Program Overview

Program Overview

The University of Copenhagen offers a course titled "Managing and Analyzing Data in Social Science" (NIFK19006U). This course is designed to equip students with the skills to handle and extract information from large quantities of data, a crucial skill for their future careers and Master's thesis projects.

Course Description

The course introduces students to concepts, terminology, and methods relevant to handling data and spatial information in R and QGIS. Students will learn to manage data, clean and code variables, and overlay spatial layers. They will also be introduced to basic procedures for testing hypotheses, including tabulating statistical measures, specifying regression models, and interpreting and visualizing results.

Learning Outcomes

The course aims to provide students with the tools and experience in managing and analyzing data, with a focus on socioeconomic and spatial data. Upon completion, students will be able to:

• Describe different types of datasets and variables
• Explain principles of good conduct in relation to data storage, documentation, and anonymization
• Apply procedures for managing different types of data in R and QGIS
• Combine different data sets and produce composite maps
• Develop research questions and hypotheses
• Implement statistical analysis in R
• Interpret, visualize, and present statistical results

Literature

The course uses various literature, including:

• Paradis, E.: 2005, R for beginners
• Ricci V.: 2005 - R Functions For Regression Analysis
• Thiede R., Sutton T., Düster H., Sutton M.: 2014, Quantum GIS Training Manual Release 1.0
• Abedin, J., & Das, K. K. (2015). Data Manipulation with R
• Osborne, J. W. (2012). Best practices in data cleaning

Recommended Academic Qualifications

A basic statistics course is recommended, and some experience with R and insight into simple data management and analysis is expected. Academic qualifications equivalent to a BSc degree are recommended.

Teaching and Learning Methods

The course combines lectures, individual work, and group work. Practical and theoretical considerations are presented in lectures, supported by relevant examples. Students will work on exercises and accumulate a command library for relevant tasks applicable to a similar data management and analysis project.

Workload

The course workload is distributed as follows:

• Lectures: 30 hours
• Preparation: 40 hours
• Practical exercises: 40 hours
• Project work: 96 hours
• Total: 206 hours

Assessment

The course is assessed through an oral examination, where students will present their own developed research hypothesis, script with data management procedures, and output of analysis. The exam is conditional on handing in a written group assignment.

Course Information

• Language: English
• Course code: NIFK19006U
• Credit: 7.5 ECTS
• Level: Full Degree Master
• Duration: The course begins on Monday, August 2, and ends on Friday, August 20
• Placement: Summer
• Schedule: Every day from 9 to 16 the first two weeks, with the third week being independent work on group assignments
• Course capacity: 30 persons

Contracting Department and Faculty

• Department: Department of Food and Resource Economics
• Faculty: Faculty of Science

Course Coordinators

• Martin Reinhardt Nielsen