DSK801: Programming for Data Science
Study Board for Natural Scientific IT Programmes
Teaching language: English
EKA: N340138202
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Kolding
Offered in: Autumn
Level: Master
STADS ID (UVA): N340138201
ECTS value: 10
Date of Approval: 08-04-2025
Duration: 1 semester
Version: Approved - active
Internal Course Code
Comment
Entry requirements
The course cannot be taken by students enrolled in the master programme in Computer Science.
The course cannot be followed if the student has passed DM574, DM550, DM562, DM536, DM857, DS830, DM831, DS801, DSK811 or MM560, or has it mandatory in their curriculum.
The course cannot be followed if the student has passed DM574, DM550, DM562, DM536, DM857, DS830, DM831, DS801, DSK811 or MM560, or has it mandatory in their curriculum.
Academic preconditions
Course introduction
The aim of the course is to enable the student to solve data analysis tasks for a diversity of problems from different research areas. Next to algorithmic thinking, data analysis workflows include activities like data modeling, gathering, cleaning, processing, and means to visualize certain attributes in basic plots. This is important in regard to the remainder of the Data Science education as it provides the basis for carrying out data analysis projects.
The course gives an academic basis for studying the topics Data Mining and Machine Learning, Applied Machine Learning, Visualization and Deep Learning, that are part of the degree.
Among others, students partaking the course will particularly earn the following 21st Century Skills:
- The ability to integrate and assess information
- Competently find, utilise and assess information
- Being able to execute and implement
- Be flexible and adaptable
- Co-create solutions to existing problems and work effectively in teams
Expected learning outcome
The learning objectives of the course is that the student demonstrates the ability to:
- Apply learned problem solving strategies to different data processing tasks
- Adapt existing solutions to related tasks across domains
- Develop new data analysis strategies
- Develop Python programs that implement data processing workflows
- Find, select and utilize existing modules to collect, clean and process data
- Collaboratively develop data analysis solutions in project teams
Content
The following topics are contained in the course:
1. Basics of computing and algorithmic thinking
2. Python programming
- basic data types
- branching
- loops
- functions
- mutable data types
- modules
- file I/O, exceptions
- classes
- basic data visualization
3. Data analysis workflows with data sets from different domains, e.g.:
- biography data
- climate data
- textual data
- numerical data
Literature
Examination regulations
Exam element a)
Timing
Autumn and January
Tests
Portfolio
EKA
N340138202
Assessment
Second examiner: Internal
Grading
7-point grading scale
Identification
Full name and SDU username
Language
Normally, the same as teaching language
Examination aids
All common aids allowed
ECTS value
10
Additional information
The portfolio exam consists of the following elements:
- a group project with a written short report (1 page) describing the group's programming project (to be submitted)
- a shared group presentation of the project with an oral group discussion (after element 1 has been reviewed)
- a short individual oral exam with questions on aspects of the implemented project (directly after the shared group presentation)
To achieve a passing grade overall, all elements must independently meet the objectives. The assessment of element 1 will take place in conjunction with the completion of elements 2 and 3.
Element 1 counts for 35%, element 2 counts for 35%, and element 3 counts for 30%, in which a overall evaluation is applied.
Indicative number of lessons
Teaching Method
Planned lessons:
Total number of planned lessons: 90
Hereof:
Common lessons in classroom/auditorium: 54
Team lessons in classroom: 21
The lectures facilitates the introduction to new material and topics, which in the skills training phase are processed with exercises prepared at home and discussed in class to validate the acquired knowledge
Other planned teaching activities:
Gives the students the possibility to apply and use the knowledge acquired.
Teacher responsible
Timetable
Administrative Unit
Team at Registration
Offered in
Recommended course of study
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.