Program Overview

Program Overview

The University of Copenhagen offers a Master's program in Agricultural Economics and Environmental and Natural Resource Economics, with a course focused on managing and analyzing data in social science.

Course Description

The course, NIFK19006U Managing and Analyzing Data in Social Science, is designed to equip students with the skills to handle and extract information from large quantities of data. It introduces concepts, terminology, and methods relevant to handling data and spatial information in R. Students will learn to optimize data management procedures, merge datasets, clean data, and code different types of variables. The course also covers basic procedures for analysis, including tabulating statistical measures, specifying regression models, and interpreting and visualizing results.

Learning Outcomes

The aim of this course is to provide participants with tools and experience in managing and analyzing data, using cross-sectional and spatial data from the social sciences as examples. The learning outcomes include:

• Knowledge of codes required to identify different types of datasets and variables
• Understanding of principles and procedures for importing, merging, coding, transforming, and preparing data for statistical analysis in R
• Ability to apply procedures for managing different types of data in R
• Skill in implementing statistical analysis in R to derive basic cross-sectional and spatial metrics and estimate linear regression models
• Competency in formulating and implementing a strategy for solving data management and analysis problems

Literature and Recommendations

There is no obligatory literature curriculum, but relevant material will be shared through Absalon. 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 involves hands-on writing of R code, focusing on providing students with practical programming skills. Students will implement codes from packages relevant for data management as well as analysis. Learning outcomes are achieved by students individually but supported by peer groups, working on scripts with illustrative exercises.

Workload

The workload for the course is as follows:

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

Assessment

The exam involves a plenum presentation of relevant code with the objective of furthering learning, including through failed attempts to solve coding problems. Students will be assessed individually based on a short oral presentation, in plenum, of the course project taking departure in their script with data management procedures, and output of analysis such as tables, figures, and models testing their research questions and hypothesis.

Course Information

• Language: English
• Course code: NIFK19006U
• Credit: 7.5 ECTS
• Level: Full Degree Master
• Duration: The course begins on Monday, 5 August 2024, and ends on Friday, 23 August 2024
• Placement: Summer
• Schedule: Summer course, every day from 9 to 16 the first two weeks, with the third week being independent work on group assignments
• Course capacity: 40 persons

Study Board and Contracting Department

• Study Board of Natural Resources, Environment and Animal Science
• Department of Food and Resource Economics
• Faculty of Science

Course Coordinators and Lecturers

• Martin Reinhardt Nielsen
• Toke Emil Panduro and Martin Reinhardt Nielsen

Additional Information

The course is also available as continuing and professional education. The number of seats may be reduced in the late registration period. The exam is scheduled for Friday in week 34, the last day of the course. The re-exam is held as the ordinary exam, with the requirement that students must hand in a written assignment (the course project) three weeks prior to the re-exam if they have not already done so. The criteria for exam assessment include convincingly fulfilling the learning outcomes and displaying command of the packages and individual commands and procedures covered by the curriculum.