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Wahlfach A: Architekturdarstellung

Wahlfach A: Architekturdarstellung

Study Programmes
Bachelorstudiengang Architektur (BSc AR 14) (01.09.2014)
Project Description
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:
Teaching Method
Fachstudio mit Blockunterricht ( Vorlesung und Übung)
In Form von Vortrag, Projektarbeiten, Übungen, Recherche, Videos, Visualisierung, Interviews, Präsentation, Peerfeedback, Zeichnung und Plänen.
Assessment Methods
Modulnote = Lehrveranstaltungsnote die ermittelt wird aus:
Fachprojekt, Übungen, Peerfeedback und der Mitarbeit im Unterricht
Anwesenheitspflicht: min. 75% verpflichtend
Module number:
4708972
Semester:
SS 19
ECTS Credits:
2
Courses:
16 L / 12 h
Self-study:
48 h
Sprache:
Deutsch
Scheduled Semester:
4 und 6

C12_Bachelorthesis

C12_Bachelorthesis

Module Coordinator/Lecturers
Study Programmes
Bachelorstudiengang Betriebswirtschaftslehre (BSc BWL 12) (01.09.2012)
Learning Objectives
  • 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.
Literature
Der Studierende identifiziert die für die Bearbeitung seines Forschungsvorhabens relevante Literatur eigenständig.
Requirements (formal)
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
Assessment Methods
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)
Grade
Bitte beachten Sie die Hinweise / Regelungen im Leitfaden zum Modul Bachelorthesis. Dieser ist unter Richtlinien/Reglemente zum Download verfügbar.
Module number:
4706594
Semester:
SS 19
ECTS Credits:
12
Courses:
30 L / 23 h
Self-study:
338 h
Sprache:
Deutsch/Englisch
Scheduled Semester:
6

C15 Cyber Security

C15 Cyber Security

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15) (01.09.2015)
Project Description
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
Teaching Method
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.
Learning Results
  • 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.
Literature
to be announced
Module number:
4609526
Semester:
WS 18/19
ECTS Credits:
3
Courses:
35 L / 27 h
Self-study:
64 h
Sprache:
Englisch
Scheduled Semester:
1

C15 Business Statistics I

C15 Business Statistics I

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15) (01.09.2015)
Project Description
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
Teaching Method
  • 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.
Learning Results
  • 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.
Literature
Compulsory reading
  • Wooldridge, J.M. (2013). Introductory Econometrics. (International Student Edition, 5th edition). Mason: South Western Cengage Learning.

Further reading
  • Sweeney, D.J., Williams, T.A., David R. Anderson, D.R. (2009). Fundamentals of Business Statistics (International Student Edition, 5th edition). Manson: South-Western Cengange Learning.
  • Berensen, M.L., Levine, D.M., Krehbiel, T.C. (2012). Basic Business Statistics (Global Edition, 12th edition), Essex: Pearson Education Limited.
Module number:
4608127
Semester:
WS 18/19
ECTS Credits:
3
Courses:
28 L / 21 h
Self-study:
69 h
Sprache:
Englisch
Scheduled Semester:
1

C15 Business Intelligence

C15 Business Intelligence

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15) (01.09.2015)
Project Description
  • 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
Teaching Method
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.
Learning Objectives
  • 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
Literature
  • Further reading:Egger et al. (2007). SAP Business Intelligence, SAP Press, ISBN: 978-1-59229-082-6Plattner, H. (2009). A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database, Hasso Plattner Institute for IT Systems Engineering University of Potsdam.Inmon, W. H. (2005). Building the Data Warehouse, (4th ed) John Wiley.Grossmann, W. & Rinderle-Ma, S. (2015). Fundamentals of Business Intelligence, Springer (ISBN 978-3-662-46530-1), Ch. 1-4https://help.sap.com/viewer/p/SAP_ANALYTICSPlattner, H. & Leukert B. (2015). The in-memory revolution. How SAP HANA Enables business of the futureCodd, E.F. et al. (1993). Providing OLAP (Online Analytical Processing) to User-Analysts: An IT Mandate, White Paper Hyperion Solutions, E.F. Codd AssociatesEgger N., Fiechter J.-M. & Rohlf J. (2005). SAP BW - Data Modeling, SAP Press (ISBN: 1-59229-043-4)Plattner, H. & Zeier, A. (2011). In-Memory Data Management. An Inflection Point for Enterprise Applications, Springer, ISBN-13: 9783642193620Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3Salmeron, Jose L. (2003). EIS Success: Keys and difficulties in major companies. Technovation, 23 (1), 35–38.Kimball, R. & Ross M. (2013). The Data Warehouse Toolkit. The Definitive Guide to Dimensional Modeling, John Wiley & Sons, Inc. ISBN: 978-1-118-53080-1
Assessment Methods
Grade consists of exam (50%), project (40%) and its presentation(10%).
Module number:
4608153
Semester:
WS 18/19
ECTS Credits:
6
Courses:
54 L / 41 h
Self-study:
140 h
Sprache:
Englisch
Scheduled Semester:
3

Business Statistics

Business Statistics

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang IT and Business Process Management (MSc BPM 08) (01.10.2008)
Project Description
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
Teaching Method
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.
Literature
The students will be provided with the lecture slides and supporting materials (literature and exercises) throughout the course.

Compulsory reading:
  • Wooldridge, J.M. (2009). Introductory Econometrics. (International Student Edition, 4th edition). Mason: South Western Cengage Learning.

Further reading:
  • Montgomery, D.C., Peck, A.E. & Vining, G.G. (2012). Introduction to Linear Regression Analysis. (5th edition). New York: John Wiley & Sons.
  • Faraway, J.J. (2005). Linear Models with R. Boca Raton: Chapman & Hall/CRC.
Module number:
4608549
Semester:
WS 18/19
ECTS Credits:
5
Courses:
60 L / 45 h
Self-study:
105 h
Sprache:
Englisch
Scheduled Semester:
1

C15 Research Greenhouse

C15 Research Greenhouse

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15) (01.09.2015)
Module number:
4608116
Semester:
WS 18/19
ECTS Credits:
3
Courses:
28 L / 21 h
Self-study:
69 h
Sprache:
Englisch
Scheduled Semester:
3

C15 Quantitative Finance

C15 Quantitative Finance

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15) (01.09.2015)
Project Description
Quantitative Finance will cover:
  • Classical linear regression model assumptions and diagnostic tests
  • Expansions of the simple linear regression model to multiple linear regressions
  • Long-run relationships in finance
  • Models of time series volatility and covariances
  • Simulational methods in finance
  • Introduction to ThomsonReuters Eikon
Assessment Methods
See lectures within this module.
Class participation in "Data Sourcing and Analysis" is obligatory.
Module number:
4608077
Semester:
WS 18/19
ECTS Credits:
6
Courses:
58 L / 44 h
Self-study:
137 h
Sprache:
Englisch
Scheduled Semester:
1

C15 Private Banking

C15 Private Banking

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15) (01.09.2015)
Module number:
4608288
Semester:
WS 18/19
ECTS Credits:
3
Courses:
28 L / 21 h
Self-study:
69 h
Sprache:
Englisch
Scheduled Semester:
3

C15 Pension Finance

C15 Pension Finance

Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15) (01.09.2015)
Module number:
4608114
Semester:
WS 18/19
ECTS Credits:
3
Courses:
28 L / 21 h
Self-study:
69 h
Sprache:
Englisch
Scheduled Semester:
3
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