Data Science Course Portfolio

Extend your skills and competences by finding the right learning path for your needs in HIDA's extensive training portfolio! Together with the platforms of the Information & Data Science Framework, we have developed a coordinated course offer that covers a wide array of topics.

HIDA offers an extensive training portfolio in Information & Data Science, designed to support learners at every stage of their development. Therefore, the five Information & Data Science Platforms provide a centrally accessible and coordinated course offer. Whether you are taking your first steps in coding, diving into advanced statistical modeling, or exploring AI techniques, our portfolio offers curated clusters and learning paths to suit your goals. What makes our training portfolio unique is the applied approach that is predominant in all our courses. Practical applications and case studies are designed to prepare you to succeed in your research or project.

All courses are held in English.

Discover our diverse learning paths for data scientists

HIDA's learning paths provide a structured roadmap for mastering data science concepts within and across the six clusters. We present learning paths that direct you from beginners’ level, e.g. in the form of foundational programming in Python or R, to an advanced learning stage in for example essential statistics and AI.

Programming with R

Harness the power of R. Learn how to code with R, learning to read, manipulate, and visualize data while building reproducible workflows and manuscripts tailored for scientific research.

Statistics

Deepen your understanding of probability, hypothesis testing, regression, and more. This cluster is perfect for learners aiming to ground their data science practice in rigorous statistical methods.

Introduction to Statistics

Mixed Models

Multivariate Statistics 1

Multivariate Statistics 2

Open Research

Unlock the potential of open science through courses on collaborative research practices and project management, open-source tools and software, and publishing ethically sound, reproducible, and transparent results.

Kickstart Shell & Git
Introduction to Git & GitLab
Foundations of Research Software Publication
Continuous Integration
Fundamentals of Software Testing
Reproducible and Open Research
AI Ethics: Model Cards for Model Reporting

Python Fundamentals

Start with the building blocks of Python, the groundwork for AI based techniques like machine learning. These courses guide you through core programming concepts and the development of reusable, efficient code.

First Steps in Python
Data Processing with Pandas & Data Visualization with Matplotlib
Kickstart Python
Object Oriented Programming

AI and Data Science

With machine learning and deep learning being the cornerstones, the courses in this cluster prepare the learners for advanced AI tools and applications. Courses from explainable AI to supercomputers, dimensionality reduction, uncertainty quantification, and AI based imaging techniques mark the learning journey in this cluster.

Basic Methods in Machine Learning / Introduction to Machine Learning
Understanding Transformers
Advanced Methods in Machine Learning
Introduction to Uncertainty Quantification
Introduction to Deep Learning
A Practical Guide to Dimensionality Reduction
Introduction to Explainable AI
Overview of AI‘s Parallelization Methods in Supercomputers
3D Data Visualization
Regularization in Image Reconstruction
ML Based Image Analysis & ML for Instance Segmentation
Six Main Tasks in Image Processing

Fair and Data Management

Master the principles of good research data management. Explore topics like FAIR (Findable, Accessible, Interoperable, and Reusable) and metadata tools to ensure your analyses are both impactful and responsible.

Fundamentals of Scientific Metadata for Energy
Fundamentals of Scientific Metadata for Health
Reusability of Scientific Data - Matter

How does it work?

Learning paths: The arrows in the graphical representation of the training portfolio represent the learning paths.

A consistent arrow indicates that the skills and competences taught in a previous course are mandatory or required to attend the following course.
A dashed arrow indicates that the skills and competences taught in a previous course are recommended or advantageous to attend the following course.

Flexible Learning: The training portfolio follows a mix and match approach, and we offer adaptable paths that accommodate your unique needs and pace.

The learning paths can be

followed from beginning to end
be accessed at the point of your choice
not be followed at all.

Whether you need to (partially) follow a path, or just hop in the course(s) of interest depends on your skill level. Please evaluate your skill level carefully. In all course descriptions, you will find specified prerequisites. We ask you to realistically assess whether you meet all prerequisites for the course, and only then take up one of the course seats. Based on this assessment, you can then locate the starting point of your learning path accordingly.

The courses are organized by the five platforms of the Helmholtz Information & Data Science Framework.

Take the first step on your learning path to increase your data science expertise and succeed with your project. Do you have any questions or feedback? Please do not hesitate to contact us!