DM857: Introduction to programming

Study Board of Science

Teaching language: Danish or English depending on the teacher, but English if international students are enrolled
EKA: N340105102
Assessment: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Autumn
Level: Master

STADS ID (UVA): N340105101
ECTS value: 10

Date of Approval: 31-03-2022


Duration: 1 semester

Version: Approved - active

Comment

The course is a mandatory "conversion class" for MSc-students of Computational Biomedicine with a background outside the thematic field, for instance students of biology.
The course is co-read with DS830.

Entry requirements

The course can not be taken by students enrolled in the master programme in computer science.
The course cannot be followed if the student has passed DM574, DM536, DM550, DM562, DS800, DS801, DS830, or MM560, or if the student has DM574, DM536, DM550, DM562, DS800, DS801, DS830 or MM560 mandatory in their curriculum.

Academic preconditions

None

Course introduction

The course provides an academic basis for solving problems by modelling and implementing computer programs. The course builds upon the students' highschool competences in Mathematics and provides the students with the necessary prerequisites for several topics of other courses, in particular DM847, DM868 and BMB834, that appear later in the degree.

In relation to the learning outcomes of the degree the course has explicit focus on:

  • giving the competence to plan and execute computer programs
  • knowledge of common programming methods
  • developing skills in programming in different types of programming languages
  • developing skills in software development
  • developing skills in constructing bigger software systems
  • developing skills in deciding and justify professional decisions
  • developing skills in describing, formulating and disseminating problems and results to either other professional or non-specialists or collaborative partners and users
  • giving the competence to handle complex and development-oriented situations in study and work contexts
  • giving the competence to identify one's own needs for learning and structure one's own learning in different learning environments
  • giving the competence to design higher level software architectures

Expected learning outcome

The learning objectives of the course are that the student demonstrates the ability to:
  • design models for concrete problems;
  • devise a program structure based on the model;
  • implement the planned program in the concrete programming language used;
  • find and use adequate elements in the program library belonging to the  language;
  • plan and execute a testing of the program;
  • design and implement recursive solutions of problems;
  • design and implement abstract data types;
  • use basic tree structures and algorithms for these.

Content

The following main topics are contained in the course:
  • The basic structuring tools sequence, repetition, conditional instruction and subprogram.
  • Fundamental data structures such as lists, maps, and trees.
  • Structured programming techniques, including examples and applications.
  • Recursion and recursive data structures.
  • Examples of abstract data types and their realization.

The course is taught in Python.

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Autumn and January

Tests

Portfolio exam with oral defense

EKA

N340105102

Assessment

Second examiner: Internal

Grading

7-point grading scale

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

To be announced during the course

ECTS value

10

Additional information

Portfolio exam consisting of small individual assignments, a group project and an oral defence  (with written report and oral defense).
Oral defence is held during the exam period - January.

Indicative number of lessons

66 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.

  • Intro phase: 28 hours
  • Training phase: 38 hours, of which 28 hours of tutorials and 10 hours of laboratories.
The intro phase facilitates an 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.
The training phase is divided into tutorials and labs, where students learn the competencies that enable them to translate their knowledge into a solution and subsequently into concrete computer programs. 
In the study phase, the students work independently to increase their understanding and their competencies regarding the content of the subject. 

Activities in the study phase: Programming of small assignments and projects.

Teacher responsible

Name E-mail Department
Antonio Rago rago@qtc.sdu.dk Computational Science
Marco Peressotti Peressotti@imada.sdu.dk Concurrency

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration

NAT

Offered in

Odense

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.