Qualification targets Bachelor Mathematical Data Science (180 ECTS)
The aim of this subject is to familiarise students with the most important sub-fields of mathematics in the interdisciplinary field of mathematics, computer science and data science, to teach the methods of mathematical thinking and working, as well as to train analytical thinking, the ability to abstract and the ability to structure complex interrelationships.
Through the training of these skills, the students acquire the knowledge required for a possibly
for any subsequent postgraduate studies, especially a Master's degree.
In addition, they will later know how to flexibly familiarise themselves with the diverse areas of our society in which innovative computational mathematical methods are used or can be used.
can be used. The focus is on modern data-based methods.
This orientation is supported by taking numerical laboratory practical courses, in which the students are familiarised with the fundamental ways of thinking and working techniques of mathematical data science.
In the Bachelor's degree subject Mathematical Data Science, the main focus is on well-founded basic mathematical
mathematical basic knowledge, methodological skills and the development of thought structures typical for mathematics. The acquisition of knowledge in sub-areas of mathematics is subordinate to this.
Scientific qualification
| Qualification target | Implementation | Target achievement |
|---|---|---|
| Graduates are familiar with the working methods and the associated technical language of mathematics and have mastered the methods of mathematical thinking and proving. | Basic mathematical concepts and proof methods, argumentation and writing in mathematics, mandatory modules in analysis and linear algebra | Exercises in small groups, mandatory exercises, ungraded examinations, individual oral examinations |
| Graduates have a sound knowledge of the fundamentals of mathematical data science and can confidently use the methods. | Mandatory modules | Exercises and programming tasks, graded examinations, individual oral examinations |
| Graduates understand the basic concepts of artificial intelligence and machine learning. | Mandatory modules, mandatory elective modules | Exercises, programming exercises, project work |
| Graduates possess fundamental knowledge of further areas of mathematics and are familiar with the basic proof methods of these areas. | Mandatory electives modules | Exercises, ungraded examinations, individual oral examinations |
| Graduates are able to implement theoretical methods of numerics, stochastics and machine learning algorithmically and apply them to practical problems. | Numerical laboratory practical course | Project work |
| Graduates are trained in analytical thinking, possess a high level of abstraction, universally applicable problem-solving skills and the ability to structure complex contexts. | Lectures with exercises, seminars, thesis | Exercises, written examinations, individual oral examinations, presentations, thesis |
| Graduates are able to independently familiarise themselves with further areas of mathematics with the help of specialist literature. | Seminars, thesis | Presentations, thesis |
| Graduates are able to present their knowledge, ideas and solutions to problems in an understandable way. | Seminars, exercises | Presentations, presentation of the solution of exercises |
| Graduates possess the basic knowledge, ways of thinking and methodological skills required for further, especially Master's, studies. | Lectures, exercises, seminars, thesis | Exercises, individual oral examinations, presentations, thesis |
| Graduates know the rules of good scientific practice and are able to observe them in their own work. | Thesis | Thesis |
Ability to take up employment
| Qualification target | Implementation | Target achievement |
|---|---|---|
| Graduates are trained in analytical thinking, possess a high level of abstraction, universally applicable problem-solving skills and the ability to structure complex contexts. | Lectures with exercises, seminars, thesis | Exercises, written examinations, individual oral examinations, presentations, thesis |
| Graduates are able to formulate and present their knowledge, ideas and problem solutions in a target group-oriented and comprehensible way. | Seminars, exercises, external internship, tutoring and proofreading | Presentations, presentation of the solution of exercises, practical report, supervision of an exercise group under guidance |
| Graduates are able to accurately model practical problems and develop solutions using mathematical methods of data science. | Mandatory and mandatory elective modules, programming practical, numerical laboratory practical course, seminar, external practical, thesis | Exercises, programming practical, practical report and presentation, presentation, project work, thesis |
| Graduates have a strong perseverance in solving complex problems. | Exercises, thesis | Exercises, thesis |
| Graduates are able to work constructively and goal-oriented in teams. | Exercises, programming course, numerical laboratory practical courses, external practical, computer-oriented mathematics | Various exercise concepts with group work, exercises, practical report, project work |
| Graduates are able to access further areas of knowledge independently, efficiently and systematically. | Seminars, thesis | Presentations, thesis |
| Graduates will be able to implement methods they have learned and handle mathematical software with confidence. | Numerical laboratory practical courses, machine learning, numerical mathematics and stochastics, external practical | Programming exercises, project work, internship report |
| Graduates possess the ability to play a formative role in interdisciplinary teams in the field of mathematics, computer science and empirical sciences. | External practical, mandatory and mandatory elective modules | Group work in exercises and practicals, presentations |
| Graduates know the basic algorithms of artificial intelligence and machine learning and can apply them to practical problems. | Mandatory modules, mandatory elective modules, external practical | Exercises, programming tasks, project work, internship report |
Personality development
| Qualification target | Implementation | Target achievement |
|---|---|---|
| Graduates are trained in analytical thinking, possess a high level of abstraction, universally applicable problem-solving skills and the ability to structure complex contexts. | Lectures with exercises, seminar, thesis | Exercises, written examinations, individual oral examinations, presentations, thesis |
| Graduates are able to critically reflect and evaluate social, economic and historical developments and processes. | Applications of Data Science in other disciplines , selected chapters in the history of mathematics, ASQ-Pool, thesis | Presentations, project work, thesis |
| Graduates are able to participate in participatory processes. | Involvement in the student council and other student structures, participation in commissions and committees | Committee work and meetings |
| Graduates have a strong perseverance in solving complex problems. | Exercises, thesis | Exercises, thesis |
| Graduates are able to formulate and present ideas and proposed solutions in a generally understandable way. | Seminars, exercises, external practical, tutoring and proofreading | Presentations, presentation of the solution of exercises, practical report, supervision of an exercise group under guidance |
