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EC994 - Applications of Data Science

  • Module code: EC994
  • Module name: Applications of Data Science
  • Department: Economics
  • Credit: 18

Content and teaching | Assessment | Availability

Module content and teaching

Principal aims

"Big data is transforming almost every aspect of science and the humanities, driven by the emergence of a data society. This is a society in which increasingly comprehensive aspects of human behaviour and the economy are recorded as data. Employers are recognizing the need for a skilled workforce that can extract value from data, giving rise to the new job description of a data scientist. This course aims to provide economists and social scientist with a solid basis to overcome the deep technical deficit that has been identified among social scientists in the methodologies and practical tools of data science (Rebekah Luff, Rose Wiles and Patrick Sturgis, “Consultation on Methodological Research Needs in UK Social Science”, National Centre for Research Methods, March 2015.) The aim of this module is provide students with a thorough understanding of the most common statistical methods related to high-dimensional data and machine learning techniques, with a particular focus to applications on economic and social data. The course will cover both the theory underpinning these methods and will also feature an intensive applied computing component. "

Principal learning outcomes

The module should provide students with a theoretical understanding of the range of data science methods; critically evaluate the appropriateness of statistical methods and an ability to apply these methods to social and economic data.

Timetabled teaching activities

18 Lecture hours.

Departmental link

https://www2.warwick.ac.uk/fac/soc/economics/current/modules/ec994

Module assessment

Assessment group Assessment name Percentage
18 CATS (Module code: EC994-18)
B (Examination only) 2 hour examination (May) 100%

Module availability

This module is available on the following courses:

Core

N/A

Optional Core

N/A

Optional

N/A