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 with R« (13 October 2026)
• »Introduction to Machine Learning with R« (23 October 2026)
• »Deep Dive: Machine Learning with R« (29 October 2026)
• »Data Science with R – Research Lab« (17–18 November 2026)

You can already register for our subsequent courses on this website.

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, 6 October 2026, 9:00 a.m. to 5:00 p.m.

Wednesday, 7 October 2026, 9:00 a.m. to 1:00 p.m.

Registration 

Please register by email to researchschool+events(at)b-tu.de (Subject: Registration »Introduction to Data Science with 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 and postdoctoral researchers from all departments at BTU with a basic understanding of R (equivalent to the level covered in the course »Introduction to Data Science with R« on October 6 & 7).

Description

Regression analyses are a fundamental component of data analysis and machine learning. This workshop offers an in-depth introduction to performing regression analysis using the R programming language.

Objectives

Participants will acquire advanced knowledge and skills necessary for undertaking sophisticated projects.

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, 13 October 2026, 9:00 a.m. to 1:00 p.m.

Registration

Please register by email to researchschool+events(at)b-tu.de (Subject: Registration »Deep Dive – Regression Analysis with 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 and postdoctoral researchers from all departments at BTU with

• a basic understanding of R (equivalent to the level covered in the course “Introduction to Data Science with R” on October 6 & 7);
• and a basic understanding of regression analysis or other types of models is desirable.

Description

The course will cover the following topics:

• Introduction to the fundamental concepts of machine learning
• Hands-on experience with the tidymodels framework for implementing machine learning algorithms
• Understanding the logic and practice of splitting data into training and test sets
• Learning and applying crucial data preprocessing steps, such as one-hot encoding, standardisation, and imputation
• In-depth exploration of Decision Trees: understanding their functionality and practical application
• Integrating preprocessing and modelling steps into a seamless tidymodels workflow
• Interpreting key metrics for evaluating models
• For classification: (Balanced) Accuracy, AUC, F1-Score, etc.
• For regression: RMSE, MAE
• Fine-tuning models with parameter tuning techniques like Grid Search using cross-validation

Objectives

This course aims to provide an in-depth understanding of machine learning algorithms through practical exercises, enabling participants to develop and implement their machine learning models in R by the end of the course.

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

Friday, 23 October 2026, 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 Machine Learning with 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 and postdoctoral researchers from all departments at BTU with

• a basic understanding of R (equivalent to the level covered in the course “Introduction to Data Science with R” on October 6 & 7);
• a basic understanding of regression analysis or other types of models;
• and knowledge of methodologies from the "Introduction to Machine Learning with R" course on 23 October, in particular, a fundamental understanding of the tidymodels framework.

Description

The course will cover the following topics:

• Exploration of advanced machine learning topics
• Training machine learning models using a variety of algorithms, including Decision Trees, Support Vector Machines, Random Forests, XGBoost, and more
• Understanding the interpretability of predictions through explainable AI (XAI) methods
• Optional: Learning about feature importances
• Introduction to survival analysis as a time-to-event approach
• Independent application of learned concepts through practical exercises

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

Thursday, 29 October 2026, 9:00 a.m. to 5:00 p.m.

Registration

Please register by email to researchschool+events(at)b-tu.de (Subject: Registration »Deep Dive – Machine Learning with 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 and postdoctoral researchers from all departments at BTU with an interest in developing their data science projects. The participants have obtained key insights into Data Science with R in previous courses.

Description

The »Data Science with R – Research Lab« is designed to help participants develop their own data science research projects, leveraging the data science with R knowledge gained in previous courses. This workshop series begins with a brief project identification session, followed by hands-on focus workshops for in-depth development, and is complemented by additional office hours for individual support.

Key Components of the Research Lab are:

1. Project Identification Workshop

The participants identify relevant topics and projects, briefly present their ideas, and discuss fundamental requirements, possible approaches, and any open questions with eoda coaches.

2. Focus

The previously selected or defined topics are developed practically. The focus is on the concrete application and further development of the learned content.

3. Additional support and advice

To ensure the sustainable implementation of workshop content, the eoda coaches offer individual support to discuss challenges and receive practical support even after the workshops.

Objectives

This workshop aims to integrate the developed concepts and methods into participants’ daily work. Additionally, the workshop provides an opportunity to clarify specific questions and advance individual initiatives.

Trainer

Christoph Schmidt has been a data scientist at eoda GmbH since 2017. His work there focuses on implementing data science projects and delivering training courses in Python and R. He completed a Master's degree in survey statistics at the University of Bamberg. He combines theoretical knowledge with practical experience, fostering productive learning environments through his work on projects as a data scientist and project manager. With several years of experience teaching data science, ranging from introductory courses to individual advanced courses in machine learning, the R trainer, certified by Posit (RStudio), is a seasoned professional in the field.

Time

Tuesday, 17 November 2026, 9:00 a.m. to 5:00 p.m.

Wednesday, 18 November 2026, 9:00 a.m. to 1:00 p.m.

Registration

Please register by email to researchschool+events(at)b-tu.de (Subject: Registration »Data Science with R – Research Lab«). 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, 10 November 2026, 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

Friday, 20 November 2026, 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.