880254 :Research Skills: Data Processing

Algemeen

Voertaal Engels
Werkvorm: Lectures and assignments (Collegerooster)
Tentamenvorm: Midterm + computer exam (Tentamenrooster)
Niveau:Master
Studielast:3 ECTS credits
Inschrijving:Enrollment via Blackboard before start of lectures
Blackboard informatieLink to Blackboard (Als u de melding 'Guest are not allowed in this course' krijgt, dient u nog bij Blackboard in te loggen)

Docent(en)


dr. A. Alishahi (coordinator (fall semester))

M. de Haas MSc (coordinator (spring semester))

C.H. van Heck MSc


Doel van de cursus (alleen in het Engels beschikbaar)

This course teaches students to:
  • analyse problems from the perspective of solving them with a computer
  • use the language Python to implement computer programs
  • test computer programs to ensure their correct functioning


Inhoud van de cursus (alleen in het Engels beschikbaar)

The growth of the internet and associated services has enabled the investigation of large text and data corpora. The processing and analysis of these corpora using statistical methods is difficult because of the enormous volumes of data. A computer is a powerful tool to perform such analyses. This course provides students with practical skills to use computers as general tools for working with data sets and solving quantitative problems. The skills acquired are particularly helpful for reducing the time spent on data analysis in research (e.g., for Master thesis work).

The students will learn the basics of the computer language Python, which is suitable for quickly creating short programs to process texts and data sets. Python is a language that is easy to use by novices, yet sufficiently powerful to create programs of any size and complexity. Moreover, while being a complete, free-to-use, flexible, operating-system-independent language of its own, it also constitutes a strong basis to learn any other computer language from.
Please note: Students who completed the third-year course 822235 Seminar Data Processing, or 827154 Advanced Programming, must choose a different OZV/Research Skills module, as this course does not offer anything new to them.

Students of the mastertrack DSBG, who already passed 822235 Seminar Data Processing or 827154 Advanced Programming CIS, have to file a request to the Board of Examiners in order to be assigned an alternative Research Skills course.


Bijzonderheden (alleen in het Engels beschikbaar)

The course is presented as a series of iPython notebooks that will be provided on Blackboard. Students get an account on a server at the university, which makes all these notebooks available. The notebooks consist of instructions and exercises that can be done inside the notebooks or using a separate editor. In principle the notebooks contain all necessary information, and if they want students can do the course completely without further instructions if they simply follow the notebooks.

The course will be evaluated via a midterm computer exam and a final computer exam. There will be one in-class computer-based quiz that will not count for the final grade, but is a requirement to be allowed to enter the computer exam. Every week take-home exercises will be handed out, for which the students should submit solutions in the form of programs. These solutions will not count for the final grade and are optional. The main goal of the take-home assignments is to assure that the students keep working at a steady pace, and for the instructor to see where there are still problems.


Verplichte literatuur

  1. Course material provided on Blackboard.


Aanbevolen literatuur

  1. Pieter Spronck, The Coder\'s Apprentice, 2016. This book will be made freely available as a PDF on blackboard
  2. Allen Downey, Think Python 2nd edition,, Green Tea Press,, 2015. This book is published as a PDF file under the GNU Free Documentation License. It is freely available via http://greenteapress.com/thinkpython2/thinkpython2.pdf.


Vereiste voorkennis

None, though an aptitude for abstract thinking is helpful.


Verplicht voor

  • Data Science: Business and Governance ( 2015, 2016, 2017 )
  • Data Science: Business and Governance (voorjaar) ( 2017 )


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(01-feb-2018)