Summerschool 2021: Introduction to Social Data Science
Course content
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.
MSc programme in Economics – elective course
Bacheloruddannelsen i økonomi – valgfag efter 2. år
The Danish BSc programme in Economics - elective course after the 2. year
After completing the course the student is expected to be able to:
Knowledge:
- Understand use cases for different kinds of data (survey, webbased, experimental, administrative, etc.) to answer various questions in the social sciences.
- Account for benefits and challenges of working with different kinds of social data.
- Identify and account for strengths and weaknesses of linear statistical prediction algorithms and estimate these models in practice.
- Discuss ethical challenges related to the use of different types of data.
- Discuss how prediction tools relate to existing empirical tools within social sciences such as linear regression for inference.
Skills:
- Program in basic Pythion, write and debug code.
- Use data manipulation and data visualization to clean, transform, scrape, merge, visualize and analyze social data.
- Generate new data by collecting and scraping web pages (import and export data from numerous sources) and work with data APIs.
- Apply and interpret machine learning algorithms and models in practice.
- Conceptualize and execute basic projects in social data science
Competences:
- Independently master and implement computational methods and methods for working with social and behavioral data in the social science literature.
- Present modern data science methods needed for working with computational social science and social data in practice.
The course will in the two first weeks consist of lectures and
exercises with problem solving. The lectures will focus on the
broad topics covered in the course. In the exercise classes we will
get our hands dirty and present data science methods needed for
collecting and analyzing real-world data. The student must be aware
that the exercises do not have a large amount of time for learning
how to code.
The third week of the summer school will consist of peerfeedback,
guidance and project writing.
Note: Due to the Corona crisis, the lectures and exercises may be
conducted online or part online/part fysically at campus. Please
consult Absalon to be informed of the teaching, schedule and
changes.
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:
This course is available to students and practitioners who are
interested in social data science.
Because the course builds on a wide range of techniques, we do not
have any hard requirements. However, we expect that students have
acquired basic programming skills with Python before teaching
begins. Such proficiency can come either from prior knowledge or by
following our integrated learning module, “Assignment 0”. Last
year’s version of the module is available at:
https://github.com/abjer/isds2020/blob/master/assignments/assignment0/assignment_0.ipynb
We estimate this module can be completed in around 50 hours with no
prior knowledge. In addition to programming skills, we recommend
students to be familiar with essential elements of regression
analysis (e.g. Econometrics I or similar).
Schedule:
In the first and second week:
3 hours lecturing, 9 AM to 12 noon
3 hours of exercise 1 PM to 4 PM (13-16).
In the third week (Mondag to Wednesday):
The students participate in peer feedback and the students can
groupevise participate in meetings with the TAs for guidance of the
project.
Timetable and venue:
To see the time and location of classrooms please press the link
under "Timetable"/"Se skema" at the right side
of this page.
You can find the similar information in English at
https://skema.ku.dk/ku2122/uk/module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for
respond)
-Select Module:: “2200-B5-5F21; [Name of course]”
-Select Report Type: "List - Week Days"
-Select Period: “Efterår/Autumn – Week 31-5”
Press: “ View Timetable”
Please note:
- That it is the student´s own responsibility to constantly be
aware of and search for information about the study, teaching,
schedule, exam etc. through the study pages, the course
description, the digital exam portal, Absalon, KUnet, myUCPH app,
curriculum etc.
- That if the Corona crisis continue, the lectures and exercises
may be conducted online or part online/part fysically at campus.
Please consult Absalon to be informed of the teaching and changes
to the original schedule.
The students receive:
- Written feedback from assignments (correction and solution).
- Written feedback from responses to quizzes.
- Oral feedback and supervision sessions by TAs.
- Feedback by their peers on the project assignment.
For enrolled students: Registration, information, rules etc: Master(UK), Master(DK) and Bachelor(DK)
For foreign students: Admissionrequirements, registration etc: Study Economics. Read the curriculum before enrolment.
For gæste- og enkelfagsstuderende: Tilmelding via Uddannelse i Økonomi.
Læs venligst studieordningen og uddannelsen inden tilmeldning.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written assignment, 7 daysThe exam is a project paper. The project can be written individually or in groups of 3 to 4 participants. The students can give peer feedback to the project assignment of each other.
Please be aware of:
- The rules for co-writing assignments as stated in the curriculum.
- The plagiarism rules must be complied.
- The project paper must be written in English.
- The groups are randomly assigned at the beginning of the course.
____ - Aid
- All aids allowed
for the written exam.
In case of an oral reexam, please go to the section "Reexam" for further information about allowed aids.
__
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
___
Criteria for exam assessment
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.
To pass the exam, the student must be able to demonstrate a performance meeting the minimum requirements for acceptance of the relevant material and of the knowledge, skills and competencies listed in the learning outcomes.
Single subject courses (day)
- Category
- Hours
- Lectures
- 30
- Class Instruction
- 30
- Preparation
- 106
- Project work
- 40
- English
- 206
Kursusinformation
- Language
- English
- Course number
- AØKK08216U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
Bachelor
- Duration
-
1 semester
- Price
-
Information about admission and tuition fee: Summer schools and for Danes at Åbent Universitet
- Schedulegroup
-
Summer 2021:
3 weeks in the beginning of August
More info;
Go to: "Remarks" and "Exam". - Studyboard
- Department of Economics, Study Council
Contracting department
- Department of Economics
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Andreas Bjerre-Nielsen (3-636470427571666375306d7730666d)
Teacher
Lectures: See ‘Course Coordinators’
To be announced
Teachingassistants:
Are you BA- or KA-student?
Courseinformation of students