Universität Wien
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040080 UK SOLV (2023S)

Introduction to Python for Statistics

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
REMOTE
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 08.03. 11:30 - 13:00 Digital
  • Wednesday 15.03. 11:30 - 13:00 Digital
  • Wednesday 22.03. 11:30 - 13:00 Digital
  • Wednesday 29.03. 11:30 - 13:00 Digital
  • Wednesday 19.04. 11:30 - 13:00 Digital
  • Wednesday 26.04. 11:30 - 13:00 Digital
  • Wednesday 03.05. 11:30 - 13:00 Digital
  • Wednesday 10.05. 11:30 - 13:00 Digital
  • Wednesday 17.05. 11:30 - 13:00 Digital
  • Wednesday 24.05. 11:30 - 13:00 Digital
  • Wednesday 31.05. 11:30 - 13:00 Digital
  • Wednesday 07.06. 11:30 - 13:00 Digital
  • Wednesday 14.06. 11:30 - 13:00 Digital
  • Wednesday 21.06. 11:30 - 13:00 Digital
  • Wednesday 28.06. 11:30 - 13:00 Digital

Information

Aims, contents and method of the course

The goal of the course is to provide students with programming fundamentals in Python under special consideration of tasks related to statistics. Besides an introduction to the basics of the programming language itself, data processing and handling, visualization and the usage of common libraries for data analysis are covered.
An outline of the course can be given as follows (topics order might change):
- Python basic syntax & statements
- Control flow (if-statements, loops)
- Basic data structures (lists, dictionaries, sets)
- Array-oriented programming with NumPy
- Functions
- Basics of object-oriented programming
- Advanced data structures (queues, heaps, ...)
- Data processing with pandas
- Data visualization
- Overview of stats-related libraries
- Random number generation and Monte-Carlo simulation
- Stats-related workflows: R vs. Python

Assessment and permitted materials

Assessment scheme:
Homework assignments: 40%
Take-home exam: 25%
Theory quiz (online): 10%
Mini-project: 25%

Minimum requirements and assessment criteria

At least 50% (overall) have to be obtained for a positive grade. The other grades are distributed as follows:
4: 50% to <63%
3: 63% to <75%
2: 75% to <88%
1: 88% to 100%

Examination topics

Reading list

Association in the course directory

Last modified: Th 11.05.2023 11:27