Informatics bachelor degree programme

Would you like to play a creative role in shaping the ways in which we will live and work in the future? Are you also interested in technology-related fields? Then our Informatics programme is the perfect choice for you!

One thing’s for sure: informatics has had a bigger impact on developments in how we live and work than any other field over the past few decades, and will grow in significance over the years to come.

The degree programme: Bachelor of Science in Engineering (BSc)

As of autumn 2019 the IMC University of Applied Sciences Krems will offer the english taught Bachelor degree programme Informatics.

Informatics is a key driver of innovation that is fuelling advances in many aspects of working and everyday life. Computers make it possible to model and predict economic, ecological and social developments; bioinformatics supports the development of new drugs; and thanks to machine learning, large volumes of data become clear and readable, while pattern recognition turns it into a valuable resource with a variety of potential applications. Informatics opens up new options for creating economic added value, protecting the environment and saving lives.

IMC Krems’ English-language Informatics Bachelor degree programme is aimed at people who are interested in technology-related subjects and want to play a creative part in shaping aspects of the way we will live and work in the future. The programme includes a specialisation in data science, as well as electives with a strong practical focus: Business Process and Enterprise Technologies and Bioinformatics.

Graduates can look forward to a wide range of career options, including in software development, information-based decision making and IT consulting. They will be in strong demand both in Austria and abroad, with a skills set aligned with future requirements.

This programme provides you with a broad, non-industry-specific education in informatics and will qualify you for entry level roles in a wide range of organisations and industries, as well as advanced master-level study.

“Computer science is not about machines, in the same way that astronomy is not about telescopes.” (Edsger Wybe Dijkstra)

Informatics

Informatics graduates are experts in the ways in which science and technology can help us to process information, translate it into knowledge and then make this knowledge accessible in the form of services. Graduates can work as software developers, data scientists and IT consultants, and develop human-computer interaction interfaces.

The internet of things, Industry 4.0 and social media, as well as the exponential growth in storage capacity and the increase in cloud-based processing power are all synonymous with transformative information-based technologies.

Knowledge and advances in informatics are key drivers of innovation which lead to developments in other fields. This discipline puts a strong focus on teamwork and practical application.

Those who can imagine anything, can create the impossible.

Pioneering computer scientist Alan Turing

Curriculum

What can you expect from your studies? The curriculum provides an overview.
Click on the individual courses for further information.

Semester 1

Course	SWS	ECTS
Mathematics, Statistics and Theoretical Computer Science
Mathematics I
Mathematics I - Theory	2	3
Mathematics I - Theory Module: Mathematics I Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 1 Course code: MATIT1VO Contact hours per week: 2 ECTS: 3 Course Content: Basic mathematical concepts (number theory, logic, sets and combinatorics) Graph theory & applications (directionality, degree, trees & forests, route problems & algorithms) Linear algebra & applications (vector spaces, matrices, endo-/homomorphism) Coding theory & applications (linear coding, cryptography) Introduction to R software for algebraic mathematics Course outcome: Upon completion of the module, students are able to: formulate, calculate and correctly correlate classic computer science problems with their mathematical foundations, explain the relation of practical applications in computer science and their corresponding mathematical fields and theories, utilize a combination of manual solving and scientific computing applications to solve the problems and verify their solutions, explain their solutions to the given problems (on paper), use the R software to solve similar problems computationally.
Mathematics I - Exercise	2	3
Mathematics I - Exercise Module: Mathematics I Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 1 Course code: MATIE1UE Contact hours per week: 2 ECTS: 3 Course Content: Accompanying practical mathematical exercises to the lecture "Mathematics I - Theory"
Statistics and Probability in Computer Science
Statistics and Probability in Computer Science - Theory	2	3
Statistics and Probability in Computer Science - Theory Module: Statistics and Probability in Computer Science Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 1 Course code: SPCST1VO Contact hours per week: 2 ECTS: 3 Course Content: Combinatorics (permutations, enumeration) Stochastics Probability theory (probability spaces, random variables, distributions, standard deviation & variance, quantiles, independence) Joint & conditional probabilities, Bayes Theorem, law of large numbers Statistical testing of hypotheses (significance tests, confidence intervals) Descriptive statistics Applications in Data Science (statistical inference and predictions) Introduction to R software for statistics Course outcome: Upon completion of the module, students are able to: explain the foundational concepts of mathematical statistics, perform calculations of probabilities, deviation and variance, utilize statistical tests of hypotheses, both on paper and with computational software (R), use the R software to solve similar problems computationally, relate these concepts to data science applications and the available electives, utilize a combination of manual solving and scientific computing applications to solve the problems and verify their solutions, explain their solutions to the given problems on paper.
Statistics and Probability in Computer Science - Exercise	2	3
Statistics and Probability in Computer Science - Exercise Module: Statistics and Probability in Computer Science Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 1 Course code: SPCSE1UE Contact hours per week: 2 ECTS: 3 Course Content: Accompanying practical exercises to the lecture "Statistics and Probability in Computer Science - Theory"
Software Engineering and Data Modelling
Web Technologies	4	6
Web Technologies Module: Software Engineering and Data Modelling Root module: Software Engineering and Data Modelling Semester: 1 Course code: WT1ILV Contact hours per week: 4 ECTS: 6 Course Content: Practical Web-Project Languages of the web (e.g. XHTML, HTML5, CSS, JavaScript) Standard libraries and tools (JQuery, Angular/React, SVG) Foundational concepts (responsive design, WAI/W3C/accessible design) Protocols (HTTP(S)) Asynchronism (XMLHttpRequest. Ajax) Course outcome: Upon completion of this course, students are able to: conceptualize, design, develop and deploy static and dynamic webpages using languages such as XHTML, HTML5, CSS, JavaScript, assess and apply principles of responsive and accessible design and development to web applications in a project.
Programming I	4	6
Programming I Module: Software Engineering and Data Modelling Root module: Software Engineering and Data Modelling Semester: 1 Course code: PROGI1ILV Contact hours per week: 4 ECTS: 6 Course Content: Introduction to programming and programming languages Programming with Python Foundational concepts (variables, data types, operators, loops, branches, simple data structures, subroutines) Input/Output (incl. input validation) Iteration vs. Recursion Development operations in Python (modules and packages, virtual environments, libraries) Course outcome: Upon completion of this course, students are able to: explain basic concepts of programming, write simple programs in Python including input/output of data, set up and maintain a development environment in Python.
Computer Science and Society
Introduction: Applied Informatics	1	1
Introduction: Applied Informatics Module: Computer Science and Society Root module: Computer Science and Society Semester: 1 Course code: IAI1VO Contact hours per week: 1 ECTS: 1 Course Content: Introduction of the electives as continuation of the fundamentals of computer science Examples of applied informatics in the different fields of application Outlook towards the planned project work relating to the electives Course outcome: Upon completion of this course, students are able to: to discuss the SW development framework for programming and data analysis for an exemplary field (analysis, problem definition, solution design, implementation), understand the different domains in sufficient detail to be able to explain and select their elective path preference.
General Business Administration	2	3
General Business Administration Module: Computer Science and Society Root module: Computer Science and Society Semester: 1 Course code: GBUS1VO Contact hours per week: 2 ECTS: 3 Course Content: Basics of Business Administration and overview on and principles of: Human Resources Finance Marketing Operations Course outcome: Upon completion of this course, students are able to: explain basic business terms, identify and describe the structure, functioning and complexity of an organization and the dependencies of its parts, describe the fundamentals of doing business according to the classical different functions (Marketing, Accounting, Finance, HR, Operations), distinguish between strategic, and operative decisions.
Social Skills
Intercultural Competences	1	2
Intercultural Competences Module: Social Skills Root module: Social Skills Semester: 1 Course code: IC1WK Contact hours per week: 1 ECTS: 2 Course Content: Definition, elements and characteristics of culture National cultures and subcultures Contradictory views of culture Intercultural abilities and skills (cognitive, emotional and social components) Companies and cultural diversity, and intercultural experiences Course outcome: Upon completion of this course, students are able to: discuss problems arising in international projects or companies as a result of cultural differences between individuals and groups, and draw up approaches for resolving such problems, identify influences on communication and management, taking cultural circumstances into account, and describe the most important intercultural-management models, Critically reflect the cultural heterogeneity of their group.

Semester 2

Course	SWS	ECTS
Mathematics, Statistics and Theoretical Computer Science
Mathematics II	2	3
Mathematics II Module: Mathematics, Statistics and Theoretical Computer Science Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 2 Course code: MATII2ILV Contact hours per week: 2 ECTS: 3 Course Content: Basic concepts of mathematical analysis (sequences, series) Elementary functions & applications (limits, zero sets, continuity) Differential / integral calculus, differential equations Applications in Data Science (gradient descent) Course outcome: Upon completion of this course, students are able to: describe foundational concepts of mathematical analysis, solve problems from the domains of differential and integral calculus, both on paper and with computational software (R), relate the mathematical concepts to data science applications and the available electives.
Theoretical Computer Science and Logic	2	3
Theoretical Computer Science and Logic Module: Mathematics, Statistics and Theoretical Computer Science Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 2 Course code: TCSL2ILV Contact hours per week: 2 ECTS: 3 Course Content: Propositional logic (concepts, syntax & semant Finite-state machines (concepts, representations) Formal language theory (regular/context free grammars, regular expressions) Fundamentals of first order logic (concepts, syntax & semantics: alphabets, symbols, formation rules) Petri nets and applications Course outcome: Upon completion of this course, students are able to: demonstrate their knowledge and understanding of foundational theoretical computer science concepts, relate theoretical concepts and their paradigms to their applications in data science and the electives.
Algorithms and Data Structures I	2	3
Algorithms and Data Structures I Module: Mathematics, Statistics and Theoretical Computer Science Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 2 Course code: ADSI2ILV Contact hours per week: 2 ECTS: 3 Course Content: Descriptive analysis of algorithms (runtime complexity, notation) Algorithmic paradigms (greedy, divide and conquer, branch and bound) Sorting and searching (binary/balanced search trees, hashing, insertion sort, selection sort, merge sort, quicksort, bubble sort) Course outcome: Upon completion of this course, students are able to: explain foundational algorithmic concepts such as runtime complexity and notations, implement algorithmic paradigms for common software development tasks, evaluate algorithmic implementations based on their performance and applicability to the problem at hand.
Computer Systems
Technical Foundations of Computer Science	2	2
Technical Foundations of Computer Science Module: Computer Systems Root module: Computer Systems Semester: 2 Course code: TFCS2VO Contact hours per week: 2 ECTS: 2 Course Content: Binary numbers Information and coding theory Boolean algebra Logic circuits (adders, encoding, multiplexing) Foundations of computer hardware (CPU, BUS, memory, storage, mobile device technologies) Course outcome: Upon completion of this course, students are able to: explain the foundational concepts of information and coding theory, perform basic binary calculations and Boolean algebra, identify logic circuits and explain their use.
Networking Technologies and Management Systems I	2	2
Networking Technologies and Management Systems I Module: Computer Systems Root module: Computer Systems Semester: 2 Course code: NTMGTI2ILV Contact hours per week: 2 ECTS: 2 Course Content: Foundational concepts of network technologies (architectures, topologies, OSI-model, network addressing, routing, switching, NAT, subnets) Network protocols (ARP, TCP/IP – IPv4/6, DNS, UDP, ICMP) Transfer security concepts (firewalls, end-to-end encryption, public key encryption, ciphers, certificates, SSH/TLS) Network management (SNMP) Course outcome: Upon completion of this course, students are able to: explain foundational concepts in wired computer networks and technologies, identify key issues in network security and their remedies, describe basic network protocols and layers.
Database Systems
Database Systems - Theory	2	2
Database Systems - Theory Module: Database Systems Root module: Computer Systems Semester: 2 Course code: DBST2VO Contact hours per week: 2 ECTS: 2 Course Content: Semantic data modelling in relational contexts (EER diagrams, conceptual schemata) Foundations of relational database management systems (RDBMS) Relational query languages (SQL, SQL extensions) Transaction and error handling Non-Relational database concepts and applications (NoSQL: semi-structured data, schema-less data models, graph databases) Database systems for big data analysis In-memory and high–performance key-value-stores (Redis, SAP Hana) Course outcome: Upon completion of the module, students are able to: explain basic relational and non-relational database concepts, relate common database paradigms to applications in Data Science, semantically create data models based on real-world examples and visualize them via EER diagrams, install and administrate simple relational and other database systems (including virtualized instances), perform simple CRUD operations and basic queries in SQL as well as query key-value stores.
Database Systems - Exercise	2	3
Database Systems - Exercise Module: Database Systems Root module: Computer Systems Semester: 2 Course code: DBSE2UE Contact hours per week: 2 ECTS: 3 Course Content: Accompanying practical exercises to the lecture "Database Systems - Theory" Introduction to database administration for relational and other databases Exercises focusing on querying databases (joins, subqueries, generic where clauses)
Software Engineering and Data Modelling
Programming II	4	6
Programming II Module: Software Engineering and Data Modelling Root module: Software Engineering and Data Modelling Semester: 2 Course code: PROGII2ILV Contact hours per week: 4 ECTS: 6 Course Content: Advanced data abstraction concepts (objects, classes, instances, inheritance) Recursive data structures (lists, dictionaries, list comprehension) Basic design patterns (singleton, façade, decorators, context managers) Database access (ORMs, active record and data mapper) Exception handling Automated software testing (unit tests, fixtures, coverage) Pythonic programming (PEP8 style guide, code documentation) Basic backend development for web applications (Django) Course outcome: Upon completion of this course, students are able to: implement complex software projects in Python, utilize advanced data abstraction concepts in Python, follow basic paradigms of error handling, software testing and pattern utilization.
Design Methodology in Human Computer Interaction	2	3
Design Methodology in Human Computer Interaction Module: Software Engineering and Data Modelling Root module: Software Engineering and Data Modelling Semester: 2 Course code: DMHCI2ILV Contact hours per week: 2 ECTS: 3 Course Content: Practical Software-Project I (customer meeting, project concept) Introduction to user interface design HCI-Design paradigms (participatory design, user-centered design) Design tools and methodologies (prototyping, scenarios, mock-ups, user stories, technology probes) Foundations of User Modelling Course outcome: Upon completion of this course, students are able to: design and adapt interfaces, products and applications for, and in cooperation with end-users, apply design ideation and iterative user testing methodologies in software projects, relate paradigms and methodologies in HCI-Design to the curricular electives and their application.
Computer Science and Society
Reflections on Computer Science, Society and Ethics	1	1
Reflections on Computer Science, Society and Ethics Module: Computer Science and Society Root module: Computer Science and Society Semester: 2 Course code: RCSSE2TU Contact hours per week: 1 ECTS: 1 Course Content: subject areas related to the curriculum of the current semester are reviewed, critically analysed and discussed from ethical, cultural and sociological viewpoints by means of lectures and contributions from students Course outcome: Upon completion of this course, students are able to: critically reflect on and discuss ethical, social and cultural aspects of information technology as framed by interdisciplinary overlaps, manage and moderate a critical discussion of ethical questions connected with informatics, data and related responsibilities, and formulate possible courses of action based on this discussion.
Social Skills
Creative Thinking	2	2
Creative Thinking Module: Social Skills Root module: Social Skills Semester: 2 Course code: CT2WK Contact hours per week: 2 ECTS: 2 Course Content: Design thinking methods Creativity and problem-solving techniques and methods: Brainstorming, Mind mapping, customer journey mapping, personas, empathy cards and mock-ups Lateral thinking, Divergent and convergent thinking, Disney Method Course outcome: Upon completion of this course, students are able to: address specific questions/problems using various creative techniques, describe the creative process and present the solution process, develop a systematic approach to dealing with complex problems in line with user requirements and needs, and outline its application.

Semester 3

Course	SWS	ECTS
Mathematics, Statistics and Theoretical Computer Science
Advanced Statistical Methods for Data Science	2	4
Advanced Statistical Methods for Data Science Module: Mathematics, Statistics and Theoretical Computer Science Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 3 Course code: ASMDS3ILV Contact hours per week: 2 ECTS: 4
Algorithms and Data Structures II	2	3
Algorithms and Data Structures II Module: Mathematics, Statistics and Theoretical Computer Science Root module: Mathematics, Statistics and Theoretical Computer Science Semester: 3 Course code: ADSII3ILV Contact hours per week: 2 ECTS: 3 Course Content: Advanced theoretical aspects of algorithms (P vs. NP, NP-completeness) Advanced algorithmic paradigms (prune and search, parametrized complexity, kernelization) Algorithmic approximation, heuristics (TSP, evolutionary algorithms) Applications in Data Science (runtime optimization for large datasets) Course outcome: Upon completion of this course, students are able to: explain advanced algorithmic concepts such as NP-hardness, describe advanced algorithmic paradigms, judge algorithmic implementations based on their performance and applicability to the problem at hand, relate algorithms and their paradigms to their application in data science and the electives.
Computer Systems
Networking Technologies and Management Systems II	2	3
Networking Technologies and Management Systems II Module: Computer Systems Root module: Computer Systems Semester: 3 Course code: NTMGTII3ILV Contact hours per week: 2 ECTS: 3 Course Content: Wireless network transmission technologies and specifications (eg. GSM/LTE/3G/4G/5G, IEEE 802.11, Bluetooth, NB-IoT, mesh networks) Quality of Service (QoS), fault tolerance Introduction to unix commandline tools for systems and network adminstration Virtualisation technologies (Docker, Linux Containers) Course outcome: Upon completion of this course, students are able to: explain foundational concepts in mobile / wireless computer networks and technologies, identify key issues in network performance (QoS) and their remedies, troubleshoot computer network issues.
Operating Systems	2	3
Operating Systems Module: Computer Systems Root module: Computer Systems Semester: 3 Course code: OS3ILV Contact hours per week: 2 ECTS: 3 Course Content: History and basic components of operating systems OS architectures, abstractions Processes and threads (process states and management, multithreading) Memory management (de-/allocation, relocation, segmentation, paging, virtual memory management, page replacement, protection and sharing) Input-output and disk management (devices and device characteristics, I/O operations, drivers, buffering, disks, file systems, file system organization) OS networking concepts OS security (threats, security measures, access control, authentication, disk encryption) OS virtualization OS considerations for big data analysis (Windows/Unix comparisons, memory management for data science) Course outcome: Upon completion of this course, students are able to: explain fundamental concepts of operating systems, develop applications with regards to operating system considerations and limitations, evaluate different operating system choices regarding their suitability for data science applications.
Software Engineering and Data Modelling
Software Engineering and Project Management	4	6
Software Engineering and Project Management Module: Software Engineering and Data Modelling Root module: Software Engineering and Data Modelling Semester: 3 Course code: SWEPM3ILV Contact hours per week: 4 ECTS: 6 Course Content: Practical Software-Project II (technical implementation) Requirements analysis and engineering Software development process modelling & model comparison Scrum (focusing on values, introducing practices, roles & responsibilities) Working in iterations with quick to rapid feedback Version control with Git QA with unit, integration and functional tests Automation with CI/CD Preparation of IPMA certification Course outcome: Upon completion of this course, students are able to: explain and assess different project management methodologies, apply project management in in-class work in managed and self-organised software development teams in software projects.
Computer Science and Society
Process Management	2	3
Process Management Module: Computer Science and Society Root module: Computer Science and Society Semester: 3 Course code: PSM3ILV Contact hours per week: 2 ECTS: 3 Course Content: Definition of processes (management, business, support) Corporate value chain model Business process management lifecycle Business process mapping, re-engineering Modelling business processes using UML and corresponding tools Business Process Automation (SOA Reference Architecture) Introduction to popular business process management software suites Course outcome: Upon completion of this course, students are able to: discover and model business processes using elicitation techniques and modelling language and tools, identify bottlenecks and problems in business processes, improve, optimize and automate business processes, re-engineer business processes from scratch.
Computer Science and Law	2	3
Computer Science and Law Module: Computer Science and Society Root module: Computer Science and Society Semester: 3 Course code: CSL3VO Contact hours per week: 2 ECTS: 3 Course Content: Legal basics (e.g. states, national law, international law, EU laws, regulations) Foundational challenges of technology law (geographical applicability, structural challenges) Contract & liability law (EU General Data Protection Regulation) Copyright law (basic concepts, intellectual property, open source laws & licenses) Course outcome: Upon completion of this course, students are able to: explain basic legal terms and challenges related to information technologies and law, discuss practical, data-related matters linked to corporate and organisational settings from a legal perspective, identify possible steps and draw up recommendations for action.
Reflections on Critical Algorithm Studies
Reflections on Computer Science, Society and Ethics	1	1
Reflections on Computer Science, Society and Ethics Module: Reflections on Critical Algorithm Studies Root module: Reflections on Critical Algorithm Studies Semester: 3 Course code: RCSSEE3TU Contact hours per week: 1 ECTS: 1 Course Content: subject areas related to the curriculum of the current semester are reviewed, critically analysed and discussed from ethical, cultural and sociological viewpoints by means of lectures and contributions from students Course outcome: Upon completion of this course, students are able to: critically reflect on and discuss ethical, social and cultural aspects of information technology as framed by interdisciplinary overlaps, manage and moderate a critical discussion of ethical questions connected with informatics, data and related responsibilities, and formulate possible courses of action based on this discussion.
Critical Algorithm Studies	2	2
Critical Algorithm Studies Module: Reflections on Critical Algorithm Studies Root module: Reflections on Critical Algorithm Studies Semester: 3 Course code: CAS3SE Contact hours per week: 2 ECTS: 2 Course Content: Critical reflection and analysis of algorithmic systems Algorithmic bias and discrimination Algorithmic transparency and accountability Computer Science Culture (impact, influences and re-production) Critical Data Studies Auditing algorithms Course outcome: Upon completion of this course, students are able to: critically reflect on ethical challenges related to the design, production, implementation and impact of algorithmic systems, identify problematic aspects of algorithmic decision making, machine learning and data science, formulate strategies to combat inequalities perpetuated by algorithmic systems.
Research and Scientific Working
Scientific Skills and Writing	2	2
Scientific Skills and Writing Module: Research and Scientific Working Root module: Research and Scientific Working Semester: 3 Course code: SSW3PS Contact hours per week: 2 ECTS: 2 Course Content: Fundamentals of academic research (academic quality criteria, everyday and scientific knowledge, types of academic statement, types of academic paper, criteria for evaluating academic research) Formulating arguments in academic papers Literature research, evaluation and appraisal Process of compiling an academic paper Course outcome: apply the principles of academic research in the preparation of a seminar paper and address research questions using scientific literature.

Semester 4

Course	SWS	ECTS
Computer Systems
Distributed Systems
Distributed Systems - Theory	2	3
Distributed Systems - Theory Module: Distributed Systems Root module: Computer Systems Semester: 4 Course code: DST4VO Contact hours per week: 2 ECTS: 3 Course Content: Distributed processes and communication technologies (sockets, RPC/RMI) Principles of naming and discovery Fault tolerance, process resilience, consistency and replication Synchronization (clocks, fundamental algorithms for synchronization) Security in distributed systems Distributed Applications (peer-to-peer networking, SaaS, cloud computing, distributed scientific computing, Google App Engine, Amazon EC2, Apache Hadoop) Course outcome: Upon completion of the module, students are able to: describe foundational concepts and challenges of distributed systems, evaluate software paradigms and strategies for distributed applications, assess the suitability of cloud computing services for given distributed applications, conceptualize, design and implement distributed software applications, integrate and deploy a distributed application with a cloud computing platform (Google App Engine, Amazon EC2, Apache Hadoop), evaluate and improve distributed solutions in regards to security and performance.
Distributed Systems - Exercise	2	3
Distributed Systems - Exercise Module: Distributed Systems Root module: Computer Systems Semester: 4 Course code: DSE4UE Contact hours per week: 2 ECTS: 3 Course Content: Accompanying practical exercises to the lecture "Distributed Systems - Theory" Practical programming exercises for distributed applications and environments
IT Security
Risk Management	2	3
Risk Management Module: IT Security Root module: IT Security Semester: 4 Course code: ITRM4VO Contact hours per week: 2 ECTS: 3 Course Content: Definition of Risks Risk assessment methodologies Quality assurance Risk management and compliance Course outcome: Upon completion of this course, students are able to: identify key characteristics of software development projects and describe their relation to risk assessment, perform basic risk assessment for software development tasks and projects.
Cybersecurity and Data Protection	3	4
Cybersecurity and Data Protection Module: IT Security Root module: IT Security Semester: 4 Course code: CSDP4VO Contact hours per week: 3 ECTS: 4 Course Content: Security risks & threats (risk analysis, attack vectors and methodologies) Security domains Operating system and network security Organizational and operational security Security in software projects and application development Access control paradigms (authentication, authorization) Security enhancing technologies and countermeasures (cryptography, public/private key encryption, firewall systems) Security audits and penetration testing Information security guidelines, governance & protocols Course outcome: Upon completion of this course, students are able to: explain common security risks, threats and countermeasures, perform risk assessment for software projects, apply countermeasures to improve domain security.
Data Science and Emerging Technologies
From Data Capturing to Analysis and Interpretation
Data Mining, Acquisition and Preparation	2	3
Data Mining, Acquisition and Preparation Module: From Data Capturing to Analysis and Interpretation Root module: Data Science and Emerging Technologies Semester: 4 Course code: DMAP4ILV Contact hours per week: 2 ECTS: 3 Course Content: Data acquisition methods Survey techniques and crowdsourcing Scraping Distributed sensor networks Selective data acquisition Public domain datasets Multi-origin data sources and data merging Data preparation Data requirements specifications and modelling Explorative data analysis (EDA) Data integrity and quality estimation Feature engineering Course outcome: Upon completion of this course, students are able to: define data requirements based on an application problem statement, apply different data acquisition methods, explore and prepare collected datasets using EDA, quality estimations, feature engineering and text analysis methods.
Machine Learning, Artificial Intelligence and Big Data Analytics	3	4
Machine Learning, Artificial Intelligence and Big Data Analytics Module: From Data Capturing to Analysis and Interpretation Root module: Data Science and Emerging Technologies Semester: 4 Course code: MLAIBD4ILV Contact hours per week: 3 ECTS: 4 Course Content: Machine Learning paradigms and techniques Supervised learning (classification, regression) Unsupervised learning (clustering, association) Semi-supervised learning (deep learning & neural networks, advanced dimensionality reduction methods, reinforcement learning, meta learning, ensemble learning) Distributed data processing technologies / cluster environments (Microsoft AzureML, Apache Hadoop & Spark) Case studies of ML applications Image classification Recommender systems Purchase pattern analysis Course outcome: Upon completion of this course, students are able to: explain supervised and unsupervised machine learning methods, implement machine learning projects with current and emerging distributed platforms and environments, assess the suitability of different machine learning, AI and big data analytics techniques for given problem statements.
Data Visualization, Presentation and Real-time Integration of Digital Products	2	3
Data Visualization, Presentation and Real-time Integration of Digital Products Module: From Data Capturing to Analysis and Interpretation Root module: Data Science and Emerging Technologies Semester: 4 Course code: DVIS4ILV Contact hours per week: 2 ECTS: 3 Course Content: Foundational concepts of data visualization (human cognition & perception, color theory, morphology) Visualization as a means to exploration, confirmation and presentation Visualization tools and techniques (plots, charts, graphs, diagrams, alignment, scaling, order) Interactive visualization design (visual data exploration) Real-Time visualizations Visualization software libraries and tools (Tableau, Highcharts) Course outcome: Upon completion of this course, students are able to: describe foundational concepts of data visualization, their application domains and use cases, visualize a variety of datasets for data science and machine learning applications, design and implement interactive / real-time visualizations.
Data Science Capstone Project	3	4
Data Science Capstone Project Module: From Data Capturing to Analysis and Interpretation Root module: Data Science and Emerging Technologies Semester: 4 Course code: DSCP4WK Contact hours per week: 3 ECTS: 4 Course Content: Conceptualize. and implement a data science project Define and evaluate measures of success Present the project outcomes to peers Course outcome: Upon completion of this course, students are able to: apply learned techniques, technologies and methodologies in a practical context, document progress and identify obstacles throughout the implementation process, evaluate a project’s success based on self-determined criteria, present the project outcomes to their peers.
Computer Science and Society
Reflections on Computer Science, Society and Ethics	1	1
Reflections on Computer Science, Society and Ethics Module: Computer Science and Society Root module: Computer Science and Society Semester: 4 Course code: RCSSE4TU Contact hours per week: 1 ECTS: 1 Course Content: subject areas related to the curriculum of the current semester are reviewed, critically analysed and discussed from ethical, cultural and sociological viewpoints by means of lectures and contributions from students Course outcome: Upon completion of this course, students are able to: critically reflect on and discuss ethical, social and cultural aspects of information technology as framed by interdisciplinary overlaps, manage and moderate a critical discussion of ethical questions connected with informatics, data and related responsibilities, and formulate possible courses of action based on this discussion.
Research and Scientific Working
Bachelor Exposé Preparation	1	2
Bachelor Exposé Preparation Module: Research and Scientific Working Root module: Research and Scientific Working Semester: 4 Course code: BP4PS Contact hours per week: 1 ECTS: 2 Course Content: Approaches to and possible applications for research methods Preparing a proposal for an academic paper in engineering sciences Course outcome: Upon completion of this course, students are able to: independently apply the principles of academic research in the preparation of an extended proposal and the subsequent bachelor paper. The course builds on the content of Scientific Skills and Writing, with a focus on practical application.

Semester 5

Course	SWS	ECTS
Practical Training Semester
Practical Training Semester (22 weeks à 30 hours)	0	28
Practical Training Semester (22 weeks à 30 hours) Module: Practical Training Semester Root module: Practical Training Semester Semester: 5 Course code: PTS5BOPR Contact hours per week: 0 ECTS: 28 Course Content: Practical application of theory in the professional field
Practical Training Coaching Seminar	1	2
Practical Training Coaching Seminar Module: Practical Training Semester Root module: Practical Training Semester Semester: 5 Course code: PTCS5SE Contact hours per week: 1 ECTS: 2 Course Content: Supervision during the practical training through coordinator Preparing progress reports Preparing final reports Critical reflection on the practical training

Semester 6

Course	SWS	ECTS
Elective 1: Business Process and Enterprise Technologies
Integrated Value Flows	2	3
Integrated Value Flows Module: Elective 1: Business Process and Enterprise Technologies Root module: Elective 1: Business Process and Enterprise Technologies Semester: 6 Course code: IVF6ILV Contact hours per week: 2 ECTS: 3 Course Content: Based on general business administration and operational processes Value added and processes in ERP systems (purchasing, financial and cost accounting, production, distribution, logistics, cash management, taxes) Value chain / supply chain modelling and management Supply chain operations reference model (SCOR) Optional SAP Certification Course outcome: Upon completion of this course, students are able to: relate general concepts related to value flows of ERP to their implementation in SAP, describe the value flows in ERP systems and within the supply chain in SAP, use SAP main modules (material management, finance, costs), evaluate and optimize value flows by implementing SAP general ledger in a real life example.
Business Application Integration	3	4
Business Application Integration Module: Elective 1: Business Process and Enterprise Technologies Root module: Elective 1: Business Process and Enterprise Technologies Semester: 6 Course code: BAI6VO Contact hours per week: 3 ECTS: 4 Course Content: Business application modelling Initiating and carrying out business processes Expense distribution & costing sheets review Information silos vs. integrated enterprise applications Integrating different applications (CRM, ERP, SCM, HR, BI, IoT and Customer Insights) using APIs and Middleware Mediation and federation patterns, topologies Adapters/connectors Optional SAP Certification Course outcome: Upon completion of this course, students are able to: explain the concepts, challenges and technologies related to the integration of enterprise applications, utilize software development patterns and basic interfacing technologies to integrate business applications within ERP systems, collect and assess key performance indicators for given interfacing implementations.
ERP-Consulting
Customizing	3	6
Customizing Module: ERP-Consulting Root module: Elective 1: Business Process and Enterprise Technologies Semester: 6 Course code: IVF6ILV Contact hours per week: 3 ECTS: 6 Course Content: Practical project work in ERP-Consulting Adaptation and extension of existing modules, applications and implementations of an ERP system in JavaScript for a company / organization Review of Enterprise Architecture (EA) and Integration (EAI) Course outcome: Upon completion of this course, students are able to: to conceptualize, design, implement and evaluate business processses within SAP in a practical project and explain their project to peers.
Data Transformation and Data Warehousing	3	3
Data Transformation and Data Warehousing Module: ERP-Consulting Root module: Elective 1: Business Process and Enterprise Technologies Semester: 6 Course code: DTDW6ILV Contact hours per week: 3 ECTS: 3 Course Content: Practical project work in ERP-Consulting Data standardization and clearing Data mining for business applications Data transformation and storage technologies (ETL/extract-transform-load, schema on read/write, OLAP, OLTP) Enterprise Data Hubs (EDH) & Data Lakes (Hadoop) Course outcome: Upon completion of this course, students are able to: explain the concepts, challenges and technologies related to the transformation and storage of enterprise data, assess data warehousing technologies and approaches, implement data extraction, transformation and clearing processes with ERP systems in a practical project.
Elective 2: Bio Informatics
Biological Foundations of Bio Informatics	2	3
Biological Foundations of Bio Informatics Module: Elective 2: Bio Informatics Root module: Elective 2: Bio Informatics Semester: 6 Course code: BFBI6VO Contact hours per week: 2 ECTS: 3 Course Content: Basics of general and organic chemistry Nucleic acids and proteins Genes and genomes Basics of cell biochemistry and bioenergetics Experimental techniques which generate big data for bioinformatics analysis: DNA microarrays, DNA sequencing, RNA sequencing, proteomics, metabolomics Course outcome: Upon completion of this course, students are able to: explain the structure and function of bioinformatics-relevant biomolecules, explain the bioinformatics-relevant topics related to genes and genomes, discuss the crucial technologies for generating bioinformatics-relevant data, explain foundational concepts of physics, biology and chemistry.
Algorithms and Tools in Bioinformatics	2	3
Algorithms and Tools in Bioinformatics Module: Elective 2: Bio Informatics Root module: Elective 2: Bio Informatics Semester: 6 Course code: ATBI6ILV Contact hours per week: 2 ECTS: 3 Course Content: Algorithms and data structures in Bioinformatics: Sequence alignment algorithms Data structures in bioinformatics Suffix trees Gene expression profiling Gene prediction Data, tools and technologies in Bioinformatics: Standard data sets and –sources Introduction to software tools for bioinformatics Gene Prediction: ORF Finder, GeneScan Gene Expression Databases: GEO, ArrayExpress Course outcome: Upon completion of this course, students are able to: describe foundational problems in Bioinformatics and relate them to their respective algorithmic techniques, select, curate and utilize standard datasets and resources, apply machine learning methods, tools and algorithms in the context of bio-informatics.
Big Data for Bioanalytics and Medicine	4	6
Big Data for Bioanalytics and Medicine Module: Elective 2: Bio Informatics Root module: Elective 2: Bio Informatics Semester: 6 Course code: BDBM6ILV Contact hours per week: 4 ECTS: 6 Course Content: Practical project work in Bio-Informatics Implement big data analysis projects based on provided raw data From data curation and preparation to analysis, interpretation and visualization Themes / topics: Next-Generation Sequencing Analysis RNA Sequencing Analysis Microarray / DNA Chip Analysis Analysis of Proteomics Data Personalized / predictive biomarkers Course outcome: Upon completion of this course, students are able to: prepare and transform raw datasets from the domain of bioinformatics, analyse and interpret bioinformatics data through data science methods, visualize the results of data analysis to support interpretation, apply the learned work steps in a project and present the project results.
Image Processing and Visual Computing in Biology and Medicine
Image Processing and Visual Computing in Biology and Medicine - Theory	2	2
Image Processing and Visual Computing in Biology and Medicine - Theory Module: Image Processing and Visual Computing in Biology and Medicine Root module: Elective 2: Bio Informatics Semester: 6 Course code: IPBMT6VO Contact hours per week: 2 ECTS: 2 Course Content: Introduction to visual computing Foundations of computer graphics & vision Digital image processing (image metrics, formats, sampling, filtering in spatial and frequency domains, compression) Foundations of augmented and virtual reality Imaging techniques in Biology and Medicine Image segmentation (active contours, level-set) Model-based processing of anatomical data Texture analysis Machine learning in neuroimaging (analysis, classification) Course outcome: Upon completion of this course, students are able to: explain foundational concepts of visual computing, apply imaging techniques to visual datasets and utilize data science approaches to analyse them, relate the learned techniques and methodologies to practical problems in Bioinformatics. perform visual data preparation and processing in the domains of Biology and Medicine, apply data science techniques to analyse medical image data.
Image Processing and Visual Computing in Biology and Medicine - Exercise	1	2
Image Processing and Visual Computing in Biology and Medicine - Exercise Module: Image Processing and Visual Computing in Biology and Medicine Root module: Elective 2: Bio Informatics Semester: 6 Course code: IPBME6UE Contact hours per week: 1 ECTS: 2 Course Content: Accompanying practical exercises to the lecture “Image Processing and Visual Computing in Biology and Medicine - Theory” Practical exercises in medical image processing and analysis
Computer Science and Society
Reflections on Computer Science, Society and Ethics	1	1
Reflections on Computer Science, Society and Ethics Module: Computer Science and Society Root module: Computer Science and Society Semester: 6 Course code: RCSSE6TU Contact hours per week: 1 ECTS: 1 Course Content: subject areas related to the curriculum of the current semester are reviewed, critically analysed and discussed from ethical, cultural and sociological viewpoints by means of lectures and contributions from students Course outcome: Upon completion of this course, students are able to: critically reflect on and discuss ethical, social and cultural aspects of information technology as framed by interdisciplinary overlaps, manage and moderate a critical discussion of ethical questions connected with informatics, data and related responsibilities, and formulate possible courses of action based on this discussion.
Data Science and Emerging Technologies
Current Trends and Emerging Technologies	2	2
Current Trends and Emerging Technologies Module: Data Science and Emerging Technologies Root module: Data Science and Emerging Technologies Semester: 6 Course code: CTET6TU Contact hours per week: 2 ECTS: 2 Course Content: State-of-the-art outlook in emerging technology and new fields of application Examples of current trends in computer science: such as Blockchain applications and cryptocurrencies Social media analysis Autonomous computing Augmented Analytics Digital Ethics and Privacy Course outcome: Upon completion of this course, students are able to: explain, analyze and critically discuss the technological trends that inform applications of computer and data science, discuss how companies and organizations can gain important insights and which new fields of applications can evolve with the help of new technologies and what opportunities and risks are involved.
Social Skills
Communication and Presentation Skills	1	1
Communication and Presentation Skills Module: Social Skills Root module: Social Skills Semester: 6 Course code: CPS6WK Contact hours per week: 1 ECTS: 1 Course Content: Verbal and non-verbal communication Seven phases of presentations Effective use of audio-visual media Handling difficult situations Storytelling Course outcome: Upon completion of this course, students are able to: give a presentation for a particular target group, using a range of methods and media.
Research and Scientific Working
Bachelor Seminar and Bachelor Paper	1	8
Bachelor Seminar and Bachelor Paper Module: Research and Scientific Working Root module: Research and Scientific Working Semester: 6 Course code: BPA6BASE Contact hours per week: 1 ECTS: 8 Course Content: Writing the bachelor paper, including project planning Topic-focused literature research Applying content-related and formal academic requirements Preparing the bachelor paper on the basis of the proposal Course outcome: Upon completion of this course, students are able to: independently write an academic paper, in accordance with content-related and formal academic requirements
Bachelor Exam	0	2
Bachelor Exam Module: Research and Scientific Working Root module: Research and Scientific Working Semester: 6 Course code: BEX6AP Contact hours per week: 0 ECTS: 2 Course Content: Presentation of bachelor paper (and project ) Oral examination on the bachelor paper (in accordance with section 16 Fachhochschul-Studiengesetz [University of Applied Sciences Studies Act]), and the links to related subjects on the curriculum (in accordance with section 16 University of Applied Sciences Studies Act) Course outcome: In the bachelor exam, students demonstrate their ability to: present their bachelor paper and the question they addressed in a manner appropriate to the target audience, and defend the paper before an expert committee, outline the significance of the findings for professional practice and research, and present supporting arguments and answer follow-up questions on degree-programme subjects and the links between them, and justify their answers

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Der Vollzeit-Bachelor-Studiengang Unternehmensführung bietet eine ausgewogene Mischung aus BWL und neuen IT-Technologien für Nachwuchsführungskräfte.

Informatics bachelor degree programme

Would you like to play a creative role in shaping the ways in which we will live and work in the future? Are you also interested in technology-related fields? Then our Informatics programme is the perfect choice for you!

Full-Time

English

6 semester

22 weeks

EUR 363.36

The degree programme: Bachelor of Science in Engineering (BSc)

Informatics

Those who can imagine anything, can create the impossible.

Pioneering computer scientist Alan Turing

Curriculum

Semester 1

Mathematics I - Theory

Mathematics I - Exercise

Statistics and Probability in Computer Science - Theory

Statistics and Probability in Computer Science - Exercise

Web Technologies

Programming I

Introduction: Applied Informatics

General Business Administration

Intercultural Competences

Semester 2

Mathematics II

Theoretical Computer Science and Logic

Algorithms and Data Structures I

Technical Foundations of Computer Science

Networking Technologies and Management Systems I

Database Systems - Theory

Database Systems - Exercise

Programming II

Design Methodology in Human Computer Interaction

Reflections on Computer Science, Society and Ethics

Creative Thinking

Semester 3

Advanced Statistical Methods for Data Science

Algorithms and Data Structures II

Networking Technologies and Management Systems II

Operating Systems

Software Engineering and Project Management

Process Management

Computer Science and Law

Reflections on Computer Science, Society and Ethics

Critical Algorithm Studies

Scientific Skills and Writing

Semester 4

Distributed Systems - Theory

Distributed Systems - Exercise

Risk Management

Cybersecurity and Data Protection

Data Mining, Acquisition and Preparation

Machine Learning, Artificial Intelligence and Big Data Analytics

Data Visualization, Presentation and Real-time Integration of Digital Products

Data Science Capstone Project

Reflections on Computer Science, Society and Ethics

Bachelor Exposé Preparation

Semester 5

Practical Training Semester (22 weeks à 30 hours)

Practical Training Coaching Seminar

Semester 6

Integrated Value Flows

Business Application Integration

Customizing

Data Transformation and Data Warehousing

Biological Foundations of Bio Informatics

Algorithms and Tools in Bioinformatics

Big Data for Bioanalytics and Medicine

Image Processing and Visual Computing in Biology and Medicine - Theory

Image Processing and Visual Computing in Biology and Medicine - Exercise

Reflections on Computer Science, Society and Ethics

Current Trends and Emerging Technologies

Communication and Presentation Skills

Bachelor Seminar and Bachelor Paper

Bachelor Exam

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