AØKK08216U  Summerschool 2018: Social Data Science

Volume 2017/2018
Education

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

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.

Learning Outcome
  1. After the course the student should be able to:

Knowledge:

- 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.

- Use statistical prediction algorithms as well as the ability to estimate these models in practice.

Skills:

- 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 statistical techniques for predicting and classification (known as statistical learning) relate to existing empirical tools within economics such as causal inference and regression.

Competences:

- 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.

 

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:

https:/​/​abjer.github.io/​sds

This course is available to students and pracitioners who are interested in social data science. Because the course builds on a wide range of techniques, we do not have any hard requirements,but students are expected to have an interest in at least one of the following: statistics, econometrics, linear algebra, and a scripting language (we will focus on Python in this course).
The course will consist of lectures and exercises and problem solving. The lectures will focus on the broad topics covered in the course (part 1¬3 listed above). In the exercise classes we will get our hands dirty and present data science methods needed for collecting and analyzing real¬world data. The exercises do not have a large amount of time for learning how to code. We will use some of this time like development meetings: going over assignments, having detailed code reviews of various forms, and discussing blocking issues and potential solutions.
Schedule:
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.

The dates will be annonced not later than 1 November 2017

Timetable and venue:
Will be available from April 2018
Credit
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.
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.

Aid
All aids allowed

 

 

Marking scale
7-point grading scale
Censorship form
External censorship
if chosen by the Head of Studies.
Exam period

Summer 2018:

To be announced ot later than 1. November 2017

For enrolled students more information about examination, rules, exam schedule etc. is available at the intranet for master students (UK) , master students (DK) and bachelor students (DK).

Re-exam

In the period December 2018 - January 2019.

The exact day and time of the exam will be informed at the  student intranet for Summer schools and in the Self-Service at KUnet during Autumn 2018.

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.

More information about reexamination, rules, schedule etc. is available at the intranet for master students (UK) , master students (DK) and bachelor students (DK).

 

 

 

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 be able to demonstrate in an excellent manner that he or she has acquired and can make use of the knowledge, skills and competencies listed in the learning outcomes.

  • Category
  • Hours
  • Lectures
  • 30
  • Preparation
  • 106
  • Project work
  • 40
  • Class Instruction
  • 30
  • Total
  • 206