Course of Study Data Science (Study Cohort w20)

Sample course plan B Bachelor Data Science (DSBS)
Specialisation Materials Science

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

Semester 1

Form

Hrs/wk

Semester 2

Form

Hrs/wk

Semester 3

Form

Hrs/wk

Semester 4

Form

Hrs/wk

Semester 5

Form

Hrs/wk

Semester 6

Form

Hrs/wk


Discrete Algebraic Structures
Discrete Algebraic Structures	VL	2
Discrete Algebraic Structures	UE	2


Automata Theory and Formal Languages
Automata Theory and Formal Languages	VL	2
Automata Theory and Formal Languages	UE	2


Databases
Databases	VL	4
Databases	PBL	1


Signals and Systems
Signals and Systems	VL	3
Signals and Systems	UE	2


Introduction to Information Security
Introduction to Information Security	VL	3
Introduction to Information Security	UE	2


Seminars Data Science
Seminar Data Science I	SE	2
Seminar Data Science II	SE	2


Procedural Programming
Procedural Programming	VL	1
Procedural Programming	HÜ	1
Procedural Programming	PR	2


Stochastics
Stochastics	VL	2
Stochastics	UE	2


Numerical Mathematics I
Numerical Mathematics I	VL	2
Numerical Mathematics I	UE	2


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


Data Mining
Data Mining	VL	2
Data Mining	UE	2


Enhanced Fundamentals of Materials Science
Enhanced Fundamentals: Metals	VL	2
Enhanced Fundamentals: Ceramics and Polymers	VL	2
Enhanced Fundamentals: Ceramics and Polymers	HÜ	1


Linear Algebra
Linear Algebra	VL	4
Linear Algebra	HÜ	2
Linear Algebra	UE	2


Mathematical Analysis
Mathematical Analysis	VL	4
Mathematical Analysis	HÜ	2
Mathematical Analysis	UE	2


Mathematics III
Analysis III	VL	2
Analysis III	UE	1
Analysis III	HÜ	1
Differential Equations 1	VL	2
Differential Equations 1	UE	1
Differential Equations 1	HÜ	1


Graph Theory and Optimization
Graph Theory and Optimization	VL	2
Graph Theory and Optimization	UE	2


Practical Course Data Science
Practical Course Data Science	PR	8


Bachelor Thesis


Scientific Programming
Scientific Programming	VL	3
Scientific Programming	UE	2


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


Fundamentals of Materials Science (part 1)
Fundamentals of Materials Science I	VL	2
Physical and Chemical Basics of Materials Science	VL	2


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


Algorithms and Data Structures
Algorithms and Data Structures	VL	4
Algorithms and Data Structures	UE	1


Machine Learning
Machine Learning	VL	2
Machine Learning	UE	2


Introduction to Communications and Random Processes
Introduction to Communications and Random Processes	VL	3
Introduction to Communications and Random Processes	HÜ	1
Introduction to Communications and Random Processes	UE	1


Fundamentals of Materials Science (part 2)
Fundamentals of Materials Science II	VL	2


Advanced Stochastics
Advanced Stochastics	VL	2
Advanced Stochastics	UE	2


Advanced Materials
Advanced Materials Characterization	VL	2
Advanced Materials Design	VL	2
Advanced Materials Design	HÜ	2

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.