Beschreibung des Studienfachs
The subject matter of the consecutive master's program Statistics and Data Science is advanced methods for collecting data correctly, extracting reliable information from data, and drawing scientifically sound conclusions. One focus is on the ability to independently develop powerful methods that are precisely tailored to complex content-related questions. On the one hand, in-depth methods of statistical inference, machine learning and statistical modeling are taught, and on the other hand, interdisciplinary communication skills are taught, especially in order to be able to formalize content-related questions appropriately and to be able to prepare the analysis results accordingly for practical consulting in science, administration and business. Students specialize in one of the areas "Machine Learning", "Biostatistics", "Statistics and Data Science in the Social Sciences", "Econometrics" and "Modeling Methodology", where they are introduced to current methodological research.
Tätigkeits- und Berufsfelder
Since data is one of the most important raw materials of the 21st century, people with the competence to actually mine the wealth of knowledge available in complex data sets are more in demand than ever. Thus, graduates of the program will have excellent job market opportunities. Graduates of the master's programs offered so far by the Institute of Statistics work in all empirical areas of the economy, from industrial pharmaceutical research to professional market and opinion research to banks and insurance companies, in administration and public authorities, including of course official statistics, as well as in intra- and extra-university research, including corresponding large institutions such as the Helmholtz Center, the Institute for Employment Research, the Federal Employment Agency or various Leibniz Institutes.
Fakten auf einen Blick
- Studiengang
- Statistics and Data Science (Master)
- Abschlussgrad
- Master of Science (M.Sc.)
- Fachtyp
- Hauptfach
- Regelstudienzeit
- 4 Fachsemester
Bewerbung und Zulassung
- Formale Studienvoraussetzung
- Hochschulzugangsberechtigung
- Zulassungsmodus 1. Semester
- Eignungsverfahren
- Zulassungsmodus höheres Semester
- Eignungsverfahren
- Zugangsdetails
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The admission requirement for this Master's program is proof of a professional university degree with at least 180 ECTS credits or an equivalent degree from Germany or abroad in the field of statistics or data science or a degree program with statistics or data science as a major or minor subject as well as successful participation in an aptitude test. The application for the aptitude test must be submitted to the Department of Statistics by May 15 for the following winter semester and by November 15 for the following summer semester (cut-off deadline).
Further Information for the Application for the Master Statistics and Data Science
- Link zum Fach
- Master Statistics and Data Science
Ihr Weg zum Studienplatz
Der Studiengang im Detail
The study program comprises five compulsory and 56 compulsory elective modules. The following five mandatory modules, to which the respective ECTS points and SWS are assigned, must be completed:
- P 1 Statistical Modelling; 12 ECTS-Punkte; 8 SWS
- P 2 Supervised Learning; 6 ECTS-Punkte; 4 SWS
- P 3 Statistical Inference; 9 ECTS-Punkte; 6 SWS
- P 4 Consulting; 12 ECTS-Punkte; 3 SWS
- P 5 Final Module; 30 ECTS-Punkte
Module P 5 contains the Master's thesis, to which 28 ECTS credits are assigned, and the disputation, to which 2 ECTS credits are assigned. The subject of the disputation is the master's thesis.
The following 56 elective modules, to which the ECTS points and SWS indicated in each case are assigned, are offered:
- WP 1 Optimization; 6 ECTS-Punkte; 4 SWS
- WP 2 Preclinical and Clinical Studies; 6 ECTS-Punkte; 4 SWS
- WP 3 Complex Samples and Data Structures; 6 ECTS-Punkte; 4 SWS
- WP 4 Basic Concepts and Structures in Official Statistics, Dissemination and Privacy Protection; 6 ECTS-Punkte; 4 SWS
- WP 5 Causal Inference; 6 ECTS-Punkte; 4 SWS
- WP 6 Survival Analysis; 6 ECTS-Punkte; 4 SWS
- WP 7 Deep Learning; 6 ECTS-Punkte; 4 SWS
- WP 8 Advanced Machine Learning; 6 ECTS-Punkte; 4 SWS
- WP 9 Applied Machine Learning; 6 ECTS-Punkte; 4 SWS
- WP 10 Diagnostic Accuracy Studies; 6 ECTS-Punkte; 4 SWS
- WP 11 Selected Topics of Biostatistics; 3 ECTS-Punkte; 2 SWS
- WP 12 Analysis of High-dimensional Biological Data; 6 ECTS-Punkte; 4 SWS
- WP 13 Introduction to Medical Terminology; 3 ECTS-Punkte; 2 SWS
- WP 14 Data Collection and Questionnaire Design; 6 ECTS-Punkte; 4 SWS
- WP 15 Official Statistics on Households, Enterprises, Economies and Populations; 6 ECTS-Punkte; 4 SWS
- WP 16 Advanced Methods in Social Statistics and Social Data Science; 6 ECTS-Punkte; 4 SWS
- WP 17 Econometric Theory; 6 ECTS-Punkte; 4 SWS WP 18 Time Series; 6 ECTS-Punkte; 4 SWS
- WP 18 Time Series; 6 ECTS-Punkte; 4 SWS
- WP 19 Machine Learning in Econometrics; 6 ECTS-Punkte; 4 SWS
- WP 20 Selected Topics of Econometrics; 3 ECTS-Punkte; 2 SWS
- WP 21 Regression for Correlated Data; 6 ECTS-Punkte; 4 SWS
- WP 22 Decision Theory; 6 ECTS-Punkte; 4 SWS
- WP 23 Methodological Discourses in Statistics and Data Science; 6 ECTS-Punkte;4 SWS
- WP 24 Design of Experiments; 6 ECTS-Punkte; 4 SWS
- WP 25 Advanced Mathematical Concepts for Statistics and Data Science; 6 ECTSPunkte;4 SWS
- WP 26 Stochastic Processes; 6 ECTS-Punkte; 4 SWSWP 27 Teaching Statistics and Data Science; 6 ECTS-Punkte; 2 SWS
- WP 28 Statistical Literacy; 3 ECTS-Punkte; 2 SWS
- WP 29 Selected Topics of Applied Statistics; 3 ECTS-Punkte; 2 SWS
- WP 30 Selected Software for Applied Statistics; 3 ECTS-Punkte; 2 SWS
- WP 31 Advanced Research Methods in Applied Statistics; 9 ECTS-Punkte; 2 SWS
- WP 32 Current Research in Machine Learning; 6 ECTS-Punkte; 4 SWS
- WP 33 Automated Machine Learning; 6 ECTS-Punkte; 4 SWS
- WP 34 Selected Topics of Machine Learning; 3 ECTS-Punkte; 2 SWS
- WP 35 Statistical Methods in Epidemiology; 6 ECTS-Punkte; 4 SWS
- WP 36 Advanced Methods in Biostatistics; 6 ECTS-Punkte; 4 SWS
- WP 37 Selected Biostatistical Applications; 3 ECTS-Punkte; 2 SWS
- WP 38 Measurement and Modelling in Social Sciences; 6 ECTS-Punkte; 4 SWS
- WP 39 Computational Social Science; 6 ECTS-Punkte; 4 SWS
- WP 40 Selected Topics of Social Statistics and Social Data Science; 3 ECTS-Punkte;2 SWS
- WP 41 Nonparametric Econometrics; 6 ECTS-Punkte; 4 SWS
- WP 42 Current Research in Econometrics; 6 ECTS-Punkte; 4 SWS
- WP 43 Advanced Applied Econometrics; 6 ECTS-Punkte; 4 SWS
- WP 44 Advanced Statistical Modelling; 6 ECTS-Punkte; 4 SWS
- WP 45 Spatial Statistics; 6 ECTS-Punkte; 4 SWS
- WP 46 Selected Topics of Methodology and Modelling; 3 ECTS-Punkte; 2 SWS
- WP 47 Advanced Programming; 6 ECTS-Punkte; 3 SWS
- WP 48 Recent Advances in Theoretical Statistics; 6 ECTS-Punkte; 4 SWS
- WP 49 Selected Topics of Statistical Computing; 3 ECTS-Punkte; 2 SWS
- WP 50 Selected Topics of Programming; 3 ECTS-Punkte; 2 SWS
- WP 51 Advanced Research Methods in Theoretical Statistics; 9 ECTS-Punkte; 2 SWS
- WP 52 Advanced Research Methods in Machine Learning; 9 ECTS-Punkte; 2 SWS
- WP 53 Advanced Research Methods in Biostatistics; 9 ECTS-Punkte; 2 SWS
- WP 54 Advanced Research Methods in Social Statistics and Social Data Science;9 ECTS-Punkte; 2 SWS
- WP 55 Advanced Research Methods in Econometrics; 9 ECTS-Punkte; 2 SWS
- WP 56 Advanced Research Methods in Methodology and Modelling; 9 ECTS-Punkte; 2 SWS
Exactly one elective must be chosen from the elective areas "Machine Learning", "Biostatistics", "Social Statistics and Data Science", "Econometrics" and "Methodology and Modelling".
For this purpose, compulsory elective modules with a total of 51 ECTS credits each are to be selected from the compulsory elective modules WP 1 to WP 56, namely
1. for the elective course "Machine Learning"
- the compulsory elective modules WP 1, WP 7 und WP 52,
- from the compulsory elective modules WP 8, WP 9 and WP 32 to WP 34 compulsory elective modules amounting to at least 12 ECTS credits and
- from the compulsory elective modules WP 1 to WP 51 to achieve the 51 ECTS points per compulsory elective area, further compulsory elective modules amounting to a maximum of 18 ECTS points
2. for the elective course "Biostatistics"
- the compulsory elecitive modules WP 2 und WP 53,
- from the compulsory elective modules WP 10 to WP 13 and WP 35 to WP 37 elective modules amounting to 12 ECTS credits,
- from the compulsory elective modules WP 6, WP 10 to WP 13, WP 21 and WP 35 to WP 37 further compulsory elective modules amounting to at least 6 ECTS credits and
- from the compulsory elective modules WP 1 to WP 51 to achieve the 51 ECTS points per compulsory elective area, further compulsory elective modules amounting to a maximum of 18 ECTS points.
3. for the elective course "Social Statistics and Social Data Science"
- the compulsory elective module WP 54,
- two compulsory elective modules from the elective modules WP 3, WP 14 and WP 38,
- from the compulsory elective modules WP 3 to WP 6, WP 14 to WP 16, WP 21, WP 22 and WP 38 to WP 40 further compulsory elective modules amounting to at least 12 ECTS credits and
- from the compulsory elective modules WP 1 to WP 51 to achieve the 51 ECTS points per compulsory elective area, further compulsory elective modules amounting to a maximum of 18 ECTS points.
4. for the elecitive course "Econometrics“
- the compulsory elective modules WP 5, WP 17, (WP 19 or WP 41) and WP 55,
- from the compulsory elective modules WP 4, WP 6, WP 15, WP 18 to WP 22, WP 38 and WP 41 to WP 43 further compulsory elective modules amounting to at least 6 ECTS credits and
- from the compulsory elective modules WP 1 to WP 51 to achieve the 51 ECTS points per compulsory elective area, further compulsory elective modules amounting to a maximum of 18 ECTS points.
5. for the elective course "Methodology and Modelling"
- the compulsory elective module WP 56,
- two compulsory elective modules from the compulsory elective modules WP 6, WP 21 and WP 22,
- from the compulsory elective modules WP 6, WP 21 to WP 26, WP 38 and WP 44 to WP 46 further compulsory elective modules amounting to at least 12 ECTS points and
- from the compulsory elective modules WP 1 to WP 51 to achieve the 51 ECTS points per compulsory elective area, further compulsory elective modules amounting to a maximum of 18 ECTS points.
In the 1st semester, compulsory elective modules worth 12 ECTS credits are to be selected, in the 2nd semester compulsory elective modules worth 18 ECTS credits and in the 3rd semester compulsory elective modules worth 21 ECTS credits.
Module Catalogue for the Master's Programme Statistics and Data Science, based on the ,Prüfungs- und Studienordnung' of 08 December 2021; issued on 09.05.2022 - for the start of studies in the winter semester
Module Catalogue for the Master's Programme Statistics and Data Science, based on the ,Prüfungs- und Studienordnung' of 08 December 2021; issued on 09.05.2022 - for the start of studies in the summer semester
Englisch
Institut für Statistik
Sprechstunden, Aushänge, Änderungen des Lehrangebots
Fachstudienberatung Statistik
Inhaltliche und spezifische Fragen des Studiums, Studienaufbau, Stundenplan, fachliche Schwerpunkte
Prüfungsamt Naturwissenschaften Innenstadt
Prüfungsangelegenheiten, Prüfungsanmeldung, Semesteranrechnungsbescheide