Introduction to Programming with Python for Data Science

Module Group: Computer Science

In this module, participants learn basic programming skills in Python, focusing on the application area of Data Science. This includes correctly reading, processing, and cleaning data, as well as creating aggregation analyses and visualizations. All learning content is conveyed in a practical manner with exercises based on real datasets.

The module includes a fixed weekly online session on Tuesday evenings, from 19:00 to 20:30.

Dates:

• 07/14/21/28 October 2025
• 04/11/18/25 November 2025
• 02/09/16/23 December 2025
• 13/20/27 January 2026
• 03 February 2026

Module Handbook

The module handbook can be found here.

Contents of the Module

The following contents are conveyed with a comprehensive practice component based on real data (e.g., from the Kaggle website):

• Basics of Python, IPython, and Jupyter Notebooks
• Built-in data structures, functions, handling files
• NumPy and vectorization
• Pandas
• Data cleaning
• Aggregation of data and pivot tables
• Visualization

The contents are combined in a project at the end.

Learning Objectives

Fachkompetenz (Subject-specific Competence)

• Explain syntax and semantics of language constructs in Python
• Understand basic concepts of procedural programming
• Implement programming rules for understandable and maintainable programs
• Apply basic Python libraries for data analysis
• Choose algorithmic solution patterns suitable for given analytical problem statements

Methodenkompetenz (Methodological Competence)

• Solve simple application problems with the help of a suitable programmatic solution
• Design, implement, and test algorithms for problem-solving
• Can place solutions in the operational context

Sozial- und Selbstkompetenz (Social and Self-Competence)

• Develop and discuss solutions for programming problems and work in small groups
• Assess own analytical and conceptual abilities and can reflect on technical strengths and weaknesses

Learning Setting

The online study takes place in self-study in the form of a "Flipped Classroom". For self-study, video lectures, detailed and cleanly documented code examples as Jupyter Notebooks, and practical exercises are available. In weekly online office hours, you will be supported in processing the learning material.

Prerequisites

Prerequisite is a first university degree.

In terms of content: Knowledge of relational databases (SQL) and basic programming concepts is helpful but not mandatory.

Technical Requirements for E-Learning Units

Recommended:

• A desktop computer or notebook with a current version of Microsoft Windows, Apple macOS, or Linux
• A headset
• The current version of Mozilla Firefox, Google Chrome, Apple Safari, or Microsoft Edge
• Internet access (e.g., via DSL, cable, LTE, 5G) with at least 3 Mbit/s in downstream and 384 kbit/s in upstream direction ("DSL 3000")

Please do not hesitate to contact us with questions about technical requirements.

Performance Records

For admission to the module examination (written/oral exam), the following requirements must be met:

• Participation in at least 2 attendance days
• Processing of online content marked as mandatory

In hardship cases, a informal application for admission to the exam can be submitted to the module manager.

In case of illness, a medical certificate must be presented to the module manager.

Regular participation in online forums supports you in working on the learning material. Detailed information can be found in the module description in the module handbook.

Certification

Upon successful completion of the module, you will receive a certificate and a supplement that lists the module contents as an overview. In the supplement, the module manager confirms to you the equivalent of 3 credit points according to ECTS.

Fees

The study fees for the modules for enrolled students or for taking individual modules in attendance study can be found on the page Study fees and participation fees.

Instructor

Prof. Dr. Reinhold von Schwerin
Ulm University of Applied Sciences

This certificate course was developed as part of the project "Data Literacy and Data Science for SMEs: Qualification and Further Training".

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Last edited:
12 October 2025

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