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Qualification objectives of the Master’s Programme
Competences
A) Subject-specific competences A1) Broadening of knowledge To make sure that all students meet the same academic standards, they will attend fundamental courses from outside their original academic field during their first and second semester. At the end of the second semester, all students have a basic knowledge in the following areas: computer sciences, mathematics, statistics and social-scientific methods. The students will be able to collect, manage and analyse data of different kinds and from various sources, to interpret and communicate the results in order to gain new insights and to support decision making. A2) Deepening of knowledge In the subsequent semesters, the students select advanced modules from the Master’s programmes at the participating departments or they join the GSDS doctoral programme as a “fast-track” option. A data analysis project, which may take the form of an internship, will serve the development of practical skills. The Master’s thesis is then to be written in the student’s field of choice. B) Generic competences The students are in a position to quickly and independently delve into new subjects. They can apply the methods acquired during the Master's programme. The students are able to present their findings in English. They can enter into a critical dialogue with others about the underlying premises and the methods used to derive the findings.
Learning outcomes
− In written exams, students prove that they have an in-depth understanding of the core concepts in social and economic data analysis and that they can apply these concepts to solve simple problems in a short time.
− In tutorials, students show that their knowledge and skills enable them to also solve more complex tasks.
− In tutorials, students work successfully in teams. They present their results to other students, who discuss these results critically.
− In advanced classes, students write short essays that satisfy scientific standards and reveal a detailed knowledge in special areas.
− In seminars, students show that they can grasp the essence of scientific papers and can organize the insights distilled from the literature in a well-structured manner. They communicate these insights to their fellow students and respond adequately to critical questions from the audience. Moreover, they formulate critical questions about other students' presentations.
Module Handbook Master’s Programme in Social and Economic Data Analysis
2
− The students write seminar papers on topics of their choice. For this purpose, they draw on the modern scientific literature and relate the findings in a meaningful way. They develop own ideas for small research projects and design approaches to solving these problems.
− In the Master's thesis, students demonstrate their ability to formulate more extensive research questions and to address them with the help of modern tools. They organize the time period of several months for the preparation of the thesis independently and effectively. They are successful in developing a clear and logical structure for an extensive research project. They critically assess the applied methods and premises and derive convincing conclusions.
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Contents
1 Introduction ............................................................................................................................................. 4
2 Subject Area 1: Foundations of Data Analysis ........................................................................................ 5
2.1 Computer Sciences ........................................................................................................................... 5
Datenbanksysteme .............................................................................................................................. 5
Algorithmen und Datenstrukturen ....................................................................................................... 7
Konzepte der Informatik mit Programmierkurs I ................................................................................. 8
Konzepte der Programmierung mit Programmierkurs II ................................................................... 10
2.2 Mathematics .................................................................................................................................... 12
Mathematik für Wirtschaftswissenschaftler I ..................................................................................... 12
Mathematische Grundlagen der Informatik ....................................................................................... 13
Lineare Algebra I ............................................................................................................................... 14
2.3 Statistics .......................................................................................................................................... 16
Statistik .............................................................................................................................................. 16
Statistics I (Economics) ..................................................................................................................... 17
Statistics I (Psychology) .................................................................................................................... 18
Statistik I ............................................................................................................................................ 19
2.4 Social-scientific Methods ................................................................................................................ 20
Econometrics I ................................................................................................................................... 20
Introduction to Survey Methodology .................................................................................................. 21
Methoden der empirischen Politk- und Verwaltungsforschung ......................................................... 23
Empirie: Quantitative Methoden ........................................................................................................ 24 3 Subject Area 2: Advanced Methods of Data Analysis .......................................................................... 25
Probability Theory and Statistical Inference ......................................................................................... 25
Research Design I ................................................................................................................................ 27
Big Data and Scripting .......................................................................................................................... 29
Optional Course .................................................................................................................................... 30
Seminar Module .................................................................................................................................... 31 4 Subject Area 3: Optional compulsory section ....................................................................................... 32
Optional Module .................................................................................................................................... 32
Data Analysis Project ............................................................................................................................ 33 5 Subject Area 4a: PhD Module (Study Track A) .................................................................................... 34
6 Subject Area 4b: Master’s Thesis (Study Track A) ............................................................................... 35
7 Subject Area 4: Master’s Thesis (Study Track B) ................................................................................. 36
Module Handbook Master’s Programme in Social and Economic Data Analysis
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1 Introduction
This handbook provides outlines of the modules offered for the Master’s Programme in Social and Economic Data Analysis. Aside from the Master’s thesis, the modules are:
− courses comprising lectures and tutorials − courses comprising lectures − seminars.
The modules are grouped under the subject area to which they belong: 1. Foundations of Data Analysis 2. Advanced Methods of Data Analysis 3. Optional compulsory section 4a. PhD Module (Study Track A)
Each module outline gives the following information: Applicability Specifies the subject area to which the module belongs. Credits Each module has a credit value based on the student’s workload
required to successfully complete the module, in accordance with the European Credit Transfer and Accumulation System (ECTS). To complete the Master’s programme, 120 credits in total are required and 30 credits per semester should be accumulated. Track A students, aside from the Master’s thesis (15 credits), need to obtain 105 credits in taught modules (courses and seminars). Track B and C students, aside from the Master’s thesis (30 credits), need to obtain 90 credits in taught modules (courses and seminars).
Learning Outcomes Describe what students should be able to do on completing the module. Content of Teaching Describes the topics covered in the module. Teaching Methods / Hours per Week
The type of module (a course comprising lectures, with or without tutorials, or a seminar) and its hours of tuition per week.
Workload The workload indicates the time students typically need to spend to successfully complete the module.
Recommended Background
Indicates whether specific prior knowledge would be beneficial for completing the module.
Language The modules of the Master’s programme are taught in English or German.
Frequency Offered The semester in which the module is taught (winter semester, summer semester or both winter and summer semester).
Recommended Semester
Specifies in which semester it is recommended to take the module.
Compulsory / Optional Informs whether the module must be taken to complete the Master’s programme. This applies, for instance, to Subject Area 2: Advanced Methods of Data Analysis. All modules of this subject area must be taken.
Department Specifies at which Department the module is taught. Furthermore, exams must be registered at this Department. It is not coercive that the Lecturer’s Department and the Department at which the module is taught coincide.
Module Handbook Master’s Programme in Social and Economic Data Analysis
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2 Subject Area 1: Foundations of Data Analysis
2.1 Computer Sciences
Datenbanksysteme
Applicability
Subject Area 1: Foundations of Data Analysis / Computer Sciences
Credits 9 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Die Veranstaltung vermittelt einen grundlegenden Überblick über Funktionalität, Architektur und Realisierungskonzepte von Datenbanksystemen als Grundlage für computergestützte Informationssysteme. Charakteristisch für Datenbanksysteme ist, dass Informationen gemäß irgendeinem Modell dargestellt, gespeichert und aufbewahrt werden, die mittels Operationen einer geeigneten Sprache abgefragt (wiedergewonnen) und manipuliert werden können. In dieser Veranstaltung werden sowohl die Modellierungs- als auch die Nutzungsaspekte von Datenbanksystemen vermittelt: z.B. Entity-Relationship- und Relationale Datenmodellierung, Relationale Entwurfstheorie und Normalformen, Datenbanksprachen (insbes. Algebra, Kalkül, SQL), ACID-Transaktionen. Die Lehrveranstaltung liefert Grundlage für weiterführende Lehrveranstaltungen aus den Gebieten Datenbanken, Informationssysteme und Information Retrieval.
Content of Teaching
Absolventen des Kurses haben grundlegendes Verständnis über den Aufbau und die Funktionsweise von Datenbanksystemen und deren Nutzung. Sie haben fundiertes Wissen über konzeptionelle Datenmodellierung mit Hilfe des Entity-Relationship-Modells und die Abbildung auf relationale Datenbankschemata. Sie können die grundlegenden Sprachkonstrukte von SQL mittels mathematisch präziser formaler Sprachen (Algebra, Kalkül) analysieren und können SQL-Anfragen und -Änderungsoperationen selbständig formulieren und anwenden. Sie haben die prinzipiellen Realisierungstechniken solcher deklarativer Sprachen kennen gelernt und können bestehende SQL-Anwendungen analysieren und bewerten. Sie sind in der Lage, grundlegende Informationssystem-Funktionalitäten selbständig zu realisieren. Die Funktionsweise und Abstraktionsmechanismen der transaktionsorientierten Verarbeitung sind ihnen bekannt, sie können Synchronisations- und Recovery-Probleme erkennen und grundsätzliche Lösungsmöglichkeiten aufzeigen.
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 270 Stunden
Type of Assessment
Prüfung: Klausur von 120min Dauer. Die erfolgreiche Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur. Die Note ergibt sich aus der Klausurnote.
Recommended Background
Entsprechend den Modulen Mathematik: Diskrete Strukturen (paralleler Besuch) und Konzepte der Informatik, elementare Programmierkenntnisse.
Language German
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Frequency Offered
Summer semester
Recommended Semester
2
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Computer and Information Sciences
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Algorithmen und Datenstrukturen
Applicability
Subject Area 1: Foundations of Data Analysis / Computer Sciences
Credits 9 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Kenntnis elementarer Algorithmen und Datenstrukturen. Auswahl und Effizienzbeurteilung von alternativen Verfahren und Implementationen in Anwendungsszenarien. Fähigkeit zu deren Anpassung an gegebene Umstände sowie Entwurf neuer Algorithmen und Datenstrukturen.
Content of Teaching
In der Vorlesung werden Standardalgorithmen und grundlegende Datenstrukturen behandelt. Darstellungsformen und Spezifikation von Algorithmen, elementare und höhere Datenstrukturen, Suchbäume, Hash-Tabellen, rekursive Algorithmen, Algorithmen zum Suchen und Sortieren, grundlegende Graphenalgorithmen und Zeichenkettenalgorithmen. In theoretischen Übungen wird der Vorlesungsstoff vertieft, in praktischen Übungen werden Algorithmen und Datenstrukturen in Java implementiert.
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 270 Stunden
Type of Assessment
Prüfung: Klausur. Die erfolgreiche Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur. Die Note ergibt sich aus der Klausurnote.
Recommended Background
elementare Programmierkenntnisse in der Programmiersprache Java
Language German
Frequency Offered
Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Computer and Information Sciences
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Konzepte der Informatik mit Programmierkurs I
Applicability
Subject Area 1: Foundations of Data Analysis / Computer Sciences
Credits 12 Cr Dauer 1 Sem.
Anteil des Moduls an der Gesamtnote 6.67%
Module Grade Klausur von Konzepte der Informatik
Module units − Konzepte der Informatik − Programmierkurs I
Learning Outcomes Absolventen kennen und verstehen die Grundlagen der Informationscodierung, -speicherung und -verarbeitung.
Module unit: Konzepte der Informatik
Content of Teaching − Informationscodierung und - speicherung - Codierung von Zahlen und
Zeichen, Speicherbereiche, elementare Datentypen, Streuspeicherung
− Übersicht über die verschiedenen Programmierparadigmen, ausführlich den Kern imperativer Sprachen und Objektorientierung
− Algorithmen und Datenstrukturen - häufig verwendete Datenstrukturen wie Listen, Arrays, Stapel und Warteschlangen, Bäume und allg. Graphen; Eigenschaften von Algorihmen, insbesondere Algorithmenkomplexität und Korrektheit, sowie die algorithmische Konzepte Iteration und Rekursion, Teile und Herrsche, am Beispiel verschiedener Sortierverfahren
− Theoretische Grundlagen - Einführung in die Automatentheorie sowie formale Sprachen und Grammatiken; Fragen der Berechenbarkeit von Problemen, Komplexität und Korrektheit von Algorithmen
− Einführung in die Informationswissenschaft - Grundlagen, Information Retrieval, Wissensrepräsentation
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 180 Stunden
Credits for this unit 6 Cr
Type of Assessment
Prüfung: Klausur von 120min Dauer. Die erfolgreiche Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur.
Recommended Background
Konzepte der Informatik kann nur zusammen mit dem Programmierkurs I belegt werden.
Language German
Frequency Offered Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Module Handbook Master’s Programme in Social and Economic Data Analysis
9
Department Department of Computer and Information Sciences
Module unit: Programmierkurs I
Content of Teaching − Objektorientierte Programmierung - die in der Vorlesung "Konzepte
der Informatik" vorgestellten Konzepte objektorientierter Programmiersprachen wie Klassen, Vererbung, Polymorphismus, Ausnahmebehandlung oder generische Programmierung werden praktisch mit Java an Hand verschiedenster Beispiele geübt
− Imperative Programmierung - Befehlsorientierte Programmierung mit Methoden, Schleifen und Auswahlbefehle.
− Angewandte Programmierung - Programmqualität, Dokumentation und Testen von Programmen
Teaching Methods Hours per Week
Vorlesung (2 SWS) mit Übung (2 SWS)
Workload 180 Stunden
Credits for this unit 6 Cr
Type of Assessment − 2 von 3 Programmieraufgaben bestanden
− 50% der Punkte aus dem Programmiertest Recommended Background
Der Programmierkurs I kann nur zusammen mit Konzepte der Informatik belegt werden.
Language German
Frequency Offered Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every single student by the admissions committee.
Department Department of Computer and Information Sciences
Module Handbook Master’s Programme in Social and Economic Data Analysis
10
Konzepte der Programmierung mit Programmierkurs II
Applicability
Subject Area 1: Foundations of Data Analysis / Computer Sciences
Credits 9 Cr Dauer 1 Sem.
Anteil des Moduls an der Gesamtnote 6.67%
Module Grade Klausur von Konzepte der Programmierung
Module units − Konzepte der Programmierung − Programmierkurs II
Learning Outcomes Absolventen haben ein grundlegendes Verständnis von Programmierparadigmen und von funktionaler Programmierung. Sie sind in der Lage, selbständig kleinere Projekte in Haskell zu definieren und zu implementieren. Konzepte von Programmiersprachen sollen bewusst gemacht werden.
Module unit: Konzepte der Programmierung
Content of Teaching
Kern des Moduls ist eine Einführung in deklarative Programmierung. Im Unterschied zur imperativen Programmierung wird dabei durch den Programmierer idealerweise nur vorgegeben, was berechnet werden soll, aber nicht wie genau die Berechnung durchgeführt wird.
Am Beispiel der funktionalen Programmiersprache Haskell soll dieses Konzept eingeführt werden. Dabei werden Konzepte wie z.B. Seiteneffekte, Typsysteme, Auswertestrategien und Datenstrukturen erläutert, und aus formaler Sicht betrachtet.
Mit einer Einführung in den lambda-Kalkül wird die einfachste formale Grundlage fast aller Programmiersprachen vorgestellt, viele Haskell-Konstrukte lassen sich leicht darauf zurückführen.
Vorlesungsbegleitend gibt der “Programmierkurs 2” eine praktische Einführung in die Programmierung mit Haskell. Da Vorlesung und Programmierkurs inhaltlich sehr eng verzahnt sind, werden die Übungen zu beiden Veranstaltungen zusammengelegt.
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 120 Stunden
Credits for this unit 4 Cr
Type of Assessment
Prüfung: Klausur von 120min Dauer. Die erfolgreiche Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur.
Recommended Background
Konzepte der Programmierung kann nur zusammen mit dem Programmierkurs II belegt werden.
Language German
Frequency Offered Summer semester
Recommended Semester
2
Module Handbook Master’s Programme in Social and Economic Data Analysis
11
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Computer and Information Sciences
Module unit: Programmierkurs II
Content of Teaching
Kern des Moduls ist eine Einführung in deklarative Programmierung. Im Unterschied zur imperativen Programmierung wird dabei durch den Programmierer idealerweise nur vorgegeben, was berechnet werden soll, aber nicht wie genau die Berechnung durchgeführt wird.
Am Beispiel der funktionalen Programmiersprache Haskell soll dieses Konzept eingeführt werden. Dabei werden Konzepte wie z.B. Seiteneffekte, Typsysteme, Auswertestrategien und Datenstrukturen erläutert, und aus formaler Sicht betrachtet.
Mit einer Einführung in den lambda-Kalkül wird die einfachste formale Grundlage fast aller Programmiersprachen vorgestellt, viele Haskell-Konstrukte lassen sich leicht darauf zurückführen.
Vorlesungsbegleitend gibt der “Programmierkurs 2” eine praktische Einführung in die Programmierung mit Haskell. Da Vorlesung und Programmierkurs inhaltlich sehr eng verzahnt sind, werden die Übungen zu beiden Veranstaltungen zusammengelegt.
Teaching Methods Hours per Week
Vorlesung (2 SWS)
Workload 150 Stunden
Credits for this unit 5 Cr
Type of Assessment
Prüfung: Klausur von 120min Dauer. Die erfolgreiche Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur.
Recommended Background
Der Programmierkurs II kann nur zusammen mit Konzepte der Programmierung belegt werden.
Language German
Frequency Offered Summer semester
Recommended Semester
2
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Computer and Information Sciences
Module Handbook Master’s Programme in Social and Economic Data Analysis
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2.2 Mathematics
Mathematik für Wirtschaftswissenschaftler I
Applicability
Subject Area 1: Foundations of Data Analysis / Mathematics
Credits 9 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Einführung in die mathematische Denkweise, Beherrschung mathematischer Grundaufgaben, Anwendung der Differential- und Integralrechnung
Content of Teaching
− Grundbegriffe mathematischen Denkens: Mengen, Zahlen, Funktionen, Folgen und Reihen
− Einführung in die Differentialrechnung: Differentiation, Taylor-Entwicklung, Monotonie und Konvexität von Funktionen
− Integralrechnung und Integrationstechniken: Unbestimmte, bestimmte und uneigentliche Integrale, partielle Integration und Integration durch Substitution
− Differentialrechnung in mehreren Veränderlichen: Gradient und Hessematrix, implizite Funktionen, implizites Differenzieren
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 270 Stunden
Type of Assessment
Klausur
Recommended Background
Language German
Frequency Offered
Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Economics
Module Handbook Master’s Programme in Social and Economic Data Analysis
13
Mathematische Grundlagen der Informatik
Applicability
Subject Area 1: Foundations of Data Analysis / Mathematics
Credits 9 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Beherrschung grundlegender Konzepte der Diskreten Mathematik und Logik, wie sie für das Information Engineering vorausgesetzt werden. Die Studierenden lernen vor allem den Umgang mit eindeutiger Notation sowie die Formulierung und den Beweis von Aussagen über formale Sachverhalte. Außerdem die Beherrschung grundlegender Techniken der Analysis und der Linearen Algebra.
Content of Teaching
− Logik (Aussagen, Quantoren, Beweise)
− Mengen (Darstellungen, Operationen, Mengenfamilien)
− Relationen (Funktionen, Äquivalenzrelationen, Ordnungs-relationen, Graphen)
− Induktion (vollständige Induktion, strukturelle Induktion, transitive Hülle, Mächtigkeit von Mengen)
− Analysis (Konvergenz von Folgen und Reihen, oberer und unterer Grenzwert, Potenzreihen, Asymptotik)
− Lineare Algebra (Vektorräume, lineare Abbildungen, Eigenwerte und Eigenvektoren)
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 270 Stunden
Type of Assessment
Prüfung: Klausur von 120min Dauer. Die erfolgreiche Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur. Die Note ergibt sich aus der Klausurnote.
Recommended Background
Language German
Frequency Offered
Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Computer and Information Sciences
Module Handbook Master’s Programme in Social and Economic Data Analysis
14
Lineare Algebra I
Applicability
Subject Area 1: Foundations of Data Analysis / Mathematics
Credits 9 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Die Studierenden
− kennen grundlegende abstrakte mengentheoretische und algebraische Strukturen und Konstruktionen
− erstehen die axiomatische Methode und die Prinzipien der mathematischen Strenge
− sind in der Lage, abstrakte Sätze und Methoden auf konkrete mathematische Probleme anzuwenden
− analysieren lineare geometrische Sachverhalte mit abstrakten algebraischen und konkreten rechnerischen Methoden
− können einfachere Aussagen aus der linearen Algebra selbstständig beweisen
− sind in der Lage, die Richtigkeit komplexerer Aussagen aus der linearen Algebra zu rechtfertigen.
Content of Teaching
− Mengen, Abbildungen, Elemente der Logik
− Grundlegende algebraische Strukturen: Gruppen, Ringe, Körper, Vektorräume, lineare Abbildungen, Matrizen, Koordinaten, lineare Gleichungssysteme
− Polynome, Polynomdivision mit Rest. Determinante, Eigenwerte und Eigenräume, charakteristisches Polynom und Minimalpolynom
− Bilineare und multilineare Abbildungen, quadratische und alternierende
− Formen
− Skalarprodukte, Hilberträume
− Orthogonale und unitäre Abbildungen, adjungierte Abbildung, selbstad- jungierte und normale Abbildungen
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 270 Stunden
Type of Assessment
Prüfung: Klausur. Die erfolgreiche Teilnahme an den Übungen ist Voraussetzung für ein erfolgreiches Bestehen des Moduls. Die Note ergibt sich aus der Klausurnote.
Recommended Background
Language German
Frequency Offered
Winter semester
Module Handbook Master’s Programme in Social and Economic Data Analysis
15
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Mathematics and Statistics
Module Handbook Master’s Programme in Social and Economic Data Analysis
16
2.3 Statistics
Statistik (Dept. of Politics and Public Administration)
Applicability
Subject Area 1: Foundations of Data Analysis / Statistics
Credits 8 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Das Ziel dieser Vorlesung ist, den Studierenden fundiertes Wissen der Inferenz mittels statistischer Modelle zu vermitteln. Als Etappenziele für das Endziel gilt es, die Wahrscheinlichkeitstheorie, die Grundlagen der statistischen Modelle und ihre verschiedenen Varianten zu erlernen.
Content of Teaching
Die Veranstaltung umfasst die Grundlagen der beschreibenden Statistik und der schließenden Statistik ebenso wie eine Übersicht über multivariate Verfahren der Datenanalyse. Erläutert werden:
− Kennwerte univariater Häufigkeitsverteilungen
− diskrete Wahrscheinlichkeitsverteilungen
− stetige Wahrscheinlichkeitsverteilungen
− Schätz- und Testtheorie
− Signifikanztests
− Modelle bivariater Zusammenhänge
− Grundlagen der Drittvariablenkontrolle
− multiple Regression und ihre Diagnostik
− multivariate Analyseverfahren
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 240 Stunden
Type of Assessment
Prüfung: Klausur. Die Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur. Die Note ergibt sich aus der Klausurnote.
Recommended Background
Language German
Frequency Offered
Sommer semester
Recommended Semester
2
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Politics and Public Administration
Module Handbook Master’s Programme in Social and Economic Data Analysis
17
Statistics I (Dept. of Economics)
Applicability
Subject Area 1: Foundations of Data Analysis / Statistics
Credits 6 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
The lecture offers an introduction to statistical analysis.
Content of Teaching
Topics covered include univariate and multivariate descriptive methods, explorative methods, probability, discrete and continuous random variables and their distributions. Tutorials complement the lecture and include a discussion of output from the statistical software STATA and R.
Teaching Methods Hours per Week
Lecture (2 hours) and tutorial (2 hours)
Workload 180 hours
Type of Assessment
Final exam
Recommended Background
Language English
Frequency Offered
Summer semester
Recommended Semester
2
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Economics
Module Handbook Master’s Programme in Social and Economic Data Analysis
18
Statistics I (Dept. of Psychology)
Applicability
Subject Area 1: Foundations of Data Analysis / Statistics
Credits 6 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Die Studieren lernen grundlegende Verfahren und Begriffe der Statistik kennen. Sie sind in der Lage, Daten deskriptiv darzustellen und mittels statistischer Verfahren grundlegende Analysen durchzuführen. Die Studierenden können Strategien zur Aufgabenlösung entwickeln und anwenden.
Content of Teaching
Es wird ein Überblick über deskriptive statistische Methoden gegeben. Darauf aufbauend werden inferenzstatistische Verfahren vorgestellt und im Zuge der Übungsveranstaltung eingeübt.
Teaching Methods Hours per Week
Vorlesung (2 SWS) mit Übung (2 SWS)
Workload 180 Stunden
Type of Assessment
Schriftliche Klausur
Recommended Background
Language German
Frequency Offered
Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Psychology
Module Handbook Master’s Programme in Social and Economic Data Analysis
19
Statistik I (Dept. of History and Sociology)
Applicability
Subject Area 1: Foundations of Data Analysis / Statistics
Credits 6 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
In der Vorlesung wird eine Einführung in die sozialwissenschaftliche Statistik anhand von überwiegend soziologischen Anschauungsbeispielen und Übungsaufgaben gegeben.
Content of Teaching
− Grundlagen der Wahrscheinlichkeitstheorie
− Deskriptive Statistik und Inferenzstatistik
− Univariate, bivariate und multivariate Statistik
− Zusammenhangsmaße für Variablen mit unterschiedlichem Skalenniveau
− Regressionsanalysen: OLS und generalisierte lineare Modelle (Logit) Teaching Methods Hours per Week
Vorlesung (2 SWS) mit Übung (2 SWS)
Workload 180 Stunden
Type of Assessment
Klausur
Recommended Background
Language German
Frequency Offered
Sommer semester
Recommended Semester
2
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every single student by the admissions committee.
Department Department of History and Sociology
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2.4 Social-scientific Methods
Econometrics I
Applicability
Subject Area 1: Foundations of Data Analysis / Social-scientific Methods
Credits 8 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
On completion of this module, students will be able to:
− Specify the fundamentals of regression analysis
− Demonstrate an understanding of confronting hypothesis from economic models theory with real world economic data
− Use econometric software to pursue their own empirical research and interpret econometric findings critically.
Content of Teaching
− Multiple Linear Regression Model: LS-Estimation, Tests, Forecasting, Restricted LS-Estimation
− Problems of Model Specification: Autocorrelation, Heteroscedasticity, Functional Form
− Introduction to Dynamic Models
− Quantal Response Models
− Instrumental Variables Estimation
− Computer Tutorials with Gretl
Teaching Methods Hours per Week
Lecture (3 hours) and tutorial (2 hours)
Workload 240 hours
Type of Assessment
Final exam, poss. one mid-term exam or homework assignment
Recommended Background
Statistics I and II
Language English
Frequency Offered
Summer semester
Recommended Semester
2
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Economics
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Introduction to Survey Methodology
Applicability
Subject Area 1: Foundations of Data Analysis / Statistics
Credits 8 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
This course offers an introduction to survey methodology. Starting from an exposition of the total survey error paradigm as the now dominant conceptual foundation of the field, we will discuss sampling strategies, modes of data collection, nonresponse problems, question wording and measurement issues from a data quality perspective.
The aim of this course is to equip students with the knowledge and skills necessary to design, conduct, analyze and critically evaluate sample-based surveys. The course is mandatory for B.A. students of Politics & Public Administration with a specialization in Survey Research, but it will also be relevant for all those generally interested in the collection and analysis of quantitative social science data.
The course will be supplemented by a tutorial in which the principles and methods introduced during the lecture will be practiced using simulated and real survey data and the free statistical software package R.
Content of Teaching
- Total survey error paradigm
- Probability sampling, simple random and systematic sampling, stratification
- Cluster and multistage sampling, other probability designs
- Nonresponse types, sources and biases
- Empirical strategies for missing data
- The psychology of the survey response
- Question and questionnaire design
- Classical and modern test theory
- Current developments in survey sampling and measurement
Teaching Methods Hours per Week
Lecture (2 hours) and tutorial (2 hours)
Workload 240 hours
Type of Assessment
Final exam
Recommended Background
Language English
Frequency Offered
Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is scheduled for every student individually by the admissions committee.
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Department Department of Politics and Public Administration
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Methoden der empirischen Politk- und Verwaltungsforschung
Applicability
Subject Area 1: Foundations of Data Analysis / Social-scientific Methods
Credits 9 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Die Studierenden sollen mit den Zielen und dem Ablauf wissenschaftlicher For-schung in den Sozialwissenschaften vertraut gemacht werden. Die intensive Auseinandersetzung mit Grundfragen der Wissenschaftstheorie soll die Studie-renden zu einer fundierten Stellungnahme zu Wahrheitsansprüchen für Aussa-gen aller Art befähigen. Ferner sollen sie an die detaillierte methodologische Kritik empirischer Untersuchungen in den Sozialwissenschaften herangeführt werden.
Content of Teaching
Die Lehrveranstaltung gibt eine Einführung in die Planung, Durchführung und Analyse empirischer Forschungsprojekte. Sie behandelt unter anderem Ziel und Ablauf des Forschungsprozesses, Operationalisierung und Messung, unter-schiedliche Untersuchungsformen, Messverfahren, Auswahlverfahren sowie die wichtigsten Techniken der Datenerhebung. Sie umfasst ferner eine intensive Auseinandersetzung mit Grundfragen der Wissenschaftstheorie (Erkenntnis-theorie, Wahrheitstheorien, Theoriendynamik).
Teaching Methods Hours per Week
Vorlesung (4 SWS) mit Übung (2 SWS)
Workload 270 Stunden
Type of Assessment
Prüfung: Klausur. Die Teilnahme an den Übungen ist Voraussetzung für die Zulassung zur Klausur. Die Note ergibt sich aus der Klausurnote.
Recommended Background
Language German
Frequency Offered
Winter semester
Recommended Semester
1
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of Politics and Public Administration
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Empirie: Quantitative Methoden
Applicability
Subject Area 1: Foundations of Data Analysis / Social-scientific Methods
Credits 6 Cr Duration 1 Sem.
Module Contribution to the Final Grade 6.67%
Learning Outcomes
Das Modul vermittelt grundlegende Kenntnisse im Bereich der Statistik und der sozialwissenschaftlichen Forschungsmethoden. Ziel ist die Vermittlung von wissenschaftstheoretischen Grundlagen, wichtigen Forschungsdesigns, Erhebungs- und Auswertungstechniken der empirischen Sozialforschung (quantitativ und qualitativ). Die Studierenden werden befähigt, grundlegende Methoden auf neue Frage- und Aufgabenstellungen zu übertragen und sie erlernen die Planung empirischer Untersuchungen, die Konstruktion von Erhebungsinstrumenten, die Datenauswertung und die Präsentation der Ergebnisse.
Content of Teaching
In der Veranstaltung werden grundlegende Kenntnisse der quantitativen empirischen Sozialforschung vermittelt. Im Mittelpunkt stehen die einzelnen Phasen des Forschungsablaufs von den wissenschaftstheoretischen Grundlagen bis hin zur Datenerhebung und -auswertung. Dabei wird ein Überblick über mögliche Erhebungsmethoden und Forschungsdesigns, wie Befragungen, Beobachtungen oder Experimente gegeben und auf deren Anwendungsbereich eingegangen.
Teaching Methods Hours per Week
Vorlesung (2 SWS) mit Übung (2 SWS)
Workload 180 Stunden
Type of Assessment
Klausur
Recommended Background
Language German
Frequency Offered
Sommer semester
Recommended Semester
2
Compulsory / Optional
The selection of courses in Subject Area 1 is individually scheduled for every student by the admissions committee.
Department Department of History and Sociology
Module Handbook Master’s Programme in Social and Economic Data Analysis
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3 Subject Area 2: Advanced Methods of Data Analysis
Probability Theory and Statistical Inference
Applicability
Subject Area 2: Advanced Methods of Data Analysis
Credits 8 Cr Duration 1 Sem.
Module Contribution to the Final Grade (The exact contribution depends on the weighting according to credits.)
5%
Learning Outcomes
The course covers the major topics from probability theory and statistical inference that lay the grounds for econometric theory. It conveys a proper understanding of the basic theoretical concepts and ideas needed to work in applied and theoretical econometrics.
Content of Teaching
The course gives an introduction to the basic mathematical foundations of probability theory and of statistical inference on a graduate level. The foundations of probability theory part of the course covers the following topics:
− Basics of probability theory: a. events, probability, conditional probability, independence, product
spaces and completeness; b. discrete and continuous random variables, probability distributions; c. expectation, conditional expectation, conditional distributions; d. moment generating and characteristic functions, their applications.
− Basics of asymptotic theory: a. convergence concepts, modes of convergence; b. limit theorems.
The statistical inference part of the course covers the following topics:
1) Random sample, properties; 2) Principles of data reduction, the sufficiency and the likelihood principles; 3) Point estimation, finding and evaluating point estimators; 4) Interval estimation, finding and evaluating interval estimators; 5) Estimation theory for parametric models: regression models and least
squares method. Teaching Methods Hours per Week
Lecture (2 hours) and tutorial (2 hours)
Workload 240 hours
Type of Assessment
Weekly homework assignments, surprise quizzes, mid-term take-home exam, final exam
Recommended Background
Solid mastery of calculus, up to and including series, limits, partial differentiation, and multiple integration. Knowledge of these topics will be assumed and invoked freely. Basic knowledge of statistics is required.
Language English
Frequency Offered
Winter semester
Recommended Semester
1
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Compulsory / Optional
compuslory
Department Department of Economics
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Research Design I
Applicability
Subject Area 2: Advanced Methods of Data Analysis
Credits 9 Cr Duration 1 Sem.
Module Contribution to the Final Grade (The exact contribution depends on the weighting according to credits.)
5%
Learning Outcomes
This course offers an advanced treatment of design issues in political research that aims (as it usually does) at causal inference, i.e., at answering cause-and-effect questions of the general form: is X a cause of Y? If so, how large is the causal effect of X on Y? Starting from an exposition of the counterfactual model of causality, the course introduces the assumptions necessary for identifying causal effects, and shows how these assumptions are justified to varying degrees in different experimental and observational research designs. As to observational studies, the course gives an overview of common and new large-N methods for causal inference, such as regression and panel estimators, matching, instrumental variable and control function approaches. The course also discusses how the principles and methods introduced may be put to good use for small-N studies, in particular when it comes to intentional case selection, and how methods frequently dubbed qualitative (such as process tracing) may help identifying the mechanisms underlying causal effect estimates. The course's primary aim is to provide students with the epistemological and methodological tools to critically evaluate existing empirical studies, to identify their inferential weaknesses, and to develop research designs on their own that, to the greatest possible extent, respond to these problems.
Content of Teaching
- Potential outcomes framework, assumptions identifying causal effects, definition of treatment effects
- Randomization, internal and external validity - Noncompliance and attrition - Regression - Panel and diff-in-diff estimators - Regression discontinuity design - Instrumental variables
Teaching Methods Hours per Week
Lecture (2 hours) and tutorial (2 hours)
Workload 270 hours
Type of Assessment
Final exam
Recommended Background
Language English
Frequency Offered
Winter semester
Recommended Semester
1
Compulsory / Optional
In the Subject Area 2, students have to take 36 ECTS including two seminars.
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Department Department of Politics and Public Administration
Module Handbook Master’s Programme in Social and Economic Data Analysis
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Big Data and Scripting
Applicability
Subject Area 2: Advanced Methods of Data Analysis
Credits 8 Cr Duration 1 Sem.
Module Contribution to the Final Grade (The exact contribution depends on the weighting according to credits.)
5%
Learning Outcomes
n/a
Content of Teaching
The term “big data'' is often used to describe vast collections of semi-structured data in the range of tera- or even petabytes.
Companies like Google and Amazon illustrate that mining and analyzing such collections yields the potential for completely new applications.
The lecture provides an overview of motivations to analyze big data and introduces techniques needed in the process.
This includes introductions to scripting languages, NOSQL databases and Map/Reduce systems which are accompanied by practical exercises.
Teaching Methods Hours per Week
Lecture (6 hours) and tutorial (2 hours)
Workload 240 hours
Type of Assessment
Final exam
Recommended Background
Language English
Frequency Offered
Summer semester
Recommended Semester
2
Compulsory / Optional
In the Subject Area 2, students have to take 36 ECTS including two seminars.
Department Department of Computer and Information Sciences
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Optional Course
Applicability
Subject Area 2: Advanced Methods of Data Analysis
Credits 6-9 Cr Duration 1 Sem.
Module Contribution to the Final Grade (The exact contribution depends on the weighting according to credits.)
5%
Learning Outcomes
Students have the opportunity to select one course upon consultation. The skills depend on the selected module.
Content of Teaching
This course has to be offered by one of the participating departments and has to be in the field of Advanced Methods of Data Analysis.
Workload 180 to 270 hours
Type of Assessment
The type of assessment depends on the selected course
Language English
Frequency Offered
Winter or summer semester
Recommended Semester
1 or 2
Compulsory / Optional
In the Subject Area 2, students have to take 36 ECTS including two seminars.
Department All participating Departments
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Seminar Module
Applicability
Subject Area 2: Advanced Methods of Data Analysis
Credits 12 Cr Duration 1-2 Sem.
Module Contribution to the Final Grade 10%
Learning Outcomes
Students have the opportunity to select two seminars of the participating departments according to their special interests. The skills depend on the selected seminars.
Content of Teaching
The Seminar Module contains two seminars à 6 ECTS-Credits. These seminars can be selected from Master seminars of the participating departments.
Workload 360 hours
Type of Assessment
Based on an oral presentation and a seminar paper, poss. on participation
Language English
Frequency Offered
Winter or summer semester
Recommended Semester
1 or 2
Compulsory / Optional
In the Subject Area 2, students have to take 36 ECTS including two seminars.
Department All participating Departments
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4 Subject Area 3: Optional compulsory section
Optional Module
Applicability
Subject Area 3: Optional compulsory section
Credits 20 Cr (A)
25 Cr (B)
Duration 1 Sem.
Module Contribution to the Final Grade 18% (A)
22% (B)
Learning Outcomes
Students have the opportunity to select courses of the participating departments according to their special interests. The skills depend on the selected module.
Content of Teaching
The Optional Module contains courses totalling 20 ECTS-Credits (Track A), respectively 25 ECTS-Credits (Track B). These courses can be selected from Master courses of the participating departments.
Workload 600 hours (A); 750 hours (B)
Type of Assessment
The module grade depends on the selected course
Language English
Frequency Offered
Winter semester
Recommended Semester
3
Compulsory / Optional
compulsory
Department All participating Departments
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Data Analysis Project
Applicability
Subject Area 3: Optional compulsory section
Credits 5 Cr Duration 1 Sem.
Module Contribution to the Final Grade 0%
Learning Outcomes
− An opportunity to use the skills and knowledge gained while studying − Acquire new practical knowledge − Gain practical work experience − Enhance and deepen competences and skills required for studying and
working Content of Teaching
The data analysis project provides students with the opportunity to work through a project from its conceptualization scratch down to its (software) implementation. Co-operations with external partners (firms, governmental and non-governmental organizations) are encouraged. The data analysis project constitutes an innovative teaching format, with a strong focus on applying the statistical and computational methods previously acquired. It is meant to foster cooperation and knowledge transfer between the university and external partners, thereby facilitating the integration of students into the job market early on.
Workload 150 hours
Frequency Offered
Winter semester
Recommended Semester
3
Compulsory / Optional
compulsory
Module Handbook Master’s Programme in Social and Economic Data Analysis
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5 Subject Area 4a: PhD Module (Study Track A) Applicability
Subject Area 4a: PhD Module
Credits 20 Cr
Duration 1 Sem.
Module Contribution to the Final Grade 18%
Learning Outcomes
Students of Study Track A have the opportunity to select two courses of the Doctoral Programme in Decision Science and the Doctoral Programme in Quantitative Economics and Finance according to their special interests. The skills depend on the selected module.
Content of Teaching
The PhD Module contains two courses totalling 20 ECTS-Credits. These courses can be selected from courses of the Doctoral Programme in Decision Science.
Workload 600 hours
Type of Assessment
The module grade depends on the selected course
Language English
Frequency Offered
Summer semester
Recommended Semester
4
Compulsory / Optional
compulsory for Track A; inexistent for Track B
Department All participating Departments
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6 Subject Area 4b: Master’s Thesis (Study Track A)
Applicability
Subject Area 4b: Master’s Thesis
Credits 15 Cr
Duration 3 months
Module Contribution to the Final Grade 20%
Learning Outcomes
On completion of this module, students will be able to: − Develop a research proposal − Synthesise knowledge and skills previously acquired and applied to an
in-depth study − Establish links between theory and methods within their area of study − Present the findings of their research in a coherent and logically argued
piece of writing that demonstrates competence in research and the ability to operate independently.
Content The aim of the Master’s thesis is to demonstrate that the student is in a position to independently analyse and assess a topic from the field of social science data analysis, within a prescribed time period and using scientifically recognised methods. Students select their own topic for the thesis in consultation with their supervisor.
Supervisor Each student selects a supervisor (assessor) for the thesis, who can be a professor or junior professor of the participating departments. A further member of the Department’s faculty has to be selected as the second assessor. At least one assessor has to be Principal Investigator or associated Professor of the Graduate School of Decision Sciences.
Teaching Methods The theoretical and methodological background knowledge for conducting a thesis is acquired through the compulsory and optional subject areas of the Master’s programme. Practise in the completion of research papers is obtained in the seminars of the Master’s programme.
Workload 450 hours
Type of Assessment
The average grade of the two assessments of the Master’s thesis. Number of pages according to the regulations of the first supervisor’s department.
Recommended Background
The relevant courses and seminars of the Master’s programme
Language English
Frequency Offered
Summer semester
Recommended Semester
4
Compulsory / Optional
compulsory
Department All participating Departments
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7 Subject Area 4: Master’s Thesis (Study Track B)
Applicability
Subject Area 4: Master’s Thesis
Credits 30 Cr
Duration 4 months
Module Contribution to the Final Grade 30%
Learning Outcomes
On completion of this module, students will be able to:
− Develop a research proposal − Synthesise knowledge and skills previously acquired and applied to an
in-depth study − Establish links between theory and methods within their area of study − Present the findings of their research in a coherent and logically argued
piece of writing that demonstrates competence in research and the ability to operate independently.
Content The aim of the Master’s thesis is to demonstrate that the student is in a position to independently analyse and assess a topic from the field of social science data analysis, within a prescribed time period and using scientifically recognised methods. Students select their own topic for the thesis in consultation with their supervisor.
Supervisor Each student selects a supervisor (assessor) for the thesis, who can be a professor or junior professor of the participating departments. A further member of the Department’s faculty has to be selected as the second assessor. At least one assessor has to be Principal Investigator or associated Professor of the Graduate School of Decision Sciences.
Teaching Methods The theoretical and methodological background knowledge for conducting a thesis is acquired through the compulsory and optional subject areas of the Master’s programme. Practise in the completion of research papers is obtained in the seminars of the Master’s programme.
Workload 900 hours
Type of Assessment
The average grade of the two assessments of the Master’s thesis. Number of pages according to the regulations of the first supervisor’s department.
Recommended Background
The relevant courses and seminars of the Master’s programme
Language English
Frequency Offered
Summer semester
Recommended Semester
4
Compulsory / Optional
compulsory
Department All participating Departments