822047 :Data Science for Humanities (CSAI/HAIT/BDM/T&C)

Algemeen

Voertaal Engels
Werkvorm: Online, plenary and practical sessions (Collegerooster)
Tentamenvorm: written exam, 2 assignments (Tentamenrooster)
Niveau:Bachelor
Studielast:6 ECTS credits
Inschrijving:Inschrijven via Blackboard voor aanvang colleges
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)

No photo available
dr. M. Postma-Nilsenova (coordinator)


Doel van de cursus

This course provides an introduction to Data Science for students who are interested in using it to analyze quantitative data in communications and humanities research.

Course objectives:
  • students acquire basic terminology in the field of Data Science;
  • students learn what Data Science is and how its methods differ from more familiar kinds of statistics;
  • students are introduced to the main methods and tools within Data Science;
  • students are provided with examples of data analyses;
  • students learn about some pitfalls to avoid while doing Data Science;
  • at the end of the course, students understand in general terms what Data Science is and what it can be used for;
  • students are able to pick up appropriate Data Science tools for particular tasks and to interpret their output.


Inhoud van de cursus

The course is organized as a combination of plenary meetings and practical sessions. In the lectures, we discuss theories and research literature related to the main course topics. During the practical sessions, we practice setting up a data mining flow in different programming environments (no prior knowledge of programming required).

The course provides the basis for highly relevant skills currently sought after by companies and organizations dealing with large data sets. Students gain hands on experience by analyzing real world data sets and by discussing real-world cases.


Bijzonderheden

  • In this course students are expected to have working knowledge of methodology and statistics; students should be able to work with SPSS or another standard statistical package. 
  • Students are not expected to have any prior knowledge of programming.
  • The final grade for this course is the weighted average of two assignments (10% each) and a final exam (80%). In order to pass the course, both the average grade AND the grade of the individual components must be sufficient. Resits are only allowed for the final exam.


Verplichte literatuur

  1. Attewell, P., Monaghan, D.B., Kwong, D, Data Mining for the Social Sciences: An Introduction, University of California Press., 2015). (selected chapters)
  2. Juola, P., & Ramsay, S., Six Septembers: Mathematics for the Humanist. Digital Commons, University of Nebraska-Lincoln, 2017. http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1055&context=zeabook (selected chapters)
  3. Selected journal articles, announced via Blackboard at the start of the course.


Vereiste voorkennis

Courses in basic statistics and methodology


Verplicht voor

  • Bachelor CIW: Cognitive Science and Artificial Intelligence ( 2017 )
  • Bachelorminor: CIW voor Online Culture ( 2017 )


Mogelijk interessant voor

  • Premaster Communication and Information Sciences ( 2016, 2017 )
  • Bachelor CIW: Bedrijfscommunicatie en Digitale Media ( 2014, 2015, 2016, 2017 )
  • Bachelor CIW: Tekst en Communicatie ( 2014, 2015, 2016, 2017 )
  • Bachelor CIW: Interculturele Communicatie ( 2014 )
  • Bachelor CIW: Human Aspects of Information Technology ( 2014, 2015, 2016 )
  • Bachelor CIW: New Media Design ( 2017 )

(18-jul-2017)