Data science

The following modules should be completed as a basis for advanced modules, if they (or equivalent modules) have not been completed in a previous bachelor’s programme:

• Introduction to machine learning
• Data mining: Basic concepts

Additionally, other basic modules fit to this specialization and we recommend completing some of them, if they (or equivalent modules) have not been completed in a previous bachelor’s programme. The recommended basic modules include:

• Data visualization: Basic concepts
• Document analysis: Computational methods
• Algorithm engineering
• Big data management and analysis
• Multimedia retrieval: Basic concepts
• Geografic information systems
• Network visualisation
• Applications for Powerwall and virtual reality environments

Please see ZEuS for more details and the courses that are offered in the current or upcoming semester.

As the exam regulations specify, you need to complete at least three advanced modules in one area to be able to have a specification stated on your examination certificate. For the specialization in “Data science”, a range of advanced modules are offered. These include:

• Data visualization: Advanced topics
• Graph data management and analysis
• Applied visual analytics
• Word representations and language models
• Optimization for data science

Please see ZEuS for more details and the offers of the current or upcoming semester.

The following seminars fit to the specialization "Data Science":

• Seminar Data Analysis and Visualization
• Seminar Database and Information Systems
• Seminar Data and Information Mining
• Seminar Machine Learning and Optimization

Please see ZEuS for more details and the courses that are offered in the current or upcoming semester.

The following projects fit to the specialization "Data Science":

• Master's project Visual Exploration of Large Data
• Master's project Information Visualization and Virtual Reality Environments
• Master's project Database and Information Systems
• Master's project Data and Information Mining
• Master's project Machine Learning and Optimization
• Master's project Multimedia Signal Processing

Data science has a great variety of application areas, as can be seen by our Excellence Cluster “Collective Behaviour”, for example.

The following courses from other departments  provide you with an insight into these application areas:

• from the Department of Biologie, e.g.: Evolution, behaviour (Evolution, Verhalten, taught in German)
• from the Department of Linguistics, e.g., Structure and history of English, Finite state morphology, Grammar development
• from the Department of Psychology: Social psychology (Sozialpsychologie, taught in German)

For further suitable courses from other departments and key qualifications, see the general list provided by the department or contact your mentor.

Upon completing the Data Science program at the University of Konstanz, students will be well-equipped with a robust set of skills and knowledge that position them at the forefront of data science innovation. The curriculum is designed to offer an immersive learning experience, integrating many practical elements and will enable graduates to navigate the evolving landscape of data science, driving innovations and solutions in diverse industries and research areas.

• Foundational Data Science Knowledge: Students will be adept at fundamental concepts such as data mining, data visualization, machine learning, and algorithm engineering. This ensures a solid theoretical foundation to understand and work with both current and future developments in the field.
• Data Management and Analysis Techniques: You understand the foundational methods and tools essential for managing large datasets and conducting insightful data analyses. Your proficiency in data management ensures efficient storage and retrieval mechanisms.
• Machine Learning and AI Integration: You are adept at the principles of machine learning and can implement AI-driven algorithms, understanding their intricacies and the relevant applications, from predictive analytics, natural language processing, to Large Language Models (LLMs).
• Data Visualization and Representation: With courses emphasizing both basic and advanced topics in data visualization, you can effectively represent and analyze complex datasets in an understandable format, leveraging both hardware and software solutions.
• Interdisciplinary Application Knowledge: The program extends beyond core data science, integrating knowledge from biology, linguistics, and psychology. This equips you to apply data science principles across varied sectors, from pharmaceuticals to social sciences.
• Optimization Techniques: Your proficiency in optimization for data science ensures that algorithms and data processes you design are efficient, effective, and tailored for specific industry needs.
• User Communication and Outreach Proficiency: The program does not just technical proficiency but also the essential skill of public user communication. Engaging with interdisciplinary projects, you gain experience in translating complex data and algorithms into comprehensible insights for diverse and general audiences. Your exposure to applications in areas like museum exhibits and public interfaces has equipped you to make data science accessible and engaging for the broader public, such that innovation and findings are shared, understood, and appreciated by all.
• Research and Project Management: Through extensive seminars and projects, you have honed your research and scientific writing skills, enabling you to tackle real-world data challenges, conceive innovative solutions, and execute as well as manage projects from inception to completion in small-scale interdisciplinary teams.
• Collaboration, Social Skills, and Team Dynamics: Exposure to various interdisciplinary projects and completing several projects equips you to collaborate effectively with professionals from other fields, understanding the nuances of cross-disciplinary data applications.

For contacts to companies with which you could possibly do an internship with, please contact the research groups below.

The modern 'information society' recognizes the immense value of data. As such, graduates of this program find themselves in a privileged position, with a myriad of career opportunities spanning various industries.

• In the data analytics divisions of German industrial giants (e.g. Siemens, Bosch, ThyssenKrupp), leveraging data to streamline manufacturing processes, predict machinery maintenance, and enhance supply chain efficiencies.
• Within the tech landscape, graduates can find opportunities as machine learning engineers or data analysts, working with global giants like Google, Apple, Microsoft, or Amazon. Their roles can span a large range from data driven solutions, enhancing cloud storage solutions, to setting the stage for the future of data science and machine learning through next-gen algorithms.
• As a data consultant working in strategy consulting (McKinsey, BCG, or Baine) or in IT-consulting (Accenture, T-Systems, Capgemini, Deloitte, Atos) working in a fast-pacing international environment to offer mission-critical tailored solutions to businesses, helping them navigate and gain insights in their data, while gaining unique perspectives in various industrial companies.
• In the tech-forward German automotive industry with firms such as BMW, Volkswagen, or Daimler AG. Graduates can employ data-driven insights to refine autonomous driving algorithms, enhance vehicle safety, and predict consumer preferences, as well as work towards the transition to electric cars.
• As a data scientist in the pharmaceutical and biotech sector in Germany, working for industry leaders like Bayer, Boehringer Ingelheim, or Merck. Here, they can utilize data to accelerate drug discovery, optimize clinical trials, and forecast market needs.
• As a data strategist or consultant for major banks (e.g. Deutsche Bank, UBS, Commerzbank), interpreting financial data to derive investment insights, using sophisticated data models to predict stock market movements, optimize asset portfolios, and ensure regulatory compliance.
• In the data solutions department of international conglomerates (e.g. GE, Philips), helping various business units harness the power of data in areas ranging from healthcare diagnostics to energy management.
• At top-tier research institutions or tech research units like Facebook AI, Google AI or Microsoft Research, pioneering innovative data solutions, machine learning models, and charting the course for future data-driven technologies.