The field of study aims at generating and making available knowledge gained from data and information. Graduates work in companies that depend heavily on the generation of business-relevant knowledge, e.g. from web data, e.g. digital information platforms, online merchants, social networks, online media, etc. Also a use in research or in the facilities of research infrastructures (e.g. scientific libraries, Leibniz institutes, etc.) is conceivable due to the high proportion of research-centered modules.
Information is the basis for decisions and processes in science, business and politics. One speaks here of the information needs that a person or an organization has. The lack of required information and the use of incorrect information can have serious consequences. Therefore, students learn methods and procedures of information analysis, information preparation, information retrieval, and information system design technologies with the goal of improving the availability of information and satisfying information needs.
Traditionally, information science has been concerned with how to organize information and make it usable. As a result of information technology development, information science has become a digitized, data-driven discipline. Algorithmic methods of analyzing mass data complement or completely replace traditional approaches. Techniques of text, data and web mining or knowledge discovery are representative for this development. In addition to the processes and technical methods, the human being is of particular interest here, since data, information and knowledge first develop their actual value through the application context and the integration into human information behavior.
Students are taught extensive information science knowledge (Core Information Science), which they combine with the tools of Data Science and Computer Science to enable them to work in a scientifically sound manner and to act responsibly in their professional activities, as well as to conduct independent research in the broader field of Data and Information Science. Through the targeted acquisition of competencies, they are enabled to identify, formulate and solve technical problems using scientific methods. You will be able to apply knowledge and develop solutions to problems in the fields of Data and Information Science. In doing so, they demonstrate a high level of competence and are guided by ethical thinking and action. Students in the DIS field of study are enabled to develop innovative contributions and solutions to priority future tasks and to help shape, drive and improve social innovations (including in the areas of digital economy, innovative working world, mobility, energy and environment). Students learn to process and analyze data, to order and prioritize information, to recognize patterns and to work out relevant connections and conclusions. They learn to organize and independently carry out scientific as well as economic projects, working both individually and as members of interdisciplinary project groups. Last but not least, DIS students are enabled to take up a professional activity in a wide range of industries ( employability), but equally to gain an aptitude for scientific specialization through a doctorate.
Below, you find a number of alumni profiles with their specific example curricula.
Graduates of this profile have a sound knowledge of data science and business intelligence as well as knowledge of companies and markets. They work in companies and support management in making decisions under uncertainty by implementing interactive dashboards, reporting and complex analyses of company-relevant data.
The creation of individual analyses and forecasts about the business (segment) development is also an essential part of the competence of these graduates, which, supplemented by architectural knowledge about large used data warehouses and reporting infrastructures, is particularly valuable for later employers.
The high proportion of company-related analyses and business decisions also makes it possible for them to work in consulting (both in-house and externally). Strengthening the competence to present the findings to the target group is achieved, among other things, through data visualization and project management with high application relevance.
The subsequent table contains an example curriculum for this alumnus profile.
Customize this example curriculum to fit your own needs, using our Study Planner! The Study Planner is an interactive tool that you can use to plan the modules you want to attend.
| Mapping to Focus Areas | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Term | Acronym | Module | ECTS | AR | ACS | DIP | EB | GAK | MRI |
| 1. Semester (WS) | PMI | Process Mining | 6 | 1 | 0 | 0 | 2 | 3 | 0 |
| ABIA | Advanced Business Intelligence and Analytics | 6 | 0 | 0 | 0 | 2 | 4 | 0 | |
| AML | Advanced Machine Learning | 6 | 1 | 2 | 0 | 0 | 3 | 0 | |
| OR | Operations Research | 6 | 0 | 0 | 1 | 1 | 4 | 0 | |
| SGM | Spezielle Gebiete der Mathematik | 6 | 0 | 0 | 0 | 0 | 6 | 0 | |
| Subtotal | 30 | 2 | 2 | 1 | 5 | 20 | 0 | ||
| 2. Semester (SS) | DVI | Data Visualization | 3 | 0 | 0 | 0 | 0 | 3 | 0 |
| GP-GAK | Guided Project focused on Generating and Accessing Knowledge | 12 | 0 | 2 | 2 | 2 | 4 | 2 | |
| SKD | Seminar Knowledge Discovery | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| EAM | Enterprise Architecture Management | 6 | 0 | 0 | 0 | 3 | 0 | 3 | |
| PM | Projekt Management | 6 | 5 | 1 | 0 | 0 | 0 | 0 | |
| Subtotal | 30 | 5 | 3 | 2 | 5 | 10 | 5 | ||
| 3. Semester (WS) | MA | Masterarbeit mit Kolloquium / Master Thesis with Colloquium | 30 | Contribution depends on topic | |||||
| Total | 90 | 7 | 5 | 3 | 10 | 30 | 5 | ||
Graduates of this profile have in-depth knowledge in the field of Data and Information Science. Their field of application is primarily in scientific libraries, research institutions or infrastructures, or R&D departments in companies that want to develop and offer services to enable research on digital datasets.
This may include, for example, methods for data indexing and data enrichment, procedures for searching digital datasets, and selection and provision of tools for data analysis and visualization. Accordingly, the focus here is on methods of data analysis, retrieval and visualization, as well as the responsible handling of data. A guided project with a high level of application relevance is also planned, in which results are prepared and presented in a way that is appropriate for the target group.
The subsequent table contains an example curriculum for this alumnus profile.
Customize this example curriculum to fit your own needs, using our Study Planner! The Study Planner is an interactive tool that you can use to plan the modules you want to attend.
| Mapping to Focus Areas | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Term | Acronym | Module | ECTS | AR | ACS | DIP | EB | GAK | MRI |
| 1. Semester (WS) | AML | Advanced Machine Learning | 6 | 1 | 2 | 0 | 0 | 3 | 0 |
| LOD | Linked-Open Data and Knowledge Graphs | 6 | 1 | 0 | 1 | 0 | 4 | 0 | |
| OSC | Open Science | 6 | 0 | 0 | 0 | 0 | 6 | 0 | |
| RSN | Recherche in (sozialen) Netzwerken / Research in (social) networks | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| WIR | Web Information Retrieval | 6 | 0 | 1 | 0 | 0 | 5 | 0 | |
| ANLP | Advanced Natural Language Processing | 3 | 0 | 1 | 0 | 0 | 2 | 0 | |
| Subtotal | 30 | 2 | 4 | 1 | 0 | 23 | 0 | ||
| 2. Semester (SS) | MVS | Multivariate Statistik | 6 | 0 | 0 | 0 | 0 | 6 | 0 |
| SKD | Seminar Knowledge Discovery | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| DSE | Data Science and Ethics | 6 | 2 | 1 | 1 | 0 | 2 | 0 | |
| DVI | Data Visualization | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| GP-GAK | Guided Project focused on Generating and Accessing Knowledge | 12 | 0 | 2 | 2 | 2 | 4 | 2 | |
| Subtotal | 30 | 2 | 3 | 3 | 2 | 18 | 2 | ||
| 3. Semester (WS) | MA | Masterarbeit mit Kolloquium / Master Thesis with Colloquium | 30 | Contribution depends on topic | |||||
| Total | 90 | 4 | 7 | 4 | 2 | 41 | 2 | ||
Graduates of this profile have in-depth knowledge in the field of Data and Information Science. Their field of application is primarily in scientific libraries, research institutions or infrastructures, or R&D departments in companies that want to develop and offer services to enable research on digital datasets.
This may include, for example, methods for data indexing and data enrichment, procedures for searching digital datasets, and selection and provision of tools for data analysis and visualization. Accordingly, the focus here is on methods of data analysis, retrieval and visualization, as well as the responsible handling of data. A guided project with a high level of application relevance is also planned, in which results are prepared and presented in a way that is appropriate for the target group.
The subsequent table contains an example curriculum for this alumnus profile.
Customize this example curriculum to fit your own needs, using our Study Planner! The Study Planner is an interactive tool that you can use to plan the modules you want to attend.
| Mapping to Focus Areas | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Term | Acronym | Module | ECTS | AR | ACS | DIP | EB | GAK | MRI |
| 1. Semester (WS) | AML | Advanced Machine Learning | 6 | 1 | 2 | 0 | 0 | 3 | 0 |
| LOD | Linked-Open Data and Knowledge Graphs | 6 | 1 | 0 | 1 | 0 | 4 | 0 | |
| OSC | Open Science | 6 | 0 | 0 | 0 | 0 | 6 | 0 | |
| RSN | Recherche in (sozialen) Netzwerken / Research in (social) networks | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| WIR | Web Information Retrieval | 6 | 0 | 1 | 0 | 0 | 5 | 0 | |
| ANLP | Advanced Natural Language Processing | 3 | 0 | 1 | 0 | 0 | 2 | 0 | |
| Subtotal | 30 | 2 | 4 | 1 | 0 | 23 | 0 | ||
| 2. Semester (SS) | MVS | Multivariate Statistik | 6 | 0 | 0 | 0 | 0 | 6 | 0 |
| SKD | Seminar Knowledge Discovery | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| DSE | Data Science and Ethics | 6 | 2 | 1 | 1 | 0 | 2 | 0 | |
| DVI | Data Visualization | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| ITSTR | IT Strategy | 6 | 0 | 0 | 2 | 0 | 0 | 4 | |
| MODI | Mobile and Distributed Systems | 6 | 0 | 4 | 1 | 0 | 0 | 1 | |
| Subtotal | 30 | 2 | 5 | 4 | 0 | 14 | 5 | ||
| 3. Semester (WS) | INM | Innovation Management | 6 | 1 | 0 | 4 | 1 | 0 | 0 |
| DDM | Data Driven Modelling | 6 | 1 | 2 | 0 | 1 | 2 | 0 | |
| GP-GAK | Guided Project focused on Generating and Accessing Knowledge | 12 | 0 | 2 | 2 | 2 | 4 | 2 | |
| GP-ID | Guided Project (small), focused on Interdisciplinary Topics | 6 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Subtotal | 30 | 3 | 5 | 7 | 5 | 7 | 3 | ||
| 4. Semester (SS) | MA | Masterarbeit mit Kolloquium / Master Thesis with Colloquium | 30 | Contribution depends on topic | |||||
| Total | 120 | 7 | 14 | 12 | 5 | 44 | 8 | ||
Graduates of this profile already have a sound basic knowledge of data and information science. They work in companies that rely heavily on the generation of business-relevant knowledge from web data, e.g. digital information platforms, online retailers, social networks, online media, etc. Also, a use in research or in the facilities of research infrastructures (e.g. scientific libraries, Leibniz institutes, etc.) is conceivable due to the high proportion of research-related modules.
Extraction and accessibility of data using techniques of NLP and IR are the focus here and are complemented by means of process mining. Strengthening the competence to present the findings to the target group is made possible, among other things, by Data Visualization and by a Guided Project with high application relevance.
The subsequent table contains an example curriculum for this alumnus profile.
Customize this example curriculum to fit your own needs, using our Study Planner! The Study Planner is an interactive tool that you can use to plan the modules you want to attend.
| Mapping to Focus Areas | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Term | Acronym | Module | ECTS | AR | ACS | DIP | EB | GAK | MRI |
| 1. Semester (SS) | PM | Projekt Management | 6 | 5 | 1 | 0 | 0 | 0 | 0 |
| DSE | Data Science and Ethics | 6 | 2 | 1 | 1 | 0 | 2 | 0 | |
| DVI | Data Visualization | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| MVS | Multivariate Statistik | 6 | 0 | 0 | 0 | 0 | 6 | 0 | |
| SKD | Seminar Knowledge Discovery | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| WEB | Web Technologies | 6 | 1 | 5 | 0 | 0 | 0 | 0 | |
| Subtotal | 30 | 8 | 7 | 1 | 0 | 14 | 0 | ||
| 2. Semester (WS) | ANLP | Advanced Natural Language Processing | 3 | 0 | 1 | 0 | 0 | 2 | 0 |
| GP-GAK | Guided Project focused on Generating and Accessing Knowledge | 12 | 0 | 2 | 2 | 2 | 4 | 2 | |
| LOD | Linked-Open Data and Knowledge Graphs | 6 | 1 | 0 | 1 | 0 | 4 | 0 | |
| WAM | Web Audience Measurement und Web-Analytics | 3 | 0 | 0 | 0 | 0 | 3 | 0 | |
| WIR | Web Information Retrieval | 6 | 0 | 1 | 0 | 0 | 5 | 0 | |
| Subtotal | 30 | 1 | 4 | 3 | 2 | 18 | 2 | ||
| 3. Semester (SS) | MA | Masterarbeit mit Kolloquium / Master Thesis with Colloquium | 30 | Contribution depends on topic | |||||
| Total | 90 | 9 | 11 | 4 | 2 | 32 | 2 | ||
