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2020/2021  BA-BSOCV1010U  Digital Society C. Computing in Social Science Research – Introduction to R

English Title
Digital Society C. Computing in Social Science Research – Introduction to R

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Quarter
Start time of the course Second Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 60
Study board
Study Board for BSc in Business Administration and Sociology
Course coordinator
  • Bo Hee Min - Department of Management, Politics and Philosophy (MPP)
Main academic disciplines
  • Information technology
  • Sociology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 07-02-2020

Relevant links

Learning objectives
  • Formulate a research questions and identify appropriate data for the research question
  • Devise a data management and analysis plan
  • Understand data structure and programming fundamentals
  • Construct and manage research data in R: clean, merge, restructure, and document data for analysis
  • Write and document a program in R to conduct the descriptive analysis and visualization of data
  • Present data and analysis result
Digital Society C. Computing in Social Science Research - Introduction to R:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 20 pages
The exam essay is a presentation of research questions, data, and analysis results. It consists of text, tables, and graphs.
Students are required to electronically upload their data and program codes that they used to prepare the exam essay. The data and codes will be graded as part of the exam but not count toward the page limit.
Assignment type Report
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
Description of the exam procedure

Students will use the data and code templates that the instructor provides to conduct descriptive data analysis and data visualization for their research questions. Students will be evaluated based on their data management and computing skills demonstrated in the research project report, data, and codes. The research project report will be examined for the research question, selection and construction of data and analysis, and presentation of data and analysis results. The data files and codes will be evaluated for the students’ skills in data preparation, data management, computing, and documentation.

Course content, structure and pedagogical approach

R is a free data analysis software that has been gaining popularity among social scientists and industry practitioners due to its support of statistical analysis, data visualization, text analysis, machine learning, and artificial intelligence. This course aims to help students to develop understanding in the digital society and to learn the necessary skills in R to adapt to the changes in social science research that reflect the societal changes. The course is designed to provide basic competency in digital data and computing in R that can be useful for both social science research projects and real-world applications.

This course includes demonstration and hands-on exercises from which students will get basic training in digital social science research skills and practical skills. Students will learn fundamentals in data and programming for social science research: data types, data structure, variables, syntax, and functions. They will gain skills in applying those fundamentals in R to conduct descriptive analysis and visualize data. Besides, students will learn practical skills for the presentation of data and analysis results and project management including data management and documentation.

Description of the teaching methods
The course consists of lectures, demonstrations, and hands-on exercises. Lectures will provide students with background knowledge in social science research and fundamentals in digital data and computing. Demonstrations include the skills and codes for data management, analysis, data visualization, and documentation. In the exercises, students will have hands-on experience in applying their data management and computing skills.
As a blended learning course, the course website will provide learning resources that students can use for their research projects.
Feedback during the teaching period
During exercises, the instructor will provide face-to-face assistance and feedback to students. Students will also get peer-to-peer feedback in the exercise classes. The instructor will give written or electronic feedback, such as additional data and codes, for students’ research projects. The instructor will be available for individual feedback during office hours and may prompt students to attend office hours based on the learning progress.
Student workload
Class teaching 36 hours
Preparation for classes 96 hours
Exam 74 hours
Expected literature

Main textbook: Richard Cotton, Learning R, O'Reilly.

Last updated on 07-02-2020