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Big Data in Society
OVERVIEW
CEA CAPA Partner Institution: Vrije Universiteit Amsterdam
Location: Amsterdam, Netherlands
Primary Subject Area: Computer Sciences
Instruction in: English
Transcript Source: Partner Institution
Course Details: Level 300
Recommended Semester Credits: 3
Contact Hours: 45
Prerequisites: Since some understanding of social scientific approaches is required, you should previously have studied research methods and methodology.
DESCRIPTION
More and more data is being collected about us and our activities. Our social media interactions, online search and shopping behaviour, but also offline location tracking and sensor and surveillance networks are all generating large, often real-time data sets. Potentially, these could help answer important questions facing society today. However, processing big data techniques for social problems in a responsible way within ethical boundaries represents an interdisciplinary challenge at the interface of social and computer science. We will work in small groups to address specific issues and do some hands-on analysis. The course is organized around two modules in which big data techniques are used to answer social questions or solve social problems:
In the module big data, you will look at the way such data are shaping society. For example, recommendation algorithms are determining more and more which songs you listen to, which books you buy, and what news you watch. These recommendations are based on your past behavior but also on data about your friends and other users. These algorithms can help you discover new interests and find items form the 'long tail', but they can also cause you to become stuck in a filter bubble of similar items. In this module we will look at existing algorithms and write your own algorithm based on publicly available shopping or review data. Other topics covered are introduction in R, machine learning, and automated text analysis.
In the module Digital society, you will look at publicly available data such as news items, social media messages and online reviews. Zooming in on a specific theme that has sparked public contention, for example health care or sustainability issues, you will use network analysis and other methods to further investigate the data from the first module. Zooming out, we will discuss the implications of big data on society. Specifically, we will discuss CSR, ethics and methodological challenges.
Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course. CEA's recommended credits are based on the contact hours assigned by Vrije Universiteit Amsterdam (VU Amsterdam): 15 contact hours equals 1 U.S. credit
In the module big data, you will look at the way such data are shaping society. For example, recommendation algorithms are determining more and more which songs you listen to, which books you buy, and what news you watch. These recommendations are based on your past behavior but also on data about your friends and other users. These algorithms can help you discover new interests and find items form the 'long tail', but they can also cause you to become stuck in a filter bubble of similar items. In this module we will look at existing algorithms and write your own algorithm based on publicly available shopping or review data. Other topics covered are introduction in R, machine learning, and automated text analysis.
In the module Digital society, you will look at publicly available data such as news items, social media messages and online reviews. Zooming in on a specific theme that has sparked public contention, for example health care or sustainability issues, you will use network analysis and other methods to further investigate the data from the first module. Zooming out, we will discuss the implications of big data on society. Specifically, we will discuss CSR, ethics and methodological challenges.
Contact hours listed under a course description may vary due to the combination of lecture-based and independent work required for each course. CEA's recommended credits are based on the contact hours assigned by Vrije Universiteit Amsterdam (VU Amsterdam): 15 contact hours equals 1 U.S. credit
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