85008 - Big Data For The Social Sciences

Course Unit Page

  • Teacher Oltion Preka

  • Credits 8

  • SSD SPS/04

  • Teaching Mode Traditional lectures

  • Language English

  • Course Timetable from Feb 19, 2020 to May 28, 2020


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Industry, innovation and infrastructure

Academic Year 2019/2020

Learning outcomes

By the end of the course, studemts will be alble to: 1) Understand the foundations of big data, including it’s foundations in computing technology and statistics. 2) Understand the social implications of increased knowledge, surveillance, and behavioral prediction made possible by big data, and the ethical tradeoffs faced. 3) Demonstrate the ability to formulate specific study questions concerning cybersth use of big datay. 4) Understand accepted tools and practices concerning the use of the internet for social purposes. 5) Demonstrate the ability to communicate complex concepts to multidisciplinary teams including students from computing and international affairs backgrounds. 6) Be familiar with text mining techniques.

Course contents

The course aims at teaching the students how the 'huge' volume of big data (BD) increasingly available can be useful to better understand and explain social phenomena. More specifically, throughout the course, we will be exploring the applications of PYTHON, a general purpose programming language and its main packages (Pandas, Matplotlib, NLTK) to large amounts of social data, including Twitter and/or other social media. 


These are required readings for the class:

  • Mueller, John Paul (2014) Beginning programming with Python for Dummies, eBook available free for all students here link
  • Hanneman, R. A. & Riddle M. (2005) Introduction to Social Network Methods. Riverside, CA: University of California, Riverside (chapters 1,2,3,5,6,7,10,11). Available (free) online here: http://faculty.ucr.edu/~hanneman/
  • in addition, students are expected to use their class notes
  • this one is suggested reading for those who know little on Big Data in general (very easy reading): Mayer-Schonberger V. & Cukier C. (2014) Big Data: A Revolution that will transform how we live, work and think, Eamon Dolan/Mariner Books.

Teaching methods

Lectures, in-class exercises and homework. 

Assessment methods

Students will be required to choose a research project (for a two-person team) about a topic they are really interested in. In the last two sessions of the course, all students will have to present their projects and the final product (final paper) will have to be emailed (in PDF format) to the instructor two weeks after the end of the class.

Teaching tools

  • PYTHON programming language (ANACONDA distribution or other)
  • Jupyter Notebook 
  • NLTK

Office hours

See the website of Oltion Preka