Data Science

Academic Study Board of the Faculty of Engineering

Teaching language: English
EKA: T520003102
Censorship: Second examiner: External
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

Course ID: T520003101
ECTS value: 10

Date of Approval: 31-08-2018


Duration: 1 semester

Version: Archive

Course ID

T520003101

Course Title

Data Science

ECTS value

10

Internal Course Code

SM-DSC

Responsible study board

Academic Study Board of the Faculty of Engineering

Date of Approval

31-08-2018

Course Responsible

Name Email Department
Kristian Severin Rasmussen krsr@tek.sdu.dk
Mikkel Baun Kjærgaard mbkj@mmmi.sdu.dk
Sofie Birch sbirch@tek.sdu.dk

Teachers

Name Email Department City
Fisayo Caleb Sangogboye fsan@mmmi.sdu.dk

Programme Secretary

Name Email Department City
Lise Lotte Krogh lilk@tek.sdu.dk Odense

Offered in

Odense

Level

Master

Offered in

Autumn

Duration

1 semester

Mandatory prerequisites

Bachelor in software engineering or equivalent.

Learning objectives - Knowledge

  • Explain different methods and algorithms regarding data collection, data processing, data analysis, and data visualisation.

Learning objectives - Skills

  • Implement and apply different methods and algorithms as stated above.

Learning objectives - Competences

  • Identify relevant problems within the subject area of the module.
  • Assess, select and apply methods within the subject area of the module.
  • Explain and discuss different methods within the subject area of the module.
  • Provide a proper documentation regarding the findings of the project.
  • Communicate findings in a clear and understandable manner. 

Content

The course will consist of two parts, namely theory (lectures) and practice (a project).

Lectures:
Data science is the extraction of knowledge from data. In particular it focuses on the collection, filtering, processing, creation and distribution of data. Dramatic growth in the scale and complexity of data that can be collected and analysed (Big Data) is affecting all aspects of work and society. This implies that development of effective and ethical ways of using vast amounts of data is a significant challenge to science and to society as a whole. Therefore, the main focus of this course will be on techniques and methods for data analysis and decision making, which requires interdisciplinary research in many areas, including databases, data processing, machine learning algorithms, statistics, and data visualization.


Project:
Broader understanding of the theory and practice of above stated algorithms and methods as well as a deep understanding of several selected methods.

URL for MySchedule

46
Monday
11-11-2019
Tuesday
12-11-2019
Wednesday
13-11-2019
Thursday
14-11-2019
Friday
15-11-2019
08 - 09
09 - 10
10 - 11
11 - 12
12 - 13
Class 1
Undervisning
Fisayo Caleb Sangogboye
13 - 14
Class 1
Undervisning
Fisayo Caleb Sangogboye
14 - 15
Class 1
Undervisning
Fisayo Caleb Sangogboye
15 - 16
Class 1
Undervisning
Fisayo Caleb Sangogboye
Show full time table

Number of lessons

48 hours per semester

Teaching language

English

Examination regulations

Exam regulations

Name

Exam regulations

Examination is held

By the end of the semester 

Tests

Exam

EKA

T520003102

Name

Exam

Description

The examination is based on an overall assessment of:

  • Project report
  • Oral examination

Form of examination

Combined test

Censorship

Second examiner: External

Grading

7-point grading scale

Language

English

ECTS value

10

Additional information

The module covers the key areas described above. The focus in the module is currently the specialized key areas “Data Mining” or “Decision Support”. Possible future focus areas are "Decision Support", “Big Data” and “Modeling, simulation and performance evaluations”.

Courses offered

Period Offer type Profile Programme Semester