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:
In addtion, three exercises will be done.
The effort breakdown is:
Python tutorial: 4hLectures: 7 sessions @ 2h: 14hExercises: EX1 (OO vs. DO): 5h EX2 (pandas + sklearn): 10h EX3 (project): 42h [includes review meeting (topic + questions + work plan)]SUM: 75h
All Lectures on Tuesday 11:30st-13:00. Lectures in the Main Building HS6.
Kickoff-Session, data science process, community, solution examples [Hanbury] (9.10)
Python tutorial [Böck] (23.10)
SciPy, NumPy, vectorisation, visualisation, benchmarking [Böck] (30.10)
Preprocessing, Pandas [Kiesling] (6.11)
Intro to Machine Learning/sklearn [Hanbury] (13.11)
Exercise-related sessions
Review meetings for exercise 3. 18.12.2018, 14:00-18:00 (15 minutes for each group)
Project presentation. 21.1.2019 in Hörsaal 6, 9:00-16:00
Ex1, Ex2: 1..100 points. Minimum 35.
Grade=0.25*Ex1+0.75*Ex2. Minimum 50.