Module 39-Inf-Pro Programming

Faculty

Person responsible for module

Regular cycle (beginning)

Every winter semester

Credit points and duration

5 Credit points

For information on the duration of the modul, refer to the courses of study in which the module is used.

Competencies

Non-official translation of the module descriptions. Only the German version is legally binding.

In the lectures and are the exercise courses, students will build a basic understanding of algorithmic modeling, the design and analysis of algorithms, and develop the ability to independently analyse simple algorithms and to carry out programming tasks. The programming language that we focus on in this lecture is Python. The module includes an exam at the end of the term.

Content of teaching

Before data can be analyzed it often needs to be obtained and then it needs to be transformed, cleansed and structured ? that means, it is rarely available in exactly the required form. This module provides an introduction to the programming language Python and to the libraries relevant for data analysis.

Topics of this module include:

  1. Introduction to programming in Python
  2. Standard algorithms and data structures in Python
  3. The Jupyter Notebook environment
  4. Data munging, preparation and analysis with Pandas
  5. Scientific computing with NumPy
  6. Machine learning with scikit-learn
  7. Statistical data visualization with Seaborn and Bokeh
  8. Natural Language Processing with NLTK
  9. Database Programming
  10. Interacting with Databases (e.g., Apache HBase)

Recommended previous knowledge

Necessary requirements

Explanation regarding the elements of the module

Module structure: 1 bPr 1

Courses

Programming
Type lecture
Regular cycle WiSe
Workload5 60 h (30 + 30)
LP 2 [Pr]
Programming
Type exercise
Regular cycle WiSe
Workload5 60 h (30 + 30)
LP 2

Examinations

written examination
Allocated examiner Teaching staff of the course Programming (lecture)
Weighting 1
Workload 30h
LP2 1

60 minutes

The module is used in these degree programmes:

Degree programme Profile Recom­mended start 3 Duration Manda­tory option 4
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 1 1. one semester Obli­gation

Automatic check for completeness

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Legend

1
The module structure displays the required number of study requirements and examinations.
2
LP is the short form for credit points.
3
The figures in this column are the specialist semesters in which it is recommended to start the module. Depending on the individual study schedule, entirely different courses of study are possible and advisable.
4
Explanations on mandatory option: "Obligation" means: This module is mandatory for the course of the studies; "Optional obligation" means: This module belongs to a number of modules available for selection under certain circumstances. This is more precisely regulated by the "Subject-related regulations" (see navigation).
5
Workload (contact time + self-study)
SoSe
Summer semester
WiSe
Winter semester
SL
Study requirement
Pr
Examination
bPr
Number of examinations with grades
uPr
Number of examinations without grades
This academic achievement can be reported and recognised.

Sidebar

Elements of the module

Courses

Examinations

Programme of lectures (eKVV)

Programme of lectures (eKVV)