This lecture covers the basic programming approaches in Data Science. The emphasis is on computational thinking, the formulation of problems and their solution spaces so that a computer can solve them. Methods for increasing the efficiency of the solutions are also presented. Use cases demonstrate the practical application of data science solutions.
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
Syllabus
All Lectures on Tuesday 11:00 c.t.-12:45. Lectures in the Main Building HS6.
- Kickoff-Session, data science process, community, solution examples [Hanbury] (8.10.2019)
- Introduction to DOPP, text stream processing [Böck] (15.10.2019)
- Python tutorial [Böck] (22.10.2019)
- SciPy, NumPy, vectorisation, visualisation, benchmarking [Böck] (29.10.2019)
- Preprocessing, Pandas [Piroi] (5.11.2019)
- Intro to Machine Learning [Hanbury] (19.11.2019)
- 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
<p>Three practical exercises. The third exercise requires a report, Jupyter Notebook, and presentation of the results.</p>