Introduction to Programming with Python (DLBDSIPWP01)




Introduction to Programming with Python


150 h

ECTS Punkte:


Kurstyp: Pflicht, Wahlpflicht

Zu Details beachte bitte das Curriculum des jeweiligen Studiengangs

Kursangebot : WS, SS

Course Duration : Minimum 1 Semester



Kurskoordinator(en) / Dozenten / Lektoren:

Siehe aktuelle Liste der Tutoren im Learning Management System

Bezüge zu anderen Modulen:

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Beschreibung des Kurses:

This course provides students with a foundational understanding of the Python programming language. Following an introductory exposition to the importance of Python for data science-related programming tasks, students will be acquainted with fundamental programming concepts like variables, data types, and statements. Building on this basis, the important notion of a function is explained and errors, exception handling, and logging are explicated. The course concludes with an overview of the most widely-used library packages for data science.

Course Objectives and Outcome:

On successful completion of this course, students will be able to:

  • use fundamental Python syntax.
  • recollect common elementary data types.
  • recognize foundational programming concepts and their realization in Python.
  • understand error handling and logging.
  • create working programs.
  • list the most important libraries and packages for data science.

Teaching Methods:

The learning materials include printed and online course books, vodcasts, online knowledge tests, podcasts, online tutorials, and case studies. This range of learning materials is offered to students so they can study at a time, place, and pace that best suits their circumstances and individual learning style.

Course Content:

1. Introduction

1.1 Why Python?

1.2 Obtaining and installing Python

1.3 The Python interpreter , IPython, and Jupyter

2. Variables and Data Types

2.1 Variables and value assignment

2.2 Numbers

2.3 Strings

2.4 Collections

2.5 Files

3. Statements

3.1 Assignment, expressions, and print

3.2 Conditional statements

3.4 Loops

3.5 Iterators and comprehensions

4. Functions

4.1 Function declaration

4.2 Scope

4.3 Arguments

5. Errors and Exceptions

5.1 Errors

5.2 Exception handling

5.3 Logs

6. Modules and Packages

6.1 Usage

6.2 Namespaces

6.3 Documentation

6.4 Popular Data Science packages


  • Barry, P. (2016). Head first Python: A brain-friendly guide (2nd ed.). Sebastopol, CA: O’Reilly.
  • Lubanovic, B. (2019). Introducing Python (2nd ed.). Sebastopol, CA: O’Reilly.
  • Lutz, M. (2013). Learning Python (5th ed.). Sebastopol, CA: O’Reilly.
  • Matthes, E. (2019). Python crash course: A hands-on, project-based introduction to programming (2nd ed.). San Francisco, CA: No Starch Press.
  • Ramalho, L. (2015). Fluent Python: Clear, concise, and effective programming. Sebastopol, CA: O’Reilly.

A current list with course-specific compulsory reading, as well as references to further literature, is stored in the Learning Management System.


  • Depending on the course: Completion of online knowledge tests (approx. 15 minutes per unit, pass / not pass)


Exam, 90 min.

Student Workload (in hours): 150

Self-study: 90
Self-testing: 30
Tutorials: 30