Data-oriented Programming Paradigms

Submitted by webmaster on Wed, 10/03/2018 - 14:24
Course No: 
188995
Course Type: 
VU
Term: 
2018W
Weekly Hours: 
2.0
Lecturer: 
Sebastian Böck
Elmar Kiesling
Allan Hanbury
Language: 
English
Objective: 

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.

Content: 

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
  • Data Science solution approaches and case studies
  • Introduction to network analysis

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

Information: 

Syllabus
All Lectures on Tuesday 11:00-13:00, Seminarraum Gödel, Favoritenstraße 9
 

  1. Kickoff-Session, data science process, community, solution examples [Hanbury] (9.10)
  2. Introduction to DOPP, text stream processing [Böck] (16.10)
  3. Python tutorial [Böck] (23.10)
  4. SciPy, NumPy, vectorisation, visualisation, benchmarking [Böck] (30.10)
  5. Preprocessing, Pandas [Kiesling] (6.11)
  6. Intro to Machine Learning/sklearn [Hanbury] (13.11)
  7. Network Analysis [Hanbury] (27.11)

Exercise-related sessions
Review meetings for exercise 3. 18.12.2018, 14:00-18:00 (15 minutes for each group)
Project presentation. 22.1.2019 in Seminarraum Gödel, 11:00-15:00

Notes: 
Examination: 

<p>Ex1, Ex2: 1..100 points. Minimum 35.</p>
<p>Grade=0.25*Ex1+0.75*Ex2. Minimum 50.</p>

Recommendation: