DS819: Data Driven Applications

Entry requirements

A Bachelor’s degree from the humanities or social science or enrolled at MSc. in Data Science.

Academic preconditions

HTML and CSS knowledge as the one obtained from the course DS838 Web development or equivalent

Course introduction

The aim of the course is to enable the student to search relevant and publicly available data sets to analyse, manage and integrate them into practical applications.

The course provides with the main theoretical and practical principles of Data Driven Design. The student will address issues of data- discovery, analysis, presentation and decision processes while learning to implement Data Driven Applications.

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

give competence to Identify, develop and use data-driven applications.
give competence to apply the data life-cycle from analysis, selection, modelling and representation in the implementation of data-driven applications.
give skills to use modern technologies to implement practical Data Driven Applications, for example APIs to access data sets and web technologies.

Expected learning outcome

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

identify processes and methods to establish an information architecture for Data-Driven applications.
find relevant publicly available data sets for Data Driven applications.
retrieve, analyse, manage and present relevant data into Data Driven applications.
have knowledge of modern technologies used in the implementation of Data Driven applications.

Content

The following main topics are contained in the course:

Theoretical principles for Data Driven applications
Analysis and management publicly available data sets.
Tools for retrieval, management and presentation of data.
Processes and methods used for the design of information architecture and how they are used for effective communication of content in a Data Driven application.

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn

Tests

Home assignment

EKA

N340070102

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Duration

7 days

Examination aids

To be announced during the course.

ECTS value

Additional information

Take-home exam- - individual, max. 10 standard pages, exclusive front page, table of contents, bibliography and appendix.

Indicative number of lessons

26 hours per semester

Teaching Method

The teaching method is based on three phase model:

The course total number of hours is 26, preferably with the following distribution:

9 weekly sessions where

Intro phase: 1 session 2 hrs - Introduction to the course
Skills training phase: 8 sessions 3 hrs - each of these lessons will combine theory lectures and group and individual hands on exercises with weekly assignments.

This is a research and project based course where the students start working in a semester project from the 2nd session.

Activities during the study phase:

Case-work.
Preparation of lectures.
Preparation of presentations.

Teacher responsible

Name	E-mail	Department
Andrea Valente	aval@sdu.dk	Institut for Design, Medier og Uddannelsesvidenskab

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.