will be able to discuss texts, tables, charts, and figures, and they will be sensitised about their personal and academic command of the English language.

They will be familiar with reference management systems, working with databases, formatting written texts, and academic values.

They will know principles of certain academic genres, like abstracts, research papers, articles, data commentaries, reviews, project proposals, etc.

They will be able to give and consider peer-feed-back, present and review, and they will be able to carry out joint writing activities, etc.

• identify exemplary A-journal papers of your field
• provide own texts for review
• prepare and present reviews
• participate in text discussions
• prepare and present learning input

In the defence the doctoral students prove whether they have fulfilled the requirements of the dissertation.

The defence can be held when the dissertation has been recommended for acceptance in the written appraisal of the supervisors and each supervisor has awarded a minimum grade of 4.0.

The Doctoral Examination Board holds the defence and determines which grade is awarded.

Short description
The course covers various statistical techniques for making sense of the vast and complex data sets that have emerged in business in the past twenty years. Students will learn to detect patterns in large data sets of various formats (quantitative and qualitative) and translate them into actionable insights.

Topics

• Data Visualization and Exploration
• Supervised learning techniques for regression (e.g. linear regression)
• Supervised learning techniques for classification (e.g. classification trees)
• Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
• Deep Learning Fundamentals
• Text mining (e.g. topic modeling)
• Hands-on labs with Python

The module integrates theoretical knowledge and practical skills in an interactive lecture. The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

• Students will know and understand the basic concepts and methods of data mining and predictive Analytics
• Students will assess the assumptions and quality of statistical models
• Students will select and apply the right statistical models for a given task or data set
• Students will derive actionable insights from statistical results

Written exam (90min)

Short description
The course covers the important aspects of modern data management, from design and querying to transaction processing and from traditional to present-day data-driven applications. Students will learn how to handle various data formats, assess and eventually improve data quality, and handle data using SQL, NoSQL, and Hadoop technologies. The course will also look into the basics of mining (big) data sets.

Topics

• Modern data management requirements
• Database system architecture
• Database design using the ER model
• Relational databases (SQL)
• Concurrency control techniques
• NoSQL databases (e.g., MongoDB)
• Apache Hadoop (HDFS, MapReduce)

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• Selected sessions will also require preparation of the participants through videos that will be provided in advance.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

• Students will acquire and understand foundational concepts and methods of modern data management
• Students will study the preparation of data in order to enable data-driven applications
• Students will select and apply appropriate technologies for building data-driven applications

Written exam (90min)

Short description
The course covers conceptual foundations, implementation, and operations of Business Intelligence solutions. Students will learn how to design and operate data warehouses, reports and dashboards, based on SAP BW, SAP BusinessObjects, as well as SAP HANA.

Topics

• Conceptual foundations of data warehouses and on-line analytical processing (OLAP)
• Conceptual foundations of in-memory column-based databases
• SAP BW Data Modeling & Business Explorer
• SAP BusinessObjects Cloud and Enterprise
• In-Memory Computing with SAP HANA

The module integrates theoretical knowledge and practical skills in an interactive seminar. The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

• Students know and understand foundational concepts and methods of data warehousing and on-line analytical processing (OLAP)
• Students know and understand foundational concepts and methods of in-memory column-based databases
• Students extract, transform, and load data from transactional systems into business intelligence solutions
• Students design and develop business intelligence reports and dashboards

Grade consists of exam (50%), project (40%) and its presentation (10%).

Das Modul umfasst eine grundlegende Einführung in die Managementlehre. Dies wird einerseits über die Vorlesungen zu den Grundlagen des Managements sowie zu Marketing und andererseits über ein integratives Zahnradelement erreicht.
Im Detail vermittelt das Modul ein Grundverständnis der wichtigsten Elemente von Unternehmen und deren Zusammenhänge. Andererseits werden Themen aufgegriffen, die den Mitarbeiter und die Mitarbeiterführung im Unternehmen betreffen. Ein weiterer Fokus liegt auf dem Themenbereich Marketing.

Die Veranstaltung dient dazu, die Studierenden mit den grundlegenden makroökonomischen Zusammenhängen und Gesetzmässigkeiten in Volkswirtschaften vertraut zu machen. Es geht darum, gesamtwirtschaftliche Entwicklungen zu beschreiben und gewisse gesamtwirtschaftliche Beziehungen zu erklären. Ein internationaler Vergleich kann hierbei Unterschiede und Gemeinsamkeiten in der Entwicklung von Volkswirtschaften herausarbeiten. Darüber hinaus sollen die Studierenden sensibilisiert werden für aktuelle wirtschaftspolitische Probleme, eine Herausforderung stellt die Finanz- und Wirtschaftskrise 2008/2009 und die Auswirkungen bis in die jüngste Zeit.

Basics in cost accounting (functions und structure), theory of costs and structures of costs (classification by attribution to units of output, classification of costs by capacity volume variance, classification und valuation of activities), systems of cost accounting, full cost accounting, cost type accounting (functions and structure, schema for transition from expenses to costs), cost centre accounting (functions and determination of cost centres, expense distribution sheet clearing of primary or secondary costs centres, determination of overhead rates), product cost accounting (cost unit accounting as job costing, as process costing or as joint costing, cost unit period accounting), direct costing (high-low points method, cost splitting), cost accounting schema, direct costing (as unit costing versus as period costing), level-by-level covering of fixed costs, budgeted costs und activity costing, decision making, accounting ratios, transition to controlling.

Basics, assignments und objective targets of financial statements analysis, financial presentation practice (by real facts, by rights to elect a valuation, by balance sheet disclosure), types of financial statements analysis (internal, external), methods/arrangements of financial statements, ratios of financial statements analysis in an overview, financial ratios, efficiency ratios, liquidity ratios, Kralicek's Quicktest, Du Pont ratio system of financial control (ROI, ZVEI), discriminant analysis, window dressing and accounting fraud, case studies for comprehensive financial statements analysis, modern financial statements analysis (including equity based financial statements analysis).

International accounting systems in an overview, setting up of international accounting, components of annual individual financial statements (International Financial Reporting Standards - IFRS, dealing with the most important IFRS-standards und working out differences to national accounting relating to commercial law, adjustment of a financial statement relating to commercial law concerning a financial statement relating to IFRS, basics of annual consolidated financial statements (relating to commercial law versus IFRS).