AØKK08216U Summerschool 2019: Social Data Science
The objective of this course is to learn how to analyze, gather and work with modern quantitative social science data. Increasingly, social data that capture how people behave and interact with each other is available online in new, challenging forms and formats. This opens up the possibility of gathering large amounts of interesting data, to investigate existing theories and new phenomena, provided that the analyst has sufficient computer literacy while at the same time being aware of the promises and pitfalls of working with various types of data.
In addition to core computational concepts, the class exercises will focus on the following topics
1. Gathering data: Learning how to collect and scrape data from websites as well as working with APIs.
2. Data manipulation tools: Learning how to go from unstructured data to a dataset ready for analysis. This includes to import, preprocess, transform and merge data from various sources.
3. Visualization tools: Learning best practices for visualizing data in different steps of a data analysis. Participants will learn how to visualize raw data as well as effective tools for communicating results from statistical models for broader audiences.
4. Prediction tools: Covering key implementations of statistical learning algorithms and participants will learn how to apply and interpret these models in practice.
After the course the student should be able to:
- Use computational methods and social data in the field of the state of the art social science literature.
- Use different kinds of data (survey, webbased, experimental, administrative, etc.) to answer various questions in the social sciences and have strong knowledge of advantages and challenges.
- Have an overview of key benefits and challenges of working with different kinds of social data.
- Know strengths and weaknesses of statistical prediction algorithms as well as the ability to estimate these models in practice
- Present modern data science methods needed for working with computational social science and social data in practice.
- In practice to write and debug code, to clean, transform, scrape, merge, visualize and analyze social data.
- Generating new data by collecting and scraping web pages (import and export data from numerous sources).
- Work with APIs and have basic knowledge of functional programming.
- Have strong practical data science skills and effective data visualization skills.
- Discuss ethical challenges related to the use of different types of data.
- Discuss how prediction tools relate to existing empirical tools within economics such as causal inference and regression.
The main textbooks are:
- Python for Data Analysis, 2nd ed. (2017) by Wes McKinney
- Python Machine Learning, 2nd ed. (2017) by Sebastian Raschka & Vahid Mirjalili
- Big by Bit - Social research in the digital age by Matthew J. Salganik
A comprehensive reading list as well as detailed information about the course will be available on the course website soon. For last year’s reading list see:
3 hours lecturing in 2 weeks (9 AM to 12 noon) followed by guidance in the week where the students do project work.
3 hours of exercise in the afternoon, 13-16 PM.
Timetable and venue: (Available from April 2019)
Press the link:
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-B5-5F19; [Name of course]””
-Select Report Type: "List - Week Days"
-Select Period: “Efterår/Autumn – Week 31-5”
Press: “ View Timetable”
Registration and information for students not enrolled please find more information at Study Economics.
- 7,5 ECTS
- Type of assessment
- Written assignment, 7 days- project paper. The project can be written individually or in groups of 3 to 4 participants.
The project paper must be answered in English. The plagiarism rules must be complied and please be aware of the rules for co-writing assignments.
Update 21-1-2019: The groups are randomly assigned at the beginning of the course.
- Exam registration requirements
Full participation at the summerschool is mandatory and the student must actively participate in all activities.
Students are expected to complete at least 2 out of 3 mandatory assignments.
The project description must have been handed in before the deadline given by the lecturers.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
The exam can be selected for external assessment.
- Exam period
The exam takes place August 23, 2019 from 15 PM to August 30 at 15 PM.
Note: In special cases, the exam date can be changed to another day and time within the exam period.
The written reexam will take place in the period December 2019 - January 2020
Note: If only a few students have registered the reexam it might change to oral including the date, time and place, which will be informed in KUNet or by the Examination Office.
Criteria for exam assesment
Students are assessed on the extent to which they master the learning outcome for the course.
To receive the top grade, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
- Project work
- Class Instruction