Language

English

Target Group

Doctoral and postdoctoral researchers from all departments at BTU with little or no prior knowledge of data science with Python

Description

Alongside R, Python is one of the most versatile and widely used programming languages for data science. Thanks to its clear syntax and a vast ecosystem of powerful libraries such as Pandas, NumPy, and Scikit-learn, Python enables efficient data processing, exploratory analysis, and advanced machine learning. This course serves as a comprehensive introduction to Python and its core functionalities, providing participants with practical tips and hands-on exercises to help them enter the world of programming. Using real-world datasets, participants will develop their own scripts during the workshop, serving as a sustainable reference for key Python principles and functions in their future research projects.

Content

Participants will learn the core concepts of Python, including: 

• Introduction to Python: setup, philosophy, and the Jupyter Notebook environment
• Core programming concepts: objects, functions, and the logic of the language
• Data structures and their properties: lists, dictionaries, strings, and tuples
• Data management with pandas: working with Series and DataFrames
• Data analysis with Python: calculating statistical metrics and creating informative graphics
• Control structures: writing custom functions and loops for automated workflows
• Introduction to Object-Oriented Programming (OOP): understanding classes, methods, and attributes
• Practical application: implementing learned concepts through hands-on exercises with real-world data

Objectives

This introductory course is intended for participants with little or no prior knowledge of Python or statistics. It serves as the essential starting point for further work with Python, as well as for the subsequent specialised courses:

• »Deep Dive: Regression Analysis in Python« (28 April 2026)
• »Introduction to Machine Learning with Python« (19 May 2026)
• »Deep Dive: Machine Learning with Python« (27 May 2026)
• »Data Science Research Lab« (participants will work on their own data science projects under the guidance of the trainers) (16–17 June 2026)

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, 21 April 2026, 9:00 a.m. to 5:00 p.m.

Wednesday, 22 April 2026, 9:00 a.m. to 1:00 p.m.

Registration (the workshop is fully booked)

This course is fully booked. To sign up for the waiting list, please send an email to researchschool+events(at)b-tu.de (Subject: Waiting List »Introduction to Data Science with Python«). 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 Python (equivalent to the level covered in the course »Introduction to Data Science with Python« on April 21–22).

Description

Regression analyses are a fundamental component of both classical statistical data analysis and modern machine learning. This workshop offers an in-depth introduction to performing various types of regression analysis using the Python programming language. Participants will move beyond simple correlations to build, evaluate, and interpret complex models that describe relationships between variables. By focusing on the practical implementation within the Python ecosystem, this course equips researchers with the advanced knowledge and technical skills necessary for undertaking sophisticated quantitative projects.

Content

Participants will acquire advanced knowledge and skills necessary for undertaking sophisticated projects, including:

• Understanding the mathematical and logical foundations of linear and logistic regression
• Implementing regression models using libraries such as Scikit-learn and Statsmodels
• Evaluating model fit and interpreting coefficients in a research context
• Diagnostic checking: identifying and addressing issues like multicollinearity or heteroscedasticity
• Visualizing regression results and model predictions

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, 28 April 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 – Regression Analysis with Python«). 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 Python (equivalent to the level covered in the course »Introduction to Data Science in Python«) and a basic understanding of regression analysis or other statistical models.

Description

This course provides a practical entry point into the world of Machine Learning (ML). Participants will gain insight into fundamental ML algorithms and learn how to implement them using Python’s industry-standard libraries. The workshop focuses on the entire workflow of a machine learning project, from data preparation and model training to the critical evaluation of results. By the end of the course, participants will be able to develop and implement their own basic machine learning models to solve classification and regression problems in their respective research fields.

Content

The course will cover the following topics:

• Introduction to the fundamental concepts and terminology of machine learning
• Hands-on experience with the Scikit-learn framework for implementing ML algorithms
• Understanding the logic and practice of splitting data into training and test sets
• Learning and applying crucial data preprocessing steps: One-Hot Encoding, Standardization, and Imputation
• Training and exploring various algorithms: Decision Trees, Support Vector Machines (SVM), Random Forests, and XGBoost
• Interpreting key metrics for model evaluation:
  • For classification: (Balanced) Accuracy, AUC, F1-Score
  • For regression: RMSE, MAE
• Practical exercises to reinforce the modeling workflow

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, 19 May 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 Python«). 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 Python and knowledge of the methodologies covered in the »Introduction to Machine Learning with Python« course (specifically the Scikit-learn framework).

Description

Building upon the introductory machine learning course, this "Deep Dive" workshop explores advanced topics and professional workflows in Python. The focus shifts toward creating robust, reproducible, and optimized models. Participants will learn how to streamline their code using pipelines and how to systematically improve model performance through automated tuning techniques. This course emphasizes the independent application of learned concepts, enabling researchers to handle complex datasets and more demanding predictive tasks with confidence.

Content

The course will cover the following topics:

• Exploration of advanced machine learning topics and complex algorithm configurations
• Integrating preprocessing and modeling steps into a seamless Scikit-learn Pipeline for reproducible research
• Fine-tuning models with parameter tuning techniques: Grid Search and Randomized Search
• Applying Cross-Validation to ensure model robustness and prevent overfitting
• Independent application of learned concepts through intensive practical exercises
• Discussion of best practices for deploying and documenting machine learning workflows

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

Wednesday, 27 May 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 Python«). 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 Python in previous courses.

Description

The »Data Science with Python – Research Lab« is designed to help participants develop their own data science research projects, leveraging the data science with Python 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, 16 June 2026, 9:00 a.m. to 5:00 p.m.

Wednesday, 17 June 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 Python – 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 and postdoctoral researchers from all departments at BTU

Description

The online workshop is dedicated to the increasingly important role of Artificial Intelligence (AI) in academic research and writing. We will focus on the practical application of AI technologies in the context of research and scientific writing. In this introductory seminar, you will gain in-depth insights into the opportunities that AI offers researchers. 

The workshop content is split into two parts: the first covering the use of ChatGPT and prompt engineering, and the second covering other academic AI tools that can aid research and writing. We will explore both the benefits and challenges of using AI, including creative support in the conception and formulation of scientific papers.

In addition to highlighting the opportunities, we will also address the ethical aspects and risks associated with the use of AI, such as questions of copyright and the authenticity of research results. The aim of the seminar is to equip you with practical knowledge and skills so that you can use AI as an effective tool in your day-to-day research.

Through interactive elements such as case studies and group discussions, you will have the opportunity to engage directly with the content and discuss individual questions.

Objectives

• Recognise the benefits of Artificial Intelligence in academic writing and develop the ability to use various AI tools effectively.
• Understand the different capabilities of ChatGPT through correct prompting techniques.
• Discover how AI can be utilised for brainstorming and idea generation in academic contexts.
• Explore various AI tools that facilitate research and writing processes.
• Increase awareness of and evaluate ethical concerns and potential risks associated with AI use in publishing.
• Engage in critical discussions about the future role of AI in academia.

Trainer

Dr. Dimitra Lountzi studied Life Sciences and Genetics in Glasgow and Regenerative Medicine in Edinburgh. She holds a PhD in Neuroscience from the University of Bonn. She has been a coach for TwentyOne Skills since 2020, focusing on artificial intelligence, communication and negotiation skills.

Time

Tuesday, 9 June 2026, 9:00 a.m. to 4:00 p.m.

Registration

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

Registration is on a first-come, first-served basis. 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.