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.

The following topics are covered in the lectures:

• Introduction to Data-Oriented Programming Paradigms
• Python
• 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

The effort breakdown is:

Python tutorial: 4h
Lectures: 7 sessions @ 2h: 14h
Exercises:
    EX1 (OO vs. DO): 5h
    EX2 (pandas + sklearn): 10h
    EX3 (project): 42h [includes review meeting (topic + questions + work plan)]
SUM: 75h

Syllabus

All Lectures on Tuesday 11:00 c.t.-12:45. Lectures in the Main Building HS6.

1. Kickoff-Session, data science process, community, solution examples [Hanbury] (8.10.2019)

2. Introduction to DOPP, text stream processing [Böck] (15.10.2019)

3. Python tutorial [Böck] (22.10.2019)

4. SciPy, NumPy, vectorisation, visualisation, benchmarking [Böck] (29.10.2019)

5. Preprocessing, Pandas [Piroi] (5.11.2019)

6. Intro to Machine Learning [Hanbury] (19.11.2019)

7. Network Analysis [Hanbury] (3.12.2019)
   

Exercise-related sessions

Review meetings for exercise 3: 17.12.2019, 12:00-18:00 (15 minutes for each group) - Meeting Room HC 01 15, Favoritenstraße 9, 1st Floor

Project presentation. 27.1.2020 in Hörsaal 6, 9:00-16:00

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