Social Data Science
Study Board of Political Science, Journalism, Sociology, and European Studies
Teaching language: Danish, English
EKA: B380017102
Censorship: Second examiner: None
Grading: 7-point grading scale
Offered in: Odense
Offered in: Summer school (autumn)
Level: Master
Course ID: B380017101
ECTS value: 10
Date of Approval:
Duration: 1 semester
Course ID
Course Title
Teaching language
ECTS value
Responsible study board
Course Responsible
Offered in
Level
Offered in
Duration
Mandatory prerequisites
Recommended prerequisites
Students are expected to be open-minded and willing to invest in learning the soft-ware R, which is a powerful tool in which a variety of data science methods can be implemented. Furthermore students are expected to invest a substantial amount of time in acquiring the necessary programming skills so that we can focus on implementing various techniques.
Aim and purpose
In order to understand, produce and consume the next generation of social science studies students need to know how to implement and interpret data science methods.
The purpose of this course is to introduce students to web based data gathering techniques, some text analyses and to simple implementations of machine learning. These tools have proven to be extremely power full and some of them are directly responsible for the automatization wave that is currently remolding and restructuring many industries including the public sector. Hence understanding how and what different models do is essential for students who want to be prepared for a labor market, which increasingly demands knowledge of where efficiency can be gained by implementing data science methods.
The course begin by an introduction to R but as already stipulated it is expected that students work on their programing skills outside class.
Content
The teaching language is English, unless all students (and the course’s teachers and student instructors) are proficient in Danish (in this case the teaching language will be changed to Danish).
The course starts with an intensive introduction to R and Rstudio. We shall learn how to import and export data, how to structure data and how to plot data. From there we now to visualization of data using the ggplot2 package.
After this introductory phase we move to a how to gather data from the web using the Rvest package. The next elements are concerned with introduction to automated text analyses and to simple introduction to machine learning methods.
Learning goals
To meet the goal of the course, students at the end of the course should have:
Knowledge that enables the student to:
• Understand the principles of data science and the assumptions behind select statistical methods.
• Discuss assumptions behind select data science methods
• Evaluate whether these assumptions are realistic and suggest solutions for breaches.
• Interpret results from selected statistical methods.
Skills that enables students to:
• Analyze and critically assess scientific articles that use data science methods taught in the course.
• Select appropriate data science methods for own research questions.
Competences that enables students to:
• Implement data science methods using R.
• Correctly select and interpret the quantities of interest resulting from the application of these methods and link these results to research goals.
Literature
The course will use chapters from various textbooks. Examples:
- R for Data Science; Wickham and Grolemund (2015).
- Machine Learning with R, Lantz, R. (2013) or an alternative, such as Practical Data Science with R.
In addition, 1-3 applied articles will be assigned for most lectures. Students are expected to read these materials prior to the class.
Teaching Method
The course is an intensive summer course comprising two weeks of lecturing in the last weeks of August.
Depending on who many english speaking students participate in the class we will be speaking either Danish or English as a working language
Ordinary class discussions (10 classes of 3x45 minutes) where focus is on developing code
This course is based on active learning, involving a high degree of individual and group interaction and class participation.
There will also be additional learning activities in order to further establish the empirical relevance of the methods introduced during the course. These activities can, for example, take the form of guest lectures or be in the form of methods/statistics related puzzles based on material from the media (e.g., trends in party support) or cases on methodological issues. The students may be required to present their findings to the class.
Workload
Examination regulations
Exam
Name
Exam
Timing
Ordinary examination in September. Re-examination in October.
Rules
-3 is not allowed
Tests
Exam
Name
Exam
Form of examination
Take-home assignment
Censorship
Second examiner: None
Grading
7-point grading scale
Identification
Student Identification Card - Exam number
Language
Danish, English
Duration
one week (7 days)
Length
Max. 10/15 pages (10 when written alone and 15 pages when written with one other student) (spacing, appendix notes, tables and figures included, but table of contents, list of literature, appendix with syntax and appendix with definitions and descriptive statistics excluded)
Examination aids
All exam aids are permitted.
Assignment handover
Via Blackboard.
Assignment handin
Via Blackboard.
ECTS value
10
Additional information
Students can choose between graded assessment or pass/not passed.
Internet access necessary.
EKA
B380017102
External comment
The student is automatically registered for the first examination attempt when the student is registered for a course or course element with which one or more examinations are associated. Withdrawal of registration is not possible, and students who fail to participate in an examination have used one examination attempt, unless the University has made an exemption due to special circumstances.
If a student does not meet the established university prerequisites for taking the exam, he or she has used one examination attempt, unless the University has made an exemption due to special circumstances.
Courses offered
| Offer period | Offer type | Profile | Education | Semester |
|---|---|---|---|---|
| Fall 2018 | Optional | Studieordning for kandidatuddannelsen i statskundskab gældende for studerende optaget efter 1/9-15 | Master of Science (MSc) in Political Science | Odense | |
| Fall 2018 | Optional | Sidefag på kandidatniveau i Samfundsfag, gældende fra og med 2015 | Master of Science (MSc) in Social Sciences | Odense | |
| Fall 2018 | Optional | Sidefag på kandidatniveau i Samfundsfag, gældende fra og med 2018 | Master of Science (MSc) in Social Sciences | Odense | |
| Fall 2018 | Optional | Studieordning for kandidatuddannelsen i samfundsfag, centralfag, 2018, ved et sidefag på 75 ECTS: | Master of Science (MSc) in Social Sciences | Odense | |
| Fall 2018 | Optional | Studieordning for kandidatuddannelsen i samfundsfag, centralfag, 2018, ved et sidefag på 45 ECTS, for studerende der har haft Economic Principles (soc) eller tilsvarende på deres bacheloruddannelse | Master of Science (MSc) in Social Sciences | Odense | |
| Fall 2018 | Optional | Studieordning for kandidatuddannelsen i samfundsfag, centralfag, 2018, ved et sidefag på 75 ECTS, for studerende der har haft Economic Principles (soc) eller tilsvarende på deres bacheloruddannelse | Master of Science (MSc) in Social Sciences | Odense | |
| Fall 2018 | Optional | Studieordning for kandidatuddannelsen i samfundsfag, centralfag, 2018, ved et sidefag på 45 ECTS | Master of Science (MSc) in Social Sciences | Odense | |
| Fall 2018 | Optional | Sidefag på kandidatniveau i Samfundsfag, gældende fra og med 2018 | Master of Science (MSc) in Social Sciences | Odense |