Applied Computer Science (M.Sc.)

Applied Informatics - A degree programme with a bright future!

The Master programme in Applied Informatics is a continuation programme and suitable for those with a Bachelor’s degree in Informatics or a closely related subject. The Master programme in Applied Informatics offers three specialisations.

&nbs

Autonomous systems
Data science
IT security

Networked information technology is permeating into all corners of our life. Like autonomous vehicles, autonomous systems need sensor systems and large amounts of data; networked systems on the other hand must have a secure design, the masses of data which accumulate have to be analysed. The system developers and software architects we train need comprehensive specialist knowledge and additional skills. They obtain these in the Master programme in Applied Informatics.

EQUIPPED TO TACKLE THE TASKS OF TOMORROW

The Master programme in Applied Informatics equips students with more in-depth theoretical knowledge in the various fields of informatics and extends their expertise in selected fields of specialisation. This qualifies students to undertake development work and also to take on management functions in industrial companies. The Master programme also qualifies students to go on to do their doctorate and ultimately to work in research.

The choice is yours

The Master programme in Applied Informatics offers three specialisations. Autonomous Systems deals with the methods and technologies used in autonomous vehicles and systems. Data Science focuses on the analysis and exploitation of Big Data from industry, research and commerce. The IT Security specialisation teaches the skills needed to provide better protection for systems, networks and associated components such as software.

If none of the three specialisations appeals to you, you can choose an individual specialisation to suit your interests. When all elective modules are chosen from one specialisation, the field of specialisation is stated on the degree certificate. When an individual specialisation is chosen, no field of specialisation is stated on the degree certificate.

Research and development

The Master programme equips students with skills and abilities which qualify them to enter their profession and extend far beyond those taught in a Bachelor programme. The curriculum is very research oriented. Students must complete two large research projects and write a publication in addition to their Master thesis. The close cooperation with the Fraunhofer KEIM Applied Research Center which is sited at the Faculty of Information Technology ensures that students can always choose from a range of interesting and current research topics. The Faculty of Information Technology has a sound network of industrial partners which offer assignments for research projects and final theses. This ensures the curriculum is always topical, and focused on research and application.

How is the programme structured?

The programme takes 3 semesters to complete and the first two semesters are each divided into three sections: core modules on information technology, electives and research projects. The work for the Master thesis is done in the third semester.

The core modules extend the fundamental theoretical knowledge with advanced courses on information technology, IT security and theoretical computer science. The research project can be done in a company, research institution or at the Faculty of Information Technology. Electives can be selected to suit personal preferences from one of the three specialisations - autonomous systems, data science or IT security - or can also be taken from more than one field if a student wishes to create their own specialisation. The programme concludes with the Master thesis in the third semester. The work for the Master thesis can also be done in a company, research institution or at the Faculty of Information Technology.

DOCTORATE

On successful completion of the Master programme, it is possible to do a collaborative doctorate at a partner university or at the Fraunhofer KEIM Applied Research Center in cooperation with the University of Stuttgart.

Curriculum

Curriculum Applied Informatics (M.Sc.)

Master's Degree program leaflet

Applied Computer Science (M.Sc.) in German

Facts and Figures

Facts and Figures - at a glance

Degree awarded	Master of Science (M.Sc.)
Faculty	Computer Science and Engineering
Campus	Esslingen Hilltop Campus
Number of semesters	3
Language of instruction	german/english
Application Periods	For summer semester from 15 October to 15 January For the winter semester: up to July 15th
Information on admission requirements	Admission limitation, number of places available (30 per year)
Specialisation	Autonomous systems The specialisation in autonomous systems equips students with the skills necessary to develop and realise smart, autonomous systems. A typical field of application is autonomous driving. Data from various sensors are processed and analysed and combined with a model of the environment, the road map. Intelligent algorithms then calculate the rules of conduct for the autonomous system. Keywords: Data Fusion, Intelligent Data Analytics, Machine Learning, Advanced Control, Autonomous System Data science The data science specialisation provides you with the skills to generate information from enormous amounts of data. A typical field of application is to obtain faster and better information for corporate management and to enhance productivity. Big Data is intended to provide companies with an advantage when developing new products and business models. Keywords: Big Data, Data Mining, Intelligent Data Analytics, Cloud Computing, Advanced Data Models IT security The IT security specialisation teaches students the skills they need to develop and realise secure IT systems. A typical field of application is safeguarding the IT infrastructure of a company from both a network and a software point of view. One of the main tasks is managing IT security in companies. Keywords: Network Security, Secure Coding, Digital Forensics, Penetration Testing, Information Security Management Individual fields of specialisation An individual field of specialisation incorporating some or all of the specialisations offered in the programme is also possible. Students who choose this option can select electives from all three specialisations according to their personal interests. In this case, the degree certificate does not list a specialisation.

Application and Admission

HOW TO APPLY

Applicants are required to have a Bachelor’s degree in information technology or a closely related subject. Applications for this Master programme must be submitted online via the Master degree programme portal of Esslingen University of Applied Sciences.

You can commence your studies in either the winter or the summer semester.

The deadlines for submitting applications are

15 January for the summer semester
15 July for the winter semester

Information on the documents which must be submitted can be found in the checklist for Master programmes.

ADDITIONAL DOCUMENTS WHICH MUST BE SUBMITTED WITH YOUR APPLICATION:

Appendix to the application for admission

Applicants with a degree from a foreign university

Esslingen University of Applied Sciences requires a university degree awarded by a foreign university to be recognised by the Center for International Students at Constance University of Applied Sciences (Studienkolleg Konstanz).

Modules

3. Semester	Master Thesis 25 ECTS Master Thesis Prerequisites: Knowledge of the methods of scientific work, safe application of the methods of software engineering, comprehensive knowledge in the chosen specialisation. Learning Outcomes: Knowledge - professional competences The students know - the quality criteria for scientific work - the methods of project management Skills - methodical competences Students are able to - to formulate scientific questions, - to apply scientific methods, - to plan and carry out scientific projects, - to use the knowledge acquired in the core and special subjects to solve problems, - to research, understand and evaluate solutions (state of the art), - to develop and implement their own solutions, - document the results of their scientific work in a comprehensible manner. Interdisciplinarity - multidisciplinary competences Students can - to work on a complex problem of computer science independently, scientifically, within a given period of time, - to research, structure and understand the corresponding state of research, - select appropriate methods and procedures, apply them correctly and, if necessary, adapt or further develop them, - to compare their results with other results and to review their solution approaches critically, - to document their results in a structured manner and to publish them in scientific form. Content: - Problem analysis and delimitation of the topic - Literature research - Planning of the procedure, development of a solution approach - Time and project management - Establishing a link between own approaches and the state of research - Scientific presentation of the results - Presentation Type of teaching and language of instruction: Self-reliant scientific work German or English Examination: Report graded, Presentation (20 Min) graded, Scientific Publication 5 ECTS Scientific Publication Prerequisites: Knowledge of the methods of scientific work, successful participation in the research project. Learning Outcomes: Knowledge - professional competences The students know - the formal aspects of a scientific publication, - suitable journals and conferences. Skills - methodical competences Students are able to - narrow down a topic for publication, - research, structure, understand and reflect the state of the art in research, - to establish links between one's own approaches and the state of research. Interdisciplinarity - multidisciplinary competences Students can - Document research results in a structured way and bring them into a form ready for publication. Content: Self-reliant scientific writing Type of teaching and language of instruction: Writing a scientific publication, German or English Examination: Publication graded
2. Semester	Advanced Software Engineering 5 ECTS Advanced Software Engineering Prerequisites: Basics of software engineering Learning Outcomes: Knowledge - professional competences The students know - Agile procedures in major projects: Scrum/Agile@Scale, Agile Requirements Engineering, - Architectural models for large systems: (a)Synchronous communication, batches, transactions, - Current Methods of Software Quality, Software Measurements and Metrics, Testing, - Software Archaeology Methods, - Security aspects in system development. Skills - methodical competences Students are able to - Apply project & risk management methods in real software projects, - Develop enterprise architectures: SOA, Microservices, Governance, Software in the Cloud, DevOps, - to participate in the design and implementation of architecture and software engineering processes of real projects. Interdisciplinarity - multidisciplinary competences Students can - Solving software engineering problems in real modern projects, - Work in globally distributed teams. Content: Focus on important current aspects with practical relevance to architecture, quality, procedures and digital transformation in software engineering. Type of teaching and language of instruction: Lecture with exercises with integrated exercises, German Examination: Exam (90 minutes) graded Artificial Intelligence 5 ECTS Artificial Intelligence Prerequisites: Knowledge of the programming language Python, linear algebra, statistics. Learning Outcomes: Knowledge - professional competences The students know Fundamentals of statistical learning theory, Methods of supervised learning, Methods of unsupervised learning, Basics of the Python Data-Science Stack. Skills - methodical competences Students are able to select appropriate procedures for specific problems, to apply the learned procedures with the help of the programming language Python, to interpret the results of the procedures. Interdisciplinarity - multidisciplinary competences Students can select appropriate procedures for specific problems, to apply the learned procedures with the help of the programming language Python, to interpret the results of the procedures, use machine learning methods to solve problems in other domains. Content: Basics of probability theory Basics Linear Algebra Basic concepts of statistical learning theory Supervised learning methods Unsupervised learning methods Python libraries for machine learning Type of teaching and language of instruction: Lecture with exercises with integrated exercises, German Examination: exam (90 MIn) graded Elective 3 5 ECTS Elective 3 Prerequisites: Depending on the selected module. Learning Outcomes: The students gain a scientific and subject-specific specialisation in the main field of study. Content: Depending on the chosen module. Type of teaching and language of instruction: Depending on the chosen module, German or English Examination: Depending on the chosen module. Elective 4 5 ECTS Elective 4 Prerequisites: Depending on the selected module. Learning Outcomes: The students gain a scientific and subject-specific specialisation in the main field of study. Content: Depending on the chosen module. Type of teaching and language of instruction: Depending on the chosen module, German or English Examination: Depending on the chosen module. Research Project 2 10 ECTS Research Project 2 Prerequisites: Application of the methods of software development, knowledge of the chosen specialisation, basic knowledge of scientific work. Learning Outcomes: Knowledge - professional competences The students know - the quality criteria for scientific work, - the methods of project management. Skills - methodical competences Students are able to - to plan and carry out scientific projects as a team, - the knowledge acquired in the core and in-depth subjects for solving the problem of problems in the field of research, - to research and understand solutions (state of the art), - evaluate the solutions found, - document the results of their scientific work in a comprehensible manner. Interdisciplinarity - multidisciplinary competences Students can - to solve under guidance complex problems from research or industry within a given period of time, - to gain new knowledge in computer science and to develop new methods, - Integrate knowledge from different domains, - to successfully implement a task together in a team. Content: In the research project, students work in a team under the guidance of a lecturer on current research topics from scientific institutions or research-related topics from industry. The projects are scheduled to run for one year, with all phases of a software project being completed: Problem and requirements analysis, state of the art research, project planning, development of solutions, software design, implementation, test phase. Students develop work plans and schedules and report regularly on their progress. At the end of each semester, students present intermediate and final results. Type of teaching and language of instruction: Self-reliant scientific project work German or English Examination: Report and Presentatioin (20 Min) graded
1. Semester	Theoretical Computer Science 5 ECTS Theoretical Computer Science Prerequisites: Discrete Mathematics Learning Outcomes: Knowledge - professional competences The students know Basic concepts and mathematical principles of theoretical computer science Languages of the Chomsky hierarchy with the corresponding calculation models Concept of predictability and the meaning of undecidability The halting problem and the sentence of Gödel Classes P / NP, as well as the concept of NP severe problems Selected NP severe problems Fundamentals of information theory Skills - methodical competences Students are able to formulate grammars for languages, distinguish deterministic from non-deterministic models and estimate their power against each other, to identify and classify combinatorially difficult calculation problems, to understand the limits of information processing and to classify them correctly. Interdisciplinarity - multidisciplinary competences Students can Formally specify systems and prove properties, apply the techniques of evidence learned to other problems. Content: Languages and automata: Chomsky 3 Languages and Finite Automata Chomsky 2 languages and finite automata Chomsky 1+0 languages and Turing machines Fundamentals of computability theory Calculation models and their relationship; thesis of Curch Holding problem Godell's theorem Fundamentals of complexity theory P and NP and NP severe problems Selected NP severe problems Fundamentals of information theory Entropy as a concept of information Source encoding method Translated with www.DeepL.com/Translator (free version) Type of teaching and language of instruction: Lecture with exercises with integrated exercises, German Examination: Exam (90 minutes) graded Security Engineering 5 ECTS Security Engineering Prerequisites: Basics of linear and discrete mathematics Learning Outcomes: Students are proficient in risk assessment methods and are able to select and apply appropriate methods for the secure design, implementation and operation of an IT system. Students will be able to analyse and evaluate the IT security of applications. They are able to systematically develop and present appropriate IT security solutions. Students will be able to use technologies and procedures to develop a secure, complex and heterogeneous IT system after assessing the security risk. Knowledge - professional competencies The students know: - Modern encryption methods - Cryptographic Protocols - Methods of provably secure cryptography, - Security System Modelling, - Security Requirements Engineering, - Security Design Principles, - Verification of security components. Skills - methodical competences Students are able to - to analyze your security systems, - to analyze security risks, - Validate and verify systems for security, - to design information-technically safe systems. Interdisciplinarity - multidisciplinary competences Students can - Apply security risk assessment, procedures and technologies to develop a secure, complex and heterogeneous IT system. Content: - Discrete Mathematics - Modern encryption methods - Elliptical curves - Cryptographic protocols - Security System Modelling - Security Requirements Engineering - Security risk assessment - Security design principles and patterns - Verification of security protocols - Verification of security components - Secure coding Type of teaching and language of instruction: Lecture with exercises with integrated exercises, German Examination: Exam (90 Min) graded Elective 1 5 ECTS Elective 1 Prerequisites: Depending on the selected module. Learning Outcomes: The students gain a scientific and subject-specific specialisation in the main field of study. Content: Depending on the chosen module. Type of teaching and language of instruction: Depending on the chosen module, German or English Examination: Depending on the chosen module. Elective 2 5 ECTS Elective 2 Prerequisites: Depending on the selected module. Learning Outcomes: The students gain a scientific and subject-specific specialisation in the main field of study. Content: Depending on the chosen module. Type of teaching and language of instruction: Depending on the chosen module, German or English Examination: Depending on the chosen module. Research Project 1 10 ECTS Research Project 1 Prerequisites: Application of the methods of software development, knowledge of the chosen specialisation, basic knowledge of scientific work. Learning Outcomes: Knowledge - professional competences The students know - the quality criteria for scientific work, - the methods of project management. Skills - methodical competences Students are able to - to plan and carry out scientific projects as a team, - the knowledge acquired in the core and in-depth subjects for solving the problem of problems in the field of research, - to research and understand solutions (state of the art), - evaluate the solutions found, - document the results of their scientific work in a comprehensible manner. Interdisciplinarity - multidisciplinary competences Students can - to solve under guidance complex problems from research or industry within a given period of time, - to gain new knowledge in computer science and to develop new methods, - Integrate knowledge from different domains, - to successfully implement a task together in a team. Content: In the research project, students work in a team under the guidance of a lecturer on current research topics from scientific institutions or research-related topics from industry. The projects are scheduled to run for one year, with all phases of a software project being completed: Problem and requirements analysis, state of the art research, project planning, development of solutions, software design, implementation, test phase. Students develop work plans and schedules and report regularly on their progress. At the end of each semester, students present intermediate and final results. Type of teaching and language of instruction: Self-reliant scientific project work German or English Examination: Report and Presentatioin (20 Min) graded

The curriculum for the degree programme and detailed descriptions of the programme modules are contained in the Module Catalogue.

Course Syllabi Applied Computer Science (M.Sc.) (German Version)

Career prospects

Applied Informatics - A degree programme with a bright future

All graduates of the Applied Informatics Master programme have sound knowledge in the key areas of advanced software engineering, artificial intelligence, security engineering and computer sciences. This key knowledge enables them to approach their work from an engineering perspective, apply sound methodologies to undertake and manage software projects, focusing attention on IT security in particular. The two research projects moreover help students to enhance their technical and social skills, such as abstract reasoning, the ability to analyse systems and to communicate and work in a team, and thus prepare them for their future management roles.

Specialisation: Autonomous Systems

Those who choose the autonomous systems specialisation additionally have sound knowledge of the following fields of application: data fusion, machine vision, machine learning, control systems, high-performance computing and automotive systems design. They are particularly able to apply sound methodology to develop autonomous, networked systems. They are able to work on complex research and development tasks from an engineering and mathematical perspective. They start by abstracting the issue, expressing it mathematically and creating a model, and are thus able to derive intelligent algorithms or develop them further, when required.

They are typically employed as specialists or managers for Industry 4.0, the Internet of Things and autonomous driving.

Specialisation: Data Science

Those who choose the data science specialisation additionally have sound knowledge of the following fields of application: big data, data mining, intelligent data analytics, cloud computing, and advanced data models. They are able to tackle complex research and development tasks from an engineering and mathematical perspective. They are particularly able to systematically analyse large amounts of data, to derive important characteristics therefrom and use them to gain crucial insights.

They are typically employed as specialists or managers for data science, or digitisation in a corporate environment.

Specialisation: IT Security

Those who choose the IT security specialisation have sound knowledge of the following fields of application: network security, secure coding, penetration testing, digital forensics. They are able to tackle complex research and development tasks concerned with IT security from an engineering perspective. They have the special ability to safeguard existing and new IT systems against all types of attacks.

They are typically employed as specialists or IT security managers in companies.

Graduates of this Master programme are very well qualified to take on a managerial role in Research and Development.

Accreditation

The Master programme offered by the Faculty of Information Technology has been successfully accredited by the ACQUIN accreditation agency.

ACQUIN accreditation certificate Esslingen University of Applied Sciences Applied Informatics Master programme

Further information on the accreditation and the ACQUIN Institute is available at www.acquin.org

Apply for summer semester 2022!

The application period for the summer semester 2022 starts on October 15th 2021 and runs until January 15th 2022.

Apply now

Applied Computer Science (M.Sc.)

General Information

Facts and Figures

Application and Admission

Modules

Career prospects

Accreditation

General Information

Applied Informatics - A degree programme with a bright future!

The choice is yours

Research and development

How is the programme structured?

Curriculum

Master's Degree program leaflet

Facts and Figures

Facts and Figures - at a glance

Application and Admission

HOW TO APPLY

ADDITIONAL DOCUMENTS WHICH MUST BE SUBMITTED WITH YOUR APPLICATION:

Applicants with a degree from a foreign university

Modules

Career prospects

Applied Informatics - A degree programme with a bright future

Specialisation: Autonomous Systems

Specialisation: Data Science

Specialisation: IT Security

Accreditation

Apply for summer semester 2022!

Applied Computer Science (M.Sc.)

Applied Informatics - A degree programme with a bright future!

The choice is yours

Research and development

How is the programme structured?

Curriculum

Master's Degree program leaflet

Facts and Figures - at a glance

HOW TO APPLY

ADDITIONAL DOCUMENTS WHICH MUST BE SUBMITTED WITH YOUR APPLICATION:

Applicants with a degree from a foreign university

Master Thesis

Scientific Publication

Advanced Software Engineering

Artificial Intelligence

Elective 3

Elective 4

Research Project 2

Theoretical Computer Science

Security Engineering

Elective 1

Elective 2

Research Project 1

Applied Informatics - A degree programme with a bright future

Specialisation: Autonomous Systems

Specialisation: Data Science

Specialisation: IT Security

Apply for summer semester 2022!