Preliminary Courses for the Master Programmes
Before the start of the lectures, you should check out our precourses which will help you to prepare for your upcoming master and to refresh your knowledge of all the important content from your bachelor courses. There will be 3 different pre-courses:
The first one is a statistics pre-course in German language which covers especially the most important content of the statistics bachelor. It Is a three week course and takes place live on campus or via zoom (due to the corona virus). It is recommended if you prepare for the statistics master.
The second and third pre-courses are e-learning courses on the learning platform moodle in English language. They have no fixed timetable. Therefore, you can do them where and whenever you want.
One of them is an online statistics course. It mostly covers the same content as the first precourse and is recommended for both master programmes Data Science and Econometrics. The other one is a data science course and is highly recommended for all data science students.
For more information click here
Courses taught in English (Winter Semester 2022/23)
The following elective courses will be held in English in winter semester 2022.
Many other German language courses are also open for M.Sc. Data Science students. However, generally, for these courses you need to be fluent in German). If you are interested in such courses, please contact Daniel Horn dhornstatistik.tu-dortmundde.
Note that at the current point in time (2021/08/30) it is impossible to say whether classes will be allowed on campus or have to be online, and in the end, this will be different for each class. So please check the individual websites for more information.
Pre-requisites and Reading Courses
Information on the pre-requisites and Reading courses for both Data Science and Econometrics can be found in our Moodle room here.
Statistics Department
For all Statistics courses, you should bring sufficient skills in the fields of Statistics, i.e. you should take and finish your Statistics pre-requisites at first.
- Statistical Theory (9 credits (10 credits for Econometrics), 4+2, MD 2, part of ME 1) Tue 10:00-12:00 Wed 10:00-12:00
- Asymptotic Theory (only second half of the semester) (4,5 Credits (5 Credits for Econometrics), 2+1, MD Methods, part of ME 1) Wed 12:00-14:00 Thu 14:00-16:00
- Statistics in Toxicology (Testing) (4.5 credits, 2+1, MD Applications) Mon, 10:00-12:00
- Survival Analysis (9 credits, 4+2, MD Methods; ME 7) Wed 10:00-12:00 Thu 16:00-18:00
- Industrial Data Science (4.5 credits, 2+1, MD Applications) Fri 08:00-10:00
- Deep Learning (9 credits, 4+2, MD Methods) Tue 16:00-18:00 Wed 16:00-18:00
- Unit root and cointegration analysis (9 Credits, 4+2, MD Methods) Blockcourse in February
- Panel Data Econometrics (9 Credits, 4 +2, MD Methods) Tue 14:00-18:00
- Text as Data (4,5 Credits, 2 +1, MD Methods) Tue 10:00-12:00 Fri 08:00-10:00
- Statistical Network Analysis (4.5 Credits, 2 +, MD Methods) Tue 14:00-16:00
Computer Science Department
For all Computer Science courses, you should bring sufficient skills in the fields of Computer Science, i.e. you should take and finish your Computer Science pre-requisites at first.
- Statistical Learning for Big Data (9 Credits, 4+2, MD 5) Tue, 16:00-18:00 Thu 14:00-16:00
- Causality (6 credits, 2+2, MD Methods Mon 14:00-16:00
Mathematics Departments
For all Mathematics courses, you should bring sufficient basic knowledge in the field of Mathematics.
- Numerical Methods for PDEs (4 Credits, 2+1, MD Methods Fr 14:00-16:00
- Programming Course Data Science with Python (3 Credits, Programming Course) Wed 16:00-20:00
Other Departments
We again have some courses from the linguistics departments. Registration is already closed. Please note that the application to linguistic content is in the foreground of the course.
2 courses from Bochum
- Introduction to Linguistic Data Science (10 credits, 2 semesters, MD Applications)
- Introduction to Computational Linguistics (10 credits, 2 semesters, MD Applications)
2 courses from Dortmund
- Statistics meets Linguistics (4.5 credits, MD Applications)
- Research Methods in English Linguistics (4.5 credits, MD Applications)
Courses taught in English (Summer Semester 2022)
The following courses will be held in English in summer semester 2022.
Many other German language courses are also open for M.Sc. Data Science students. However, generally, for these courses you need to be fluent in German). If you are interested in such courses, please contact Daniel Horn dhornstatistik.tu-dortmundde.
The general teaching scheme for the upcoming semester is in-person teaching, so for most classes you will have to come to campus. However, some classes my stay hybrid by the choice of the respective lecturer. So please check the individual websites for more information.
Note that most information in the upcoming list is just mirrored from the LSF, in case of doubt, the LSF has the correct information.
We added an information on the teaching form for every course. Note that this information is just taken from the respective course webpages. It is just for your information, in case of doubt, the information on the course webpage is the correct one.
On Wednesday, March 30, 10:00 there will be a digital presentation of all the english language lectures
Pre-requisites and Reading Courses
Information on the pre-requisites and Reading courses for both Data Science and Econometrics can be found in our Moodle room here.
Statistics Department
For all Statistics courses, you should bring sufficient skills in the fields of Statistics, i.e. you should take and finish your Statistics pre-requisites at first.
- Advanced Statistical Learning
Prof. Groll, 4 + 2, MD 1 (9 Credits) Info Slides
Thu 16:00-17:00, EF50/HS3
Teaching form: Inverted classroom: 2 videos per week, 1 hour in-person Q&A - Generalized Linear Models
Prof. Groll, 4 + 2, MD Methods (9 Credits) Info Slides
Thu 11:00-12:00 GBIII/HS103
Teaching form: Inverted classroom: 2 videos per week, 1 hour in-person Q&A - Statistics in Toxicology I (Modelling)
Dr. Kappenberg, 2 + 1, MD Applications (4.5 Credits) Info Slides
Wed 14:00-18:00, M/E21
Teaching form: In-person. In addition to the script and the live lecture, videos (short version of the live lecture) are available - Econometrics
Prof. Jentsch, 4 + 2, MD Applications (9 Credits) Info Slides
Tue 08:00-10:00, M/E28
Thu 10:00-12:00 M/E28
Teaching form: In-person. - Time Series Analysis
JProf. Arsova, 4 + 2, MD Methods(9 Credits) Info Slides
Tue 10:00-12:00, HGII/HS4
Thu 12:00-14:00 GBIII/HS108
Teaching form: In-person. - Advanced R
Dr. Horn, M.Sc. Görz, 2 + 1, MD 3 Programming Course (3 Credits) Info Slides
Mo 12:00-14:00, CT/HS 15
Teaching form: In-person, lecture will be recorded and uploaded. - Introduction to Causal Inference
Menggang Yu, MS 6/7, ME 7, MD Methods E1-9 (4.5 Credits)
June, 17th to July, 29th
Mo 10:15-11:45, CDI 120
Tue 14:15-14:45, CDI 120
Computer Science Department
For all Computer Science courses, you should bring sufficient skills in the fields of Computer Science, i.e. you should take and finish your Computer Science pre-requisites at first.
- Unsupervised Machine Learning Methods
Prof. Schubert, 2 + 2, MD Methods (6 Credits)
Mon, 14:15-16:00 SRG/1.001
Teaching form: In-person.
In order to participate in this course, you already must have passed the courses Advanced Statistical Learning and Big Data Analytics - Natürlichsprachliche Systeme
Prof. Schubert, 2 + 2, MD Methods (6 Credits) Module Manual Computer Science
Thu, 12:15-14:00 SRG/1.001
Teaching form: In-person. - Computer Vision
Prof. Fink, 2 + 2, MD Methods (6 Credits) Module Manual Computer Science
Tue, 14:15-16:00 OH14/E-23
Teaching form: In-person. - Architecture and Implementation of Database Systems
Prof. Teubner, 4 + 2, MD Methods (8 Credits) Module Manual Computer Science
Mo, 08:15-10:00 OH14/E-23
Wed, 08:15-10:00 OH14/E-23
Teaching form: In-person. - Real-Time Systems and Applications
Prof. Chen, 4 + 2, MD Methods (8 Credits) Module Manual Computer Science
Thu, 12:15-14:00 OH12/E.003
Fr, 10:15-12:00 OH12/E.003
Teaching form: Lecture in-person and digital, practical session in-person. - Machine Learning Paradigms for Complex Data
Prof. E. Müller, 4 + 2, MD Methods (8 Credits) Module Manual Computer Science
Tue, 12:15-14:00 HGII/HS5
The course has been canceld. As an alternative, we recommened to take the Unsupervised Machine Learning Methods by Prof. Schubert
Mathematics Departments
For all Mathematics courses, you should bring sufficient basic knowledge in the field of Mathematics.
- Introduction to Computational Fluid Dynamics (Introduction to CFD)
Prof. Kuzmin, 2 + 1, MD Methods (5 Credits)
Tue, 10:00-12:00 M/1011 - Numerical Solution of Differential Equations
Prof. Turekt, 2 + 1, MD Methods (5 Credits)
Mo, 10:00-12:00 M/E29
Fr, 12:00-14:00 EF50/HS3 - Programming Course: Data Science with Python
Dr. Ibrahim, Programming Course (3 Credits)
Mo, 10:00-12:00 CIP-Pool Mathematics
We, 12:00-14:00 CIP-Pool Mathematics - link www.lsf.tu-dortmund.de/qisserver/rds Structures, Algorithms, and Applications in Python
Dr. Ibrahim, MD Applications (4Credits)
We, 12:00-14:00 , M/E21
Other Departments
- Learning in Robotics
Prof. Hoffmann, 2 + 1, MD Applications (4,5 Credits)
Tue, 12:15-13: HGII/HS4
Only a limited amount of seats available. Please register via our deans office by writing an email to ds_learn_rob@statistik.tu-dortmund.de using the header "[Application for a seat in Learning in Robotics Summer 2022]", the application deadline is March 31. - Introduction to Artificial Intelligence
Prof. Wiskott, 2 + 1, MD Methods (3 Credits)
Only a limited amount of 15 seats available. Please register via our deans office by writing an email to ds_learn_rob@statistik.tu-dortmund.de using the header "[Application for a seat in Introduction to Artificial Intelligence Summer 2022]", the application deadline is April 4, 23:00.
Moreover, some of the seminars and case studies will be held in English.
- Case studies (Demetrescu/Reichold/Gerharz)
- Data Mining Cup (Lang/Rieger/Maletz)
- Praktikum SBAZ (Herbrandt)
- Seminar on Resampling Methods (Jentsch)
- Seminar Foundations of Data Science (Munteanu)
- Six Sigma Green Belt Kurs (Pauly/Sattler)
- Seminar Visualization of Sports Data (Doebler/Groll/Ickstadt/Elmer)
- Seminar Simulationsstudien (Herbrandt)
Courses taught in English (Winter Semester 2021/22)
The following elective courses will be held in English in winter semester 2022.
Many other German language courses are also open for M.Sc. Data Science students. However, generally, for these courses you need to be fluent in German). If you are interested in such courses, please contact Daniel Horn dhornstatistik.tu-dortmundde.
Note that at the current point in time (2021/08/30) it is impossible to say, whether classes will be allowed on campus or have to be online, and in the end, this will be different for each class. So please check the individual websites for more information.
Pre-requisites and Reading Courses
Information on the pre-requisites and Reading courses for both Data Science and Econometrics can be found in our Moodle room here.
Statistics Department
For all Statistics courses, you should bring sufficient skills in the fields of Statistics, i.e. you should take and finish your Statistics pre-requisites at first.
- Statistical Theory
(9 Credits (10 Credits for Econometrics), 4+2, MD 2, part of ME 1)
Tue 10:00-12:00
Wed 10:00-12:00 - Asymptotic Theory
(4,5 Credits (5 Credits for Econometrics), 2+1, MD Methods, part of ME 1)
Wed 12:00-14:00
Thu 14:00-16:00 - Statistics in Toxicology (Testing)
(4,5 Credits, 2+1, MD Applications)
Fri 09:00-12:00 M/E21 or online
info slides - Statistics in Genetics I (Bioinformatics)
(9 Credits, 4+2, MD Applications)
Tue 08:00-10:00 M/E21 or online
Thu 12:00-14:00 M/E21 or online
info slides - Survival Analysis
(9 Credits, 4+2, MD Methods; ME 7)
Tue 16:00-18:00 online
Thu 16:00-18:00 online
info slides - Bootstrap Methods
(9 Credits, 4+2, MD Methods, ME 7)
Tue 14:00-16:00
Thu 10:00-12:00
info slides - Industrial Data Science
(4,5 Credits, 2+1, MD Applications)
Thu 10:00-12:00
info slides
Computer Science Department
For all Computer Science courses, you should bring sufficient skills in the fields of Computer Science, i.e. you should take and finish your Computer Science pre-requisites at first.
- Big Data Analytics
(9 Credits, 4+2, MD 5)
Tue, 14:00-15:30 via ZOOM
Thu 14:00-15:30 via ZOOM - Introduction to Computational Intelligence
(4 Credits, 2+1, MD Methods
Wed 10:00-12:00
Mathematics Departments
For all Mathematics courses, you should bring sufficient basic knowledge in the field of Mathematics.
- Numerical Methods for PDEs
(5 Credits, 2+1, MD Methods)
Other Departments
From the Linguistic Data Science Department in Bochum, there are 2 courses. Both of them are limited to 10 Data Science students per course. Registration for this courses took place per mail until Wednesday, October 06, 23:59, please do not register directly at the teachers, all such requests will be ignored.
- Introduction to Linguistic Models
(5 Credits, MD Applications))
Fr 12:00-14:00 - Introduction to Computational Linguistics in Python
(5 Credits, MD Applications)
Wed 12:00-14:00
From the Linguistic Department in Dortmund, there is 2 courses. The Statistics meets linguistics course is limited to 15 participants from the statistics department, the research methods course to 8 participants. Registration for this courses took place per mail until Wednesday, October 06, 23:59, please do not register directly at the teachers, all such requests will be ignored.
Please note that the application to linguistic content is in the foreground of the course.
- Statistics meets Linguistics
(4,5 Credits, MD Applications)
Tue 10:00-12:00
detailed timetable - Research Methods in English Linguistics
(? Credits, MD Applications)
Fr 8:30-10:00
Seminars at the Department of Statistics
- Time Series Econometrics (Prof. Dr. Carsten Jentsch)
- Basics of Simulation and Statistics of Dynamic Systems (Prof. Dr. Christine Müller)
- Bayesian Data Analysis with Stan (Prof. Dr. Philipp Doebler)
- Foundations of Data Science (Dr. Alexander Munteanu)
- Empirical Processes (Dr. Marc Ditzhaus)
- Case Studies: Safety in Machine Learning (Bin Li (Computer Science))
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To event listLocation & approach
The campus of the Technical University of Dortmund is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits.
The campus of the Technical University of Dortmund is located near the freeway junction Dortmund West, where the Sauerland line A45 crosses the Ruhr expressway B1/A40. The Dortmund-Eichlinghofen exit on the A45 leads to the South Campus, the Dortmund-Dorstfeld exit on the A40 leads to the North Campus. The university is signposted at both exits.
One of the landmarks of the TU Dortmund is the H-Bahn. Line 1 runs every 10 minutes between Dortmund Eichlinghofen and the Technology Center via Campus South and Dortmund University S, while Line 2 commutes every 5 minutes between Campus North and Campus South. It covers this distance in two minutes.
From Dortmund Airport, it takes just over 20 minutes to get to Dortmund Central Station by AirportExpress and from there to the university by S-Bahn. A wider range of international flight connections is offered by Düsseldorf Airport, about 60 kilometers away, which can be reached directly by S-Bahn from the university's train station.