DS813: Introduction to Human informatics

Entry requirements

A Bachelor’s degree or a Bachelor of profession.
The course cannot be taken by students enrolled in the master programme in Computer Science.

Academic preconditions

None. The course is introductory.

Course introduction

The aim of the course is to enable the student to analyse, manage and present humanistic data (such as text- image- and sound-based data) by applying relevant tools. On a human oriented and value based foundation, the student will work with issues related to data-driven design-, analysis- and decision processes, which is important to provide humanistic grounded value creation.

The course provides a foundation for applying the knowledge, competences, and skills, which is acquired in the education, particularly addressing issues related to data-driven analysis, presentation, and data-driven decisions related to humanistic data.

In relation to the competence profile of the degree it is the explicit focus of the course to:

give competence to apply data science technologies to manage to work- and development situations in a humanistic grounded data analytical perspective.
give competence to provide data-driven analyses to support decision processes in a humanistic perspective.
give skills to analyse non-quantitative data via, e.g., clustering and sentiment analysis.
give skills to present and visualize data.
give skills to present and evaluate statistical models for humanistic data.
give knowledge about data models and humanistic analytical methods.

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:

analyse, evaluate, document and present humanistic based data analyses by applying the relevant tools.
have knowledge of statistical humanistic data analytical methods and models for humanistic data.

Content

The following main topics are contained in the course:

Humanistic data and their specific properties.
Tools for humanistic data analysis.
Inspection and management of humanistic data.
Arrangement and evaluation of statistical models for humanistic data.
Analytical methods with particularly relevance for the humanities.
Documentation and presentation of the results of a humanistic data analysis.

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

January

Tests

Written exam

EKA

N340057102

Assessment

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card

Language

Normally, the same as teaching language

Duration

4 hours

Examination aids

All common aids are allowed, e.g. textbooks, own notes, computer programs that do not use the

Internet, etc

Internet is not allowed. However, you must access the course's website in itslearning in connection

with opening the "DE – Digital Exam" system. Notes from the course, which you wish to use as aids,

must be downloaded to your computer no later than the day before the exam. During the exam, it is not

certain that all course material will be available to you.

ECTS value

Indicative number of lessons

52 hours per semester

Teaching Method

Teaching in the course may take place in the following forms: Lectures and practical work, group discussions, exercises and presentations as well as project work.

Teacher responsible

Name	E-mail	Department
Edward Abel	abel@sdu.dk	Department of Design, Media and Educational Science

Timetable

Odense

Show full time table

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study

Profile	Education	Semester	Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study.

If a transitional arrangement has been made for a course, it will be stated in the list.

See transitional arrangements for all courses at the Faculty of Science.