* Introduction to Python and Machine Learning for Doctoral Researchers: Data, Models, Cluster (GGG) in person
In this five-day intensive workshop, doctoral researchers with no programming experience get started with Python and implement their first analyses themselves.
In total, this workshop creates a continuous path: from the first line of Python through machine learning to executing code on a cluster and compatibility with already existing on-site trainings. From day 3 onward, all exercises can also be run on participants’ own datasets if they wish and if the data meet the algorithms’ requirements.
Target group:
Doctoral candidates of GGG
Schedule:
Monday, April 26 - Friday, April 30, 2027 (Advanced learners can skip certain parts. Details below.)
time t.b.a.
Location: Conference Center at the Historical Observatory, Geismar Landstraße 11, big seminar room
Available seats: 12
Language: English
Instructor
Day 3 focuses on machine learning (ML). We get to know the third-party package “pandas” before turning to some of the most used ML-concepts:
- Clustering makes structure in data visible.
- Classification produces predictions.
Quality and outputs are recorded so they can be reused and compared. Beyond applying the methods, the focus is on a basic understanding of how the algorithms work, so parameters can be optimized based on the data itself in order to produce publication-ready results.
Day 4 covers artificial neural networks with Python. We start with their general structure and how they work, look at how chatbots (language models and large language models) function, then set up and train a neural network of our own from scratch. We finish by setting up a local AI agent on the participants own laptop, for example to summarize content or answer questions about one’s own texts.
Day 5 widens the horizon: We cover the basics of running scripts on a compute cluster (for example GWDG infrastructure): We learn how to execute code scripts on a cluster and how to retrieve and interpret results. Finally, we situate GWDG’s and KISSKI’s introductory offerings - from Linux and the Scientific Compute Cluster to the AI training platform - so the next course to attend can be chosen deliberately.
- Participants need to bring a computer with a working internet connection (e.g. eduroam).
- For starting on day 3 (i.e. skip the introduction to python), you need to be familiar with the basics of Python.
- Please remember to bring data if you would like to work with it! (From Day 3 onward, all exercises can also be run on participants’ own datasets if they wish and if the data meet the algorithms’ requirements.)
Credits: 4 ECTS (altogether)
- You will only be rewarded 4 credits for full and active participation in the entire workshop (5 days).
- Advanced learners can either skip the introduction to Python and start on day 3 or only participate in the translation of GWDG’s services (Linux, Scientific Compute Cluster, AI training platform) into a portfolio relatable for PhD candidates in Social Sciences on day 5.
- In case of partial participation, credits are being rewarded as follows:
- Participation in the Python introduction (days 1-2): 1 ECTS
- Participation in exploratory data analysis, machine learning and AI (days 3-4): 2 ECTS
- Participation in the GWDG infrastructure guidance (day 5): 0 ECTS
Registration:
- Please, register online via the registration form for GGG courses. In the registration form, under course-specific information, please indicate if you do intend to participate only partially (and on which days)!
- If you require childcare or assistance in order to participate, please let us know when you register. We will work with you to find flexible solutions that meet your needs.
- Please also note our regulations on bindingness: e.g. four weeks before a course starts, all course registrations are regarded as binding.
Contact for further information:
Dr. Nelly C. Schubert
Phone: +551 39-28219
E-mail: ggg.kursanmeldung@uni-goettingen.de
This course is organized by the Göttingen Graduate School of Social Sciences (GGG).
