Course of Study Data Science (Study Cohort w22)

Sample course plan G Bachelor Data Science (DSBS)
Specialisation I. Mathematics/Computer Science, Specialisation II. Application

Legend:
Core Qualification Compulsory	Specialisation Compulsory	Focus Compulsory	Thesis Compulsory
Core Qualification Elective Compulsory	Specialisation Elective Compulsory	Focus Elective Compulsory	Interdisciplinary complement


Discrete Algebraic Structures
Discrete Algebraic Structures	VL	2
Discrete Algebraic Structures	GÜ	2


Automata Theory and Formal Languages
Automata Theory and Formal Languages	VL	2
Automata Theory and Formal Languages	GÜ	2


Databases
Databases	VL	3
Databases - Exercise	GÜ	2


Signals and Systems
Signals and Systems	VL	3
Signals and Systems	GÜ	2


Introduction to Information Security
Introduction to Information Security	VL	2
Introduction to Information Security	GÜ	2


Ethics in Information Technology
Ethics in Information Technology	VL	2
Ethics in Information Technology	SE	2


Procedural Programming for Computer Engineers
Procedural Programming for Computer Engineers	VL	2
Procedural Programming for Computer Engineers	HÜ	1
Procedural Programming for Computer Engineers	PR	2


Stochastics
Stochastics	VL	2
Stochastics	GÜ	2


Numerical Mathematics I
Numerical Mathematics I	VL	2
Numerical Mathematics I	GÜ	2


Graph Theory and Optimization
Graph Theory and Optimization	VL	2
Graph Theory and Optimization	GÜ	2


Data Mining
Data Mining	VL	2
Data Mining	PBL	2


Introduction to Electrical Engineering (Technomathematics)
Introduction to Electrical Engineering	VL	3
Introduction to Electrical Engineering	GÜ	2


Mathematics I (EN)
Mathematics I	VL	4
Mathematics I	HÜ	2
Mathematics I	GÜ	2


Foundations of Management
Introduction to Management	VL	3
Management Tutorial	GÜ	2


Algorithms and Data Structures
Algorithms and Data Structures	VL	4
Algorithms and Data Structures	GÜ	1


Seminars Computer Science
Introductory Seminar Computer Science II	SE	2
Introductory Seminar Computer Science I	SE	2


Machine Learning II
Machine Learning II	VL	2
Machine Learning II	GÜ	3


Bachelor Thesis


Programming Paradigms
Programming Paradigms	VL	2
Programming Paradigms	HÜ	1
Programming Paradigms	PR	2


Statistics
Statistics	VL	3
Statistics	GÜ	1


Scientific Programming
Scientific Programming	VL	3
Scientific Programming	GÜ	2


Computer Engineering
Computer Engineering	VL	3
Computer Engineering	GÜ	1


Introduction to Data Science
Introduction to Data Science	VL	2
Introduction to Data Science	SE	1


Mathematics II (EN)
Mathematics II	VL	4
Mathematics II	HÜ	2
Mathematics II	GÜ	2


Mathematics III (EN)
Analysis III	VL	2
Analysis III	HÜ	1
Analysis III	GÜ	1
Differential Equations 1	VL	2
Differential Equations 1	HÜ	1
Differential Equations 1	GÜ	1


Machine Learning I
Machine Learning I	VL	2
Machine Learning I	GÜ	3


Combinatorial Structures and Algorithms
Combinatorial Structures and Algorithms	VL	3
Combinatorial Structures and Algorithms	GÜ	1

Non-technical Courses for Bachelors (from catalogue) - 6LP

The choice of courses from the catalogue is flexible (depends on the semestral work load), provided the necessary number of required credits is reached.