Profil: SQ, KIKR.
Schwerpunkt: IMA-SQ, AI, VMC
weitere Studiengänge: M-M-Alg-Num, M-T

The development of solving techniques for linear programming tasks are a huge contribution of Mathematics to the solution of practical optimization problems. This course is an introduction both into the theory and the application of linear and integer optimization. We will cover the theoretical background as well as algorithmic ideas and practical applications. Main topics that we will cover in the course are the Simplex Method, Ellipsoid Method, Interior Point Method, Cutting Planes, Branch & Bound, LP Duality, and Polyhedral Theory.

Homepage zur Veranstaltung: https://www.uni-bremen.de/rtg-pi3/qualification/rtg-seminar

Note: The course will be held online.

Target group: Students with basic knowledge of statistics (For e.g.: understanding on normal distribution, t-test, chi-square test, p-value)

Course description: Students will be introduced to performing data management and simple to intermediate statistical analyses using Stata. The students will be using practical examples on how datasets and analyses should be described and documented in order to ensure the reproducibility of their own research.

Precourse preparation: Students are required to acquire short-term (1 – week) license request using the following link
https://www.stata.com/customer-service/short-term-license/
Note: Please apply for license only 2-3 days before the course as the license is valid only for 7 days.

Course content:
 Navigating the Stata interface
 Creating, importing and exporting datasets
 Structure of Stata dataset
 Elements of Stata syntax
 Creating and maintaining do files
 Saving work in log file
 Data management
 Modifying datasets
 Summarizing data
 Graphics and data visualization
 Statistical analysis
 Looping on repeated tasks

Exam and assessment: Students will be assessed via assigned statistical tasks for preparation of do-and log files at the end of the course.

Research software development deserves a systematic approach to keep up with the demand for reproducible science and reuse of codes as a citeable scientific output.
In the context of Open Science, sustainable research software is more and more estimated as vital component of research infrastructures.
This course provides an introduction to the practice of scientific programming to a broader audience within the General Studies. The basis are real world research codes that will be explored and executed on local programming environments - either on students laptops or on central compute nodes at University Bremen.
Principles of code-management and code publication will be actively explored in small practical projects, open for students interests in bringing their own software projects. Two main "code use cases" are provided, based on Fortran and C programming language, and further projects are offered for R and C++ as well. Special emphasis is laid on performance optimization and two standard approaches of parallelization, i.e. loop parallelization and domain decomposition.

The course is useful for math students and interested participants from other fields (e.g. industrial math, numerics of PDEs, modeling seminar, material sciences - ProMat).

Kursinformationen und Onlineanmeldung unter https://www.fremdsprachenzentrum-bremen.de/1375.0.html?&no_cache=1&user_kursdb_pi1%5BshowUid%5D=14275