Short description
The course focuses on judgment and decision making, with emphasis on how decisions deviate from rational and/or ethical standards, with applications in human-computer interaction.

Topics

• Introduction to decision making under certainty and risk
• Measuring and modeling individual risk preferences
• Heuristics in decision making
• Biases in decision making
• Emotions in decision making
• Designing decisions on websites

• 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 how decisions can be influenced by various human biases and how to improve individual decisions.
• Students will know basic methods of decision making in order to overcome human biases.
• Students will use methods of decision making in order to improve business decisions in organizations.

Written exam (90 minutes)

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 analyticsStudents will assess the assumptions and quality of statistical modelsStudents will select and apply the right statistical models for a given task or data setStudents 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)

Basics and assignments of controlling (functions und roles of enterprises, concepts of leadership/corporate governance, enterprises as a complex system, control mechanism for enterprises), fundamentals of financial statement analysis, Du Pont ratio system of financial control, financial accounting as analysis and information instrument (flow statement, direct and indirect cash flow determination, cash flow statement, case studies), operative control by financial ratios (liquidity, stability, profitability), planning of capital requirements for a start-up planning, prospective control by financial ratios (planning instruments, assignments of operative planning, earnings plan/budgeted income and loss statement, financial budget, budgeted balance sheet), value based corporate management, cost planning und variance analysis.

Der Inhalt des Wahlfaches ist die visuelle Annäherung an Räume und Raumfolgen. Im überwiegenden Teil des Wahlfaches wird die Arbeitsform der Bildsequenz und der systematischen Bildanordnung in der analogen sowie digitalen Fotografie behandelt, sowie die Aussenaufnahmen von Gebäuden. Ein weiterer Schwerpunkt liegt in der Erstellung von Architekturmodell-Fotografien - dem Arbeiten mit Kunstlicht, Perspektiven und Fokus anhand von Architekturmodellen der Studierenden.
Zusätzlich werden Interviews mit Architekturbüros aus der Region u.a. zu den Themen Architektur-darstellung und Unternehmenskommunikation geführt:

Fachstudio mit Blockunterricht ( Vorlesung und Übung)
In Form von Vortrag, Projektarbeiten, Übungen, Recherche, Videos, Visualisierung, Interviews, Präsentation, Peerfeedback, Zeichnung und Plänen.

Modulnote = Lehrveranstaltungsnote die ermittelt wird aus:
Fachprojekt, Übungen, Peerfeedback und der Mitarbeit im Unterricht
Anwesenheitspflicht: min. 75% verpflichtend

• Konkretisierung und Bearbeiten eines Forschungsproblems, ausgedrückt anhand einer Forschungsfrage.
• Entwicklung einer Problemlösung zur definierten Forschungsfrage anhand der wissenschaftlichen Methodik des Faches.
• Eigenständige Literaturrecherche Auswertung, Diskussion und Abgrenzung der Literatur.
• Diskussion mit dem Gutachter über methodische und inhaltliche Fragen zur Lösung der Forschungsfrage.
• Eigenständiges Verfassen einer wissenschaftlichen Arbeit
• Präsentation des eigenen Forschungsvorhabens und der Ergebnisse.
• Verteidigung der Thesis und Fachdiskussion mit der Prüfungskommission.

Voraussetzung für die Anmeldung zum Modul:

• erfolgreicher Abschluss aller Module des 1. Regelstudienjahres
• erfolgreicher Abschluss einer der Lehrveranstaltungen
• Exposé Greenhouse IFS
• Exposé Greenhouse IME
• Exposé Greenhouse IMIT
• Exposé Greenhouse IFS, IME, IMIT

Für die Anmeldung zur LV "Erstellung BT" wird das im Modul Research Methods II erstellte und durch den/die Gutachter/in und die Studienleitung freigegebene Exposé als Grundlage herangezogen.

Falls Sie ein verändertes Exposé als Basis ihrer BT verwenden möchten, ist ein erneuter Freigabeprozess notwendig, dieser muss spätestens bis zur Anmeldefrist des Moduls abgeschlossen sein. Der Prozess ist durch den Studierenden anzustossen und zu betreiben. Bitte kontaktieren Sie in diesem Fall umgehend die Modulleitung.


Bachelorthesis: 70% (schriftlich)
Präsentation und Verteidigung: 30% (mündlich vor Gremium)

Bitte beachten Sie die Hinweise / Regelungen im Leitfaden zum Modul Bachelorthesis. Dieser ist unter Richtlinien/Reglemente zum Download verfügbar.

Short description
The course provides an introduction to cyber security and covers selected issues related to computer and information security.

Topics

• Security goals and design principles
• Economic aspects of security and risk analysis
• Basics of cryptography
• Authentication and access control
• Security mechanisms of common operating systems
• Key instruments of network security
• Key instruments of web security
• Software security, vulnerabilities, and attacks
• Security management and standards

The module involves interactive lectures with exercises to integrate theoretical knowledge, practical design, and analysis skills. Lab exercises will be used to support the acquisition of practical skills.

• Students will learn about the main concepts and methods of computer and information security.
• Students will understand the principles and key requirements of security management.
• Students will be able to analyze, configurate, and manage practical security instruments.

Short description
This course covers some statistical methods that can help to take decisions in business using data. These basic concepts of the statistical testing and estimating theory should – to a large extent - be known from an introductory course on probability theory and statistics in any bachelor program.

Topics

• Graphical and numerical characterizations of random variables and their distributions
• Framework and basic applications of testing hypotheses and estimating parameters
• Ordinary least squares method and its properties
• Simple linear regression including parameter estimation, diagnostic plots, hypothesis testing, predictions and model specifications using log-transformations
• Introduction to the software package R

• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
• Students are usually asked to read corresponding parts of the lecture notes or of the textbook in order to prepare for the upcoming lectures in advance.
• In the interactive lectures, statistical concepts will be introduced and motivated by discussing examples in detail. Assignments are offered to train these skills.
• During office hours, individual problems may be discussed with the lecturer.
• In order to analyse realistic data, the software package R will be used.

• Students present the distributions of random variables graphically, calculate and interpret their moments.
• Students can explain the framework of testing hypotheses and estimating parameters and apply basic procedures.
• Students criticize the assumptions of basic testing and estimating procedures and generalize the conclusions correctly.
• Students derive the minimal sample size for basic testing and estimating procedures.
• Students apply the ordinary least squares method to derive estimators and compare the statistical properties of different estimators.
• Students explain the classical linear model assumptions, run simple linear regressions, check the diagnostics plots, use log-transformations to specify models and interpret the results correctly.

• Short descriptionThe 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.TopicsConceptual foundations of data warehouses and on-line analytical processing (OLAP)Conceptual foundations of in-memory column-based databasesSAP BW Data Modeling & Business ExplorerSAP BusinessObjects Cloud and EnterpriseIn-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 databasesStudents extract, transform, and load data from transactional systems into business intelligence solutionsStudents design and develop business intelligence reports and dashboards

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

In this course we discuss some statistical methods that can help to take decisions in business using data. After reviewing the basic concepts of ''Testing and Estimating'', usually known from an introductory course on probability theory and statistics in any bachelor program, we introduce and discuss some aspects of ''Multiple Linear Regression Analysis'', which can be regarded as one of the practically most relevant statistical techniques.

Topics:

• graphical and numerical characterizations of random variables and their distributions
• framework and basic applications of testing hypotheses and estimating parameters
• ordinary least squares method and its properties
• parameter estimation in multiple linear regression
• classical linear model assumptions and model diagnostics
• inference in multiple linear regression
• model specification techniques
• model selection techniques
• introduction to the software package R

Students are usually asked in advance to read corresponding parts of the textbook (Wooldridge, 2009) in order to prepare for the upcoming lectures. In the interactive lectures, we then introduce the statistical concepts and motivate them by discussing examples in detail. Assignments are then offered to train these skills. During the office hours, individual problems may finally be discussed with the lecturer. In order to analyze realistic data, the software package R will be used.

The same teaching methods will be used in the two different lecture series ''Testing and Estimating'' and ''Multiple Linear Regression Analysis''.

The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.