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 in R” on September 30 and October 1);
• 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

Monday, 27 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 Machine Learning 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 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 in R” on September 30 and October 1);
• a basic understanding of regression analysis or other types of models;
• and knowledge of methodologies from the "Introduction to Machine Learning in R" course on 27 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

Wednesday, 5 November 2025, 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 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

Content

• Legal and formal requirements
• Publishing options according to doctoral regulations
• How to publish your thesis electronically
• Digital Repository (OPUS)
• Support by the library

Trainer

Zdenka Günzel, University Library (IKMZ)

Time

Monday, 1 December 2025, 2:00 to 3:30 p.m.

Registration

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

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 in R in previous courses.

Description

The Data Science Research Lab is designed to help participants develop their own data science research projects, leveraging the understanding of data science in R obtained 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 Data Science Research Lab are:

1. Project Identification Workshop

The participants identify relevant topics and projects, briefly presenting their ideas and discussing 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

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.

Martin Schneider has over 10 years of experience managing data science projects at eoda GmbH, spanning various industries from SMEs to large corporations. As a consultant, he supports companies in developing AI applications and enhancing data skills. Since 2014, Martin has been a data science trainer, offering courses in R and Python, from introductory levels to advanced topics. He is certified as an R trainer by RStudio (Posit) and regularly speaks at conferences on professional data science and AI applications.

Time

Monday, 8 December 2025, 9:00 a.m. to 5:00 p.m.

Tuesday, 9 December 2025, 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 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.