Language

English

Target Group

Doctoral and postdoctoral researchers from all departments at BTU with some understanding of data science; no prior knowledge of R required.

Description

Alongside Python, R is a leading programming language for statistical analysis, machine learning and data visualisation. It features specialised libraries for efficient, reproducible, complex data analysis. R enables large datasets to be evaluated quickly, precise predictions to be made, and interactive dashboards to be created. The course will introduce the logic and terminology of the R programming language, equipping participants with the skills needed to apply R to their own projects.

This advanced course is intended for participants of the »Introduction to Data Science« course who have no prior knowledge of R or statistics. It serves as a starting point for further work with R, as well as for subsequent specialised courses such as »Deep Dive: Regression Analysis in R«, »Introduction to Machine Learning with R«, and »Deep Dive: Machine Learning with R«.

The dates of the subsequent courses will be announced shortly on this website and through the GRS Newsletter.

Objectives

Participants will learn the core concepts of R including:

• Introduction to R: the R program, CRAN mirrors, various R environments/editors
• Using internal help functions and online resources
• Concept and philosophy of R: the programming language, objects, value assignment, functions
• Data structures and their properties: vectors, data frames, lists, etc.
• Data import: TXT, CSV, XLS, and SAV files, internet sources, etc.
• Data management: creating new variables, conditional recoding, simple calculations, handling missing values
• Using dplyr, tidyr, and dealing with special data types
• Data analysis with R: statistical metrics, simple tables, and graphics
• Initial simple statistical calculations
• Loops and control structures
• Creating visualizations
• Introduction to the leading development environment RStudio

Trainer

Lukas Löber has worked as a senior data scientist and data science trainer at eoda GmbH since 2017. He specialises in data analysis projects and knowledge transfer in data science, particularly using R and Python. His work covers a range of topics, from data management to specialised machine learning methods. He is a certified R trainer with Posit (RStudio) and supports clients ranging from SMEs to large corporations across various industries. He shares his experience and knowledge through consulting projects and individual coaching sessions.

Florian Schmoll has also been a senior data scientist and project manager at eoda GmbH since 2017, helping companies ranging from SMEs to large corporations in industries such as mechanical engineering and pharmaceuticals to generate value from data. A certified R trainer with Posit (RStudio), he brings his technical and methodological expertise to his data science training courses on Python and R, covering everything from the basics to advanced machine learning. As a conference speaker, he shares insights into his daily work.

Time

Tuesday, 30 September 2025, 9:00 a.m. to 5:00 p.m.

Wednesday, 1 October 2025, 9:00 a.m. to 12:00 p.m.

Registration

Please register by email to researchschool+events(at)b-tu.de (Subject: Registration »Introduction to Data Science in R«). In your email, please indicate your status (doctoral or postdoctoral researcher) and your chair.

Registration is on a first come, first served basis (max. 15 participants). If the course is fully booked, we will place you on a waiting list and notify you if a spot becomes available. Please note that it may take a few days to confirm your registration.

Language

English

Target Group

Doctoral researchers from all departments at BTU

Description

This workshop will introduce doctoral researchers from various disciplines to LaTeX, a word-processing system specially designed to produce scientific documents. Compared to commonly used word processors such as Word, LaTeX offers the advantage that many tasks can be automated and no longer have to be done manually, e.g., creating a bibliography.

No previous knowledge is required. The workshop will use the online environment Overleaf; participants should ideally create an account on overleaf.com in advance.

Objectives

Participants will learn the basic concepts of LaTeX and can create simple text documents, correct errors and become familiar with LaTeX. Topics covered include the structure of a LaTeX document, integrating various design elements such as graphics and tables, setting mathematical formulae, and various options for customising the layout and appearance of the document.

Trainer

Dr.-Ing. Lea Schönberger is a computer science PhD graduate and freelance science communicator. Originally from Münster, she initially studied computer science with a minor in Latin at the University of Münster, before completing her PhD in computer science at the University of Dortmund in 2023. Since 2020, she has also been studying comparative literature and classical philology in Bochum. As the host of the podcast »Informatik für die moderne Hausfrau« (Computer Science for the Modern Housewife), she introduces curious listeners to topics related to computer science and provides a platform for interesting women.

Time

Tuesday, 21 October 2025, 9:00 a.m. to 5:00 p.m.

Registration

Please register by email to researchschool+events(at)b-tu.de (Subject: Registration »Introduction to LaTeX«). In your email, please indicate your status (doctoral researcher) and your chair.

Registration is on a first come, first served basis (max. 12 participants). If the course is fully booked, we will place you on a waiting list and notify you if a spot becomes available. Please note that it may take a few days to confirm your registration.

Language

English

Target Group

Doctoral and postdoctoral researchers from all departments at BTU

Description

AI-based tools such as ChatGPT and Microsoft Copilot are now widely used and have even become part of the higher education landscape. However, they can only be used carefully and appropriately if users are aware of their underlying principles, application possibilities, and limitations.

This workshop will begin with introducing artificial intelligence and its underlying concepts. Following this, one or more AI-based tools will be presented and tested. Participants will also receive an overview of current frameworks and best practices for AI-based tools in higher education.

Objectives

• Understand how machine learning and generative artificial intelligence work.
• Recognise and consider the limitations, sources of error and risks associated with these technologies.
• Learn to formulate queries for AI-based tools using prompt engineering techniques to achieve the best possible results.
• Learn about specific applications and usage scenarios.
• Gain insight into how generative AI tools can be integrated into your daily work routine.

Trainer

Dr.-Ing. Lea Schönberger is a computer science PhD graduate and freelance science communicator. Originally from Münster, she studied computer science with a minor in Latin at the University of Münster. She completed her PhD in computer science at the University of Dortmund in 2023. Since 2020, she has also been studying comparative literature and classical philology in Bochum. As the host of the podcast »Informatik für die moderne Hausfrau« (Computer Science for the Modern Housewife), she introduces curious listeners to topics related to computer science and provides a platform for interesting women.

Time

Thursday, 23 October 2025, 9:00 a.m. to 5:00 p.m.

Registration

Please register by email to researchschool+events(at)b-tu.de (Subject: Registration »Understanding the Basics of Artificial Intelligence«). In your email, please indicate your status (doctoral or postdoctoral researcher) and your chair.

Registration is on a first come, first served basis (max. 12 participants). If the course is fully booked, we will place you on a waiting list and notify you if a spot becomes available. Please note that it may take a few days to confirm your registration.