85008 - Big Data For The Social Sciences

Course Unit Page

SDGs

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

Quality education Gender equality Decent work and economic growth

Academic Year 2021/2022

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 students how the large volume of data (Big Data), that is becoming increasingly available, can be exploited 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 such as Pandas, Matplotlib, NLTK, for data analysis and visualization of social data, including Twitter and/or other social media.

Readings/Bibliography

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 [http://sol.unibo.it/SebinaOpac/query/python%20for%20dummies?context=catalogo]
  • 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/

For those who are not familiar with 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.

In addition, students will be using class notes.

Teaching methods

Theoretical lectures, hands-on coding exercises and homework. 

Assessment methods

Students (organised in two-person teams) will be required to write a short paper on a topic of their choice by applying the methodologies and techniques learned throughout the course. 

 During the last two sessions of the course, students can discuss their research idea with the instructor and colleagues.

The final product (paper) should be submitted about two weeks after the end of the class.

Teaching tools

  • Python programming language (ANACONDA distribution)
  • Jupyter Notebook/Jupyter Lab
  • Google Colab

Office hours

See the website of Oltion Preka

See the website of Enzo Loner