Foto: Universität Hohenheim, Jan Potente

AIDAHO Curricula

The AIDAHO program offers an extensive additional education in the fields of AI and Data Science. Students take five different courses alongside their main studies. In return, they receive a certificate that is listing the taken courses and acquired competences.

On this page, you can find all information about the AIDAHO curricula, the personal study organisation and how to enroll in the program.

Information on whether the course was passed or not will be recorded on F.I.T. at the end of the semester.

AIDAHO courses

How to achieve the certificate

To succesfully complete the program, students have to pass at least five AIDAHO modules.

  • There are three mandatory basic modules that all participants have to complete. The courses of these modules teach basic programming skills and statistic methods.
  • In the two semi-elective specialization modules the students can either deepen their methodological skills or choose to work on data projects in application seminars.

The following sections cover additional information about the basic and spezialitation modules. A list of all courses in the AIDAHO program can be found here.

Prerequisites

AIDAHO is open to all registered students of the University of Hohenheim. No specific prior knowledge is required. However, the program deals with advanced mathematics and statistics. We therefore strongly recommend that you have at least a keen interest in these topics. 

The basic modules contain three courses which all participants of the AIDAHO program have to pass:

  • In „Tools for AI & Data Science: Introduction to Python, R & SQL“ students will receive an overview of AIDAHO. Besides the basics of coding, students will learn the handling with data (preparation, analysis, visualisation).

  • In „Introduction to Machine Learning with Python“ students deepen their programming knowledge in python and learn basic concepts of machine learning. 

  • In „Introduction to (Applied) Data Science with R and RStudio“ students expand their programming knowledge in R and learn how to handle big amounts of data.

     

In the Specializing Modules students enroll in two courses. At least one of them has to be an application course:"

  • "Methodological In-Depth Lectures" expand or deepen the methodological knowledge acquired in the basic modules. Students can learn advamced methods in data analysis, machine learning or artificifal intelligence.

  • In "Application seminars"* students apply their knowledge by working on their own data projects. This includes the preparation and analysis of the data and the visualization and presentation of the results.

*Passed project works, seminar papers or theses, in which a substantial part was the quantitative data analysis or working with machine learning/artifical intelligence, can be credited as an “application seminar”. Therefore, students have to send this form to aidaho@uni-hohenheim.de

Individual study planning

Personal focus

AIDAHO can be completed by students of all three Hohenheim faculties.

All participants have to pass the three courses of the basic modules. The further course of the program can be designed individually. The AIDAHO courses can be taken in any order. Please note that not all courses are offered every semester.

The following sections answer questions regarding the registration for the modules and how they can be used in the main studies of the participants.

The F.I.T. platform lists all further education courses at Hohenheim University. The AIDAHO programm uses the platform for the administration of the participants and courses.

The following document contains instructions on how to register for courses on the F.I.T. platform:

FIT-Anmeldung
(german)

The AIDAHO program is completed alongside the main study program. After completing the introductory course "Tools for AI & Data Science: Introduction to Python, R & SQL", participants are free to attend the other four courses in any order. The following figure shows an example of how the AIDAHO program could be completed in four or three semesters: