Applied Computer Science (M.Sc.)

Applied Informatics - A degree programme with a bright future!

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

This study programme is taught in German. A TOEFL certificate level C1 is required.

    The Master programme in Applied Informatics offers three specialisations.

    • 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; Systems attached to networks however 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 Computer Science.

    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 may 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 may 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

    Discover 17 more Master Programs

    Facts and Figures - at a glance

    Degree awardedMaster of Science (M.Sc.)
    FacultyComputer Science and Engineering
    CampusEsslingen Hilltop Campus
    Number of semesters3
    Language of instructionGerman/English
    Bewerbungszeiträume

    For summer semester from 15 October to 15  January

    For the winter semester: from April 24th 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.

    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).

    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)

    Discover 17 more Master Programs

    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.

    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

    In July 2021, the programme was re-accredited. The accreditation has been extended the validity until the final approval by the accreditation council.

    Foundation German Council of Accreditation

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