Data-Intensive Computing
Big Data" refers to the rapidly growing amounts of data generated in science, technology and our daily lives. Technologies such as "cloud computing" and "multi-core processors" make it possible to process these large amounts of data. In order to gain meaningful knowledge from this flood of data, competencies in Data Science, scientific computing, parallel processing and algorithms are necessary. This study profile combines all these aspects and is equally interdisciplinary, theoretically sound and oriented towards current applications.
Graduates of the study profile "Data-Intensive Computing" are expected to acquire interdisciplinary competences in mathematics as well as selected natural and engineering sciences in addition to the fundamentals of computer science. Students are thus able to link interdisciplinary algorithms, methods and tools with real-world applications. As Data Analysts, Data Managers, Computational Engineers but also Computational/Data Scientists, students are thus optimally qualified for science and industry in their studies.
German name: Daten-intensives Rechnen
Designated Speaker / Deputy Speaker: Prof. Peter Sanders / Prof. Achim Streit
Special competencies acquired in the profile:
Graduates know basics in data analysis, simulations, data management, algorithms and security, and are able to further develop and optimize them.
They have developed a basic understanding in mathematics and selected natural and engineering sciences and can extract and specify the requirements from these disciplines for data-intensive computing.
You will be able to apply the portfolio of algorithms, methods and tools to develop interdisciplinary advanced and powerful solutions for natural sciences and engineering as well as an industrial application.
- The master thesis must be from the subject area of the study profile.
- At least 10 CP from each of the two compulsory elective subjects "Daten" and "Algorithmen und Parallelverarbeitung" must be taken.
- In addition, two of the advanced mandatory modules Computer Structures, Security, Algorithms II, Cognitive Systems (at least 12 CP) must be taken. If the advanced mandatory modules have already been examined in the Bachelor's degree, more LP must be taken from the other areas (compulsory elective subjects, elective block, complementary subjects).
- One of the following complementary subjects "Materialwissenschaften für datenintensives Rechnen“, „Mathematik für datenintensives Rechnen“ or „Betriebswirtschaftslehre für datenintensives Rechnen“ must be taken (9-18 CP).
- Additional thematically appropriate seminars, internships, or research practice may be taken in consultation with the profile coordinator.
- A total of at least 54 CP from 2.-5. and the elective block must be completed.
V=Vorlesung (Lecture), S=Seminar (Seminar), P=Praktikum (Practical course), Ü=Übung (Practice)
| Compulsory elective subject "Daten" (at least 10 CP) | Course | Module | Partial achievement | CP | Course type |
| Analysetechniken für große Datenbestände (selectable up to SS 21) | M-INFO-100768 | T-INFO-101305 | 5 | V | |
| Analysetechniken für große Datenbestände 2 (selectable up to SS 21) | M-INFO-102773 | T-INFO-105742 | 3 | V | |
| Data and Storage Management (selectable up to WS 23/24) | M-INFO-100739 | T-INFO-101276 | 4 | V | |
| Datenbank-Praktikum | M-INFO-101662 | T-INFO-103201 | 4 | V | |
| Datenbankeinsatz | M-INFO-100780 | T-INFO-101317 | 5 | V | |
| Datenhaltung in der Cloud (selectable up to SS 21) | M-INFO-100769 | T-INFO-101306 | 5 | V | |
| Praktikum: Analyse großer Datenbestände (selectable up to SS 21) | M-INFO-101663 | T-INFO-103202 | 4 | P | |
| Praktikum: Analysis of Complex Data Sets (selectable up to WS 22/23) | M-INFO-102807 | T-INFO-105796 | 4 | P | |
| Praktikum: Datenmanagement und Datenanalyse (selectable up to SS 21) | M-INFO-103050 | T-INFO-106066 | 4 | P | |
| Seminar: Big Data Tools (selectable up to SS 21) | M-INFO-101886 | T-INFO-103583 | 3 | S | |
| Seminar: Informationssysteme | M-INFO-101794 | T-INFO-103456 | 3 | S | |
| Verteiltes Rechnen | M-INFO-100761 | T-INFO-101298 | 4 | V | |
| Compulsory elective subject "Algorithmen und Parallelverarbeitung" (at least 10 CP) | Course | Module | Partial achievement | CP | Course type |
| Algorithm Engineering | M-INFO-100795 | T-INFO-101332 | 5 | V | |
| Algorithmen für Routenplanung | M-INFO-100031 | T-INFO-100002 | 5 | V | |
| Algorithmen II (Advanced mandatory module) | M-INFO-101173 | T-INFO-102020 | 6 | V | |
| Fortgeschrittene Datenstrukturen | M-INFO-102731 | T-INFO-105687 | 5 | V | |
| Graphpartitionierung und Graphenclustern in Theorie und Praxis | M-INFO-100758 | T-INFO-101295 | 5 | V | |
| Heterogene parallele Rechensysteme | M-INFO-100822 | T-INFO-101359 | 3 | V | |
| Multikern-Rechner und Rechnerbündel | M-INFO-100788 | T-INFO-101325 | 4 | V | |
| Parallele Algorithmen | M-INFO-100796 | T-INFO-101333 | 5 | V | |
| Parallelrechner und Parallelprogrammierung | M-INFO-100808 | T-INFO-101345 | 4 | V | |
|
Practical Course: Visual Computing (old titles:Praktikum: GPU-Computing; Praktikum: Visual Computing) |
M-INFO-101567 | T-INFO-103000 | 6 | P | |
| Praktikum: Algorithmentechnik | M-INFO-102072 | T-INFO-104374 | 6 | P | |
| Praktikum: General-Purpose Computation on Graphics Processing Units | M-INFO-100724 | T-INFO-101261 | 3 | P | |
| Praxis der Multikern-Programmierung: Werkzeuge, Modelle, Sprachen (not applicable as of SS 2020) | M-INFO-100985 | T-INFO-101565 | 6 | V | |
|
Randomisierte Algorithmen |
M-INFO-100794 | T-INFO-101331 | 5 | V | |
| Rechnerstrukturen (Advanced mandatory module) | M-INFO-100818 | T-INFO-101355 | 6 | V | |
| Seminar: Advanced Topics in Parallel Programming (selectable up to SS 21) | M-INFO-101887 | T-INFO-103584 | 3 | S | |
| Softwarepraktikum Parallele Numerik | M-INFO-102998 | T-INFO-105988 | 4 | P | |
| Text-Indexierung | M-INFO-102732 | T-INFO-105691 | 5 | V | |
| Elective block | Course | Module | Partial achievement | CP | Course type |
|
Advanced Artificial Intelligence (new title from SS 2025) Advanced mandatory module !!!! from SS 23 to WS 24/25: Fortgeschrittene Künstliche Intelligenz |
M-INFO-107198
(M-INFO-106299) |
T-INFO-114220
(T-INFO-112768) |
6 | V | |
| Automatische Sichtprüfung und Bildverarbeitung | M-INFO-100826 | T-INFO-101363 | 6 | V | |
| Computer Vision für Mensch-Maschine-Schnittstellen (selectable up to SS 21) | M-INFO-100810 | T-INFO-101347 | 6 | V | |
|
Deep Learning für Computer Vison (selectable up to SS 21) |
M-INFO-104099 | T-INFO-108484 | 3 | V | |
|
Deep Learning and Neural Networks (from SS 2025 English title) former: Deep Learning und Neuronale Netze former: Neuronale Netze |
M-INFO-107197 (M-INFO-104460) |
T-INFO-114219 (T-INFO-109124) |
6 | V | |
| Energieinformatik 1 (selectable up to SS 24) | M-INFO-101885 | T-INFO-103582 | 5 | V | |
| Energieinformatik 2 (selectable up to SS 24) | M-INFO-103044 | T-INFO-106059 | 5 | V | |
| Gehirn und Zentrales Nervensystem | M-INFO-100725 | T-INFO-101262 | 3 | V | |
| Grundlagen der Automatischen Spracherkennung (selectable until WS 23/24) | M-INFO-100847 | T-INFO-101384 | 6 | V | |
| Hands-on Bioinformatics Practical | M-INFO-101573 | T-INFO-103009 | 3 | P | |
| Informationsverarbeitung in Sensornetzwerken (selectable up to SS 24) | M-INFO-100895 | T-INFO-101466 | 6 | V | |
| Inhaltsbasierte Bild- und Videoanalyse (selectable up to SS 23) |
M-INFO-100852 | T-INFO-101389 | 3 | V | |
| Introduction to Bioinformatics for Computer Scientists | M-INFO-100749 | T-INFO-101286 | 3 | V | |
|
IT Security (Advanced mandatory module) replaces from SS 2025 IT-Sicherheit (Advanced mandatory module) replaces from WS 23/24 Sicherheit (Advanced mandatory module) |
M-INFO-106315 | T-INFO-112818 | 6 | V | |
| Kontextsensitive Systeme | M-INFO-100728 | T-INFO-107499 | 5 | V | |
| Maschinelle Übersetzung | M-INFO-100848 | T-INFO-101385 | 6 | V | |
| Maschinelles Lernen - Grundverfahren (selectable until SS 21) | M-INFO-105252 | T-INFO-110630 | 5 | V | |
| Mustererkennung | M-INFO-100825 | T-INFO-101362 | 3 | V | |
| Next Generation Internet | M-INFO-100784 | T-INFO-101321 | 4 | V | |
| Seminar: Hot Topics in Bioinformatics | M-INFO-100750 | T-INFO-101287 | 3 | S | |
| Stochastische Informationsverarbeitung | M-INFO-100829 | T-INFO-101366 | 6 | V | |
| Unscharfe Mengen (selectable up to WS 24/25) | M-INFO-100839 | T-INFO-101376 | 6 | V | |
| Visualisierung | M-INFO-100738 | T-INFO-101275 | 5 | V | |
| Complementary subject "Mathematik für Daten-Intensives Rechnen" / "Mathematics for Data Intensive Computing" (at least 9 CP) | Course | Module | Partial achievement | CP | Course type |
| Einführung in das Wissenschaftliche Rechnen | M-MATH-102889 | T-MATH-105837 | 9 | V | |
| Extremwerttheorie (Prerequisite: Wahrscheinlichkeitstheorie M-MATH-101322) | M-MATH-102939 | T-MATH-105908 | 5 | V | |
| Generalisierte Regressionsmodelle | M-MATH-102906 | T-MATH-105870 | 5 | V | |
| Mathematische Modellierung und Simulation in der Praxis | M-MATH-102929 | T-MATH-105889 | 5 | V | |
| Nichtparametrische Statistik (Prerequisite: Wahrscheinlichkeitstheorie M-MATH-101322) | M-MATH-102910 | T-MATH-105873 | 5 | V | |
| Numerische Lineare Algebra für das wissenschaftliche Rechnen auf Hochleistungsrechnern | M-MATH-103709 | T-MATH-107497 | 5 | V | |
| Optimierungstheorie | M-MATH-103219 | T-MATH-106401 | 9 | V | |
| Statistik (+ Practice) | M-MATH-103220 | T-MATH-106415 | 10 | V | |
| Vorhersagen: Theorie und Praxis (Prerequisite: Wahrscheinlichkeitstheorie M-MATH-101322) | M-MATH-102956 | T-MATH-105928 | 9 | V | |
| Zeitreihenanalyse (Prerequisite: Wahrscheinlichkeitstheorie M-MATH-101322) | M-MATH-102911 | T-MATH-105874 | 5 | V | |
| Complementary subject "Betriebswirtschaftslehre für datenintensives Rechnen" (at least 9 CP) M-INFO-104199 |
Course | Module | Partial achievement | CP | Course type |
| Artificial Intelligence in Service Systems - Applications in Computer Vision | M-INFO-104199 | T-WIWI-111219 | 4,5 | V | |
| Business Data Strategy | T-WIWI-106187 | 5 | V | ||
| Business Intelligence Systems | T-WIWI-105777 | 5 | V | ||
| Marketing Analytics | T-WIWI-103139 | 5 | V | ||
| Modeling and Analyzing Consumer Behavior in R | T-WIWI-102899 | 5 | V | ||
| Multivariate Verfahren | T-WIWI-103124 | 5 | V | ||
| OR in Supply Chain Management | T-WIWI-102715 | 5 | V | ||
| Recommender Systeme | T-WIWI-102847 | 5 | V | ||
| Statistik für Fortgeschrittene | T-WIWI-103123 | 5 | V | ||
| Service Analytics A (not applicable as of SS 2021) | T-WIWI-105778 | 4,5 | V | ||
| Complementary subject "Materialwissenschaften für datenintensives Rechnen" (at least 9 CP) | Course | Module | Partial achievement | CP | Course type |
| Atomistische Simulation und Molekulardynamik | M-INFO-104200 | T-MACH-105308 | 4 | V | |
| Einführung in die Finite-Elemente-Methode | T-MACH-105320 | 4 | V | ||
| PREREQUISITES FOR THE CLASS ASSIGNMENT to the course "Einführung in die Finite-Elemente-Methode": Exercises to "Einführung in die Finite-Elemente-Methode" | T-MACH-110330 | 1 | Ü | ||
| Mikrostruktursimulation | T-MACH-105303 | 5 | V | ||
| Statistik | T-MATH-106415 | 10 | V | ||
| Statistik | T-MATH-106415 | 0 | P | ||
| Werkstoffmodellierung: versetzungsbasierte Plastizität | T-MACH-105369 | 4 | V | ||
| Werkstoffsimulation | T-MACH-107660 | 8 | S | ||
|
Computational Homogenization on Digital Image Data no longer offered as of WS 23/24!!! |
M-MACH-102646 | T-MACH-109302 | 6 | V | |
|
Digital microstructure characterization and modeling no longer offered as of WS 23/24!!! |
M-MACH-102597 | T-MACH-110431 | 6 | V | |
|
Nichtlineare Optimierungsmethoden no longer offered as of WS 23/24!!! |
T-MACH-110380 |