Data-oriented Programming Paradigms

Submitted by webmaster on Thu, 10/08/2020 - 11:17
Course No: 
188995
Course Type: 
VU
Term: 
2020W
Weekly Hours: 
2.0
Lecturer: 
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 with Pandas
  • Data Science solution approaches and case studies
  • Introduction to machine learning
  • Introduction to network analysis
Information: 

The link to the online lectures is on TUWEL.
 
Syllabus
All Lectures on Tuesday 12:00 c.t.-13:45.

  1. Kickoff-Session, data science process, community, solution examples, Python introduction [Hanbury] (6.10.2020)
  2. Introduction to DOPP [Hanbury] (13.10.2020)
  3. SciPy, NumPy, vectorisation, visualisation, benchmarking [Piroi] (27.10.2020)
  4. Preprocessing, Pandas [Piroi] (3.11.2020)
  5. Intro to Machine Learning [Hanbury] (17.11.2020)
  6. Network Analysis [Hanbury] (1.12.2020)

Exercise-related sessions
Review meetings for exercise 3 (15 minutes for each group):

  • 15.12.2020, 9:00-13:00
  • 16.12.2020, 9:00-11:00 and 13:00-15:00

Project presentation: 27.1.2020, 9:00-16:00
 
The effort breakdown is:
Python tutorial: 4hLectures: 7 sessions @ 2h: 14hExercises:     EX1 (data wrangling): 5h    EX2 (pandas + sklearn): 10h     EX3 (project): 42h [includes review meeting (topic + questions + work plan)]SUM: 75h

Notes: 
Examination: 

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

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