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GGS-Workshop: "Data Preparation with Python"


17.06.2024 09:00 bis 25.06.2024 17:00 (Europe/Berlin / UTC200)


Room 601, Licher Str. 66, 35394 Giessen

Name des Kontakts

Telefon des Kontakts

0641 9921371


Doctoral candidates and postdocs at the GGS

Termin zum Kalender hinzufügen


Instructor:   Dr Tobias Keller

June 17, 24 & 25, 2024, 9.00 am to 5.00 pm

An "Introduction to Python (for Data Preparation)" takes place on June 14, 2024, 09.00 am to 5.00 pm.

Max. participants:   10
Course language:   English
Registration Deadline:   May 31, 2024
ECTS:   3

Please note that the knowledge of the topics covered in the previous course, “Introduction to Python (for Data 
Preparation)”, is required for participating in this follow-up course.

In particular, the participants need to be familiar with the following concepts and methods:

- Numeric data types
- String manipulation and string formatting
- Utilizing methods and method chaining
- Working with boolean data types and boolean operators
- Lists and their functionality
- Dictionaries and their usage
- Conditional statements with if statements
- Loop structures
- List comprehensions for concise data manipulation
- The creation and use of functions
- Understanding and implementing lambda functions
- Using modules and packages

If participants are not familiar with those concepts and methods, they must participate in the course "Introduction to Python (for Data Preparation)" for attending this course.



The amount of time required bringing the data into shape for machine learning and artificial intelligence algorithms or statistical analysis is often underestimated. Furthermore, introductions to data science typically focus on the methods and algorithms and do not cover the required data preparation appropriately.

This workshop aims at enabling participants to go beyond the unrealistically clean datasets provided in data science and machine learning tutorials. Instead, participants learn how to handle data as they would face it in real-life situations in research and business, where errors, inconsistencies, incompleteness, duplicates and many more problems are commonplace. They learn how to combine data from different sources and how to perform computations, aggregations, and other typical data preparation steps efficiently. Finally, participants are introduced to special data pre-processing steps required for machine learning.

Having completed this course will give participants an edge in the labour market, where most newcomers have little experience with real-life datasets – especially those aiming for a career in consulting or other areas related to data science and artificial intelligence.

This course is also an ideal complement for participants taking the course “Machine Learning with Python”.

You can find more details in the syllabus "Data Preparation with Python".