188.995 Data-oriented Programming Paradigms
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2022W, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to program in Python in a data-oriented way, using SciPy, NumPy and Pandas; explain the fundamentals of machine learning and network analysis, and implement a Data Science project.

Subject of course

The following topics are covered in the lectures:

  • Introduction to doing Data Science
  • SciPy, NumPy, vectorisation, execution performance measurement
  • Data preparation, structuring, fusion with Pandas
  • Data Science solution approaches and case studies
  • Introduction to machine learning
  • Introduction to network analysis

Teaching methods

Lectures about the fundamentals

2 practical exercises (Exercise 1 is done individually, Exercise 2 is done in a group)

Mode of examination


Additional information

The lectures are online. The link to the online lectures is on TUWEL.

All other sessions are in presence (if they must be moved online due to the pandemic, then an announcement will be made).



All Lectures on Tuesday 12:00 c.t.-13:45.

  1. Kickoff-Session, data science process, community, solution examples, introduction to DOPP (4.10.2022)

  2. SciPy, NumPy, vectorisation, visualisation, benchmarking (11.10.2022)

  3. Preprocessing, Pandas (18.10.2022)

  4. Intro to Machine Learning (25.10.2022)

  5. Network Analysis (8.11.2022)

  6. Introduction to Text Processing (22.11.2022)
  7. Data suitability, Data biases (29.11.2022)

Exercise-related sessions

Review meetings for exercise 2 (15 minutes for each group):

  • 13.12.2022, 9:00-16:00
  • 14.12.2022, 9:00-16:00

Exercise 2 consultation sessions in EI11 at the usual lecture times (voluntary):

  • 20.12.2022
  • 10.1.2023
  • 17.1.2023

Project presentation: 24.1.2023, 9:00-18:00


The effort breakdown is:

Python test: 3h
Lectures: 7 sessions @ 2h: 14h

    EX1 (data science process): 22h
    EX2 (project): 36h [includes review meeting (topic + questions + work plan)]

SUM: 75h



Course dates

Tue12:00 - 14:0004.10.2022 - 24.01.2023 OnlineLectures
Tue18:00 - 20:0011.10.2022FH Hörsaal 5 - TPH Python Test
Tue18:00 - 20:0011.10.2022EI 7 Hörsaal - ETIT Python Test
Mon14:00 - 16:0017.10.2022EI 7 Hörsaal - ETIT Python test 2
Mon14:00 - 16:0017.10.2022FH 8 Nöbauer HS - MATH Python test 2
Tue09:00 - 16:0013.12.2022 Discussions with Groups
Tue12:00 - 14:0020.12.2022 - 17.01.2023EI 11 Geodäsie HS - INF Ex 2 Consultation Session
Tue09:00 - 18:0024.01.2023FAV Hörsaal 3 Zemanek (Seminarraum Zemanek) Presentations
Tue09:00 - 18:0024.01.2023FAV Hörsaal 2 Presentations
Tue09:00 - 18:0024.01.2023Seminarraum FAV EG B (Seminarraum von Neumann) Presentations
Thu12:00 - 13:0026.01.2023Seminarraum FAV EG C (Seminarraum Gödel) DOPP Presentations extra
Data-oriented Programming Paradigms - Single appointments
Tue04.10.202212:00 - 14:00 OnlineLectures
Tue11.10.202212:00 - 14:00 OnlineLectures
Tue11.10.202218:00 - 20:00FH Hörsaal 5 - TPH Python Test
Tue11.10.202218:00 - 20:00EI 7 Hörsaal - ETIT Python Test
Mon17.10.202214:00 - 16:00EI 7 Hörsaal - ETIT Python test 2
Mon17.10.202214:00 - 16:00FH 8 Nöbauer HS - MATH Python test 2
Tue18.10.202212:00 - 14:00 OnlineLectures
Tue25.10.202212:00 - 14:00 OnlineLectures
Tue08.11.202212:00 - 14:00 OnlineLectures
Tue22.11.202212:00 - 14:00 OnlineLectures
Tue29.11.202212:00 - 14:00 OnlineLectures
Tue06.12.202212:00 - 14:00 OnlineLectures
Tue13.12.202209:00 - 16:00 Discussions with Groups
Tue13.12.202212:00 - 14:00 OnlineLectures
Tue20.12.202212:00 - 14:00EI 11 Geodäsie HS - INF Ex 2 Consultation Session
Tue20.12.202212:00 - 14:00 OnlineLectures
Tue10.01.202312:00 - 14:00EI 11 Geodäsie HS - INF Ex 2 Consultation Session
Tue10.01.202312:00 - 14:00 OnlineLectures
Tue17.01.202312:00 - 14:00EI 11 Geodäsie HS - INF Ex 2 Consultation Session
Tue17.01.202312:00 - 14:00 OnlineLectures

Examination modalities

It is necessary to pass the Python test at the beginning of the course to be able to complete the course. Support is available for this - see the details in the section "Previous knowledge". The self-assessment is a good indicator of what you need to know for the test. Note that only one of the two offered Python tests must be taken.

Two practical exercises. The second exercise requires a report, Jupyter Notebook, and presentation of the results.

Course registration

Begin End Deregistration end
06.09.2022 15:30 07.11.2022 23:00 18.11.2022 23:55


Study CodeObligationSemesterPrecon.Info
045 006 Digital Skills Mandatory electiveSTEOP
Course requires the completion of the introductory and orientation phase
066 645 Data Science Not specified
066 645 Data Science Mandatory1. Semester
066 646 Computational Science and Engineering Not specified
066 926 Business Informatics Mandatory elective
175 FW Elective Courses - Economics and Computer Science Elective
880 FW Elective Courses - Computer Science Not specified


No lecture notes are available.

Previous knowledge

Basic proficiency in programming with Python is expected for this lecture. A self-assessment is provided: https://github.com/tuw-python/tuw-python-2022WS/blob/main/self_assessment.ipynb

To assist in achieving the required proficiency in Python, the one week intensive course "194.123 Programming in Python" can be taken. The materials for this course are available to all and can also be worked through without attending the course.

A Python proficiency test is held at the beginning of the course. It is necessary to pass this test to be able to pass the course. Failing the Python test means that you cannot continue the course, but will not result in a negative certificate for the whole course.


Preceding courses

Accompanying courses