DM507: Algorithms and Data Structures

The Study Board for Science

Teaching language: Danish or English depending on the teacher
EKA: N330068112, N330068102
Assessment: Second examiner: None, Second examiner: External
Grading: Pass/Fail, 7-point grading scale
Offered in: Odense
Offered in: Spring
Level: Bachelor

STADS ID (UVA): N330068101
ECTS value: 10

Date of Approval: 21-10-2025


Duration: 1 semester

Version: Archive

Internal Course Code

DM507

Comment

The course is co-read with DM578 and DS814

Entry requirements

The course cannot be chosen if you have passed, are enrolled in, have followed DM578, DS814 or DSK814 or if either of those is a mandatory part of your curriculum.

Academic preconditions

Students taking the course are expected to

  • posses mathematical maturity corresponding to the level in the DM549 and
  • be familiar with programming in Python corresponding to the level from e.g. DM574.

Course introduction

The aim of the course is to enable the student to apply a wide range of existing algorithms and data structures for fundamental problems, as well as general methods for developing new algorithms and mathematical tools for analyzing the correctness and efficiency of algorithms. This is of paramount importance for the ability to develop efficient software, and is central to the understanding of upper and lower bounds for computational problems.

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:
  • use the algorithms and data structures taught in the course on concrete problem instances.
  • give precise arguments for the correctness or incorrectness of an algorithm or a data structure.
  • determine the asymptotic running time of an algorithm or a data structure.
  • adapt known algorithms and data structures to special cases of known problems or new problems.
  • design new algorithms and data structures for problems similar to those taught in the course, including giving a precise description of the algorithm, e.g. using pseudocode.
  • design and implement a larger program, using algorithms and data structures taught in the course.

Content

The following main topics are contained in the course:
  • Mathematical basis: recursion equations, invariants.
  • Algorithms: correctness and complexity analysis, divide and conquer (Master Theorem, Strassen's algorithm), greedy algorithms, dynamic programming, sorting algorithms (insertionsort, mergesort, heapsort, quicksort, countingsort, radixsort), graph algorithms (BFS, DFS, topological sorting of DAGs, connected components, strongly connected components, MST, SSSP, APSP), Huffmann coding.
  • Data structures: dictionaries (BSTs, red-black trees, hashing), priority queues, disjoint sets.

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Spring

Tests

A mandatory project

EKA

N330068112

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

All common aids except generative AI

ECTS value

2.5

Exam element b)

Timing

June

Tests

Written exam

EKA

N330068102

Assessment

Second examiner: External

Grading

7-point grading scale

Identification

Student Identification Card - Exam number

Language

Normally, the same as teaching language

Duration

3 hours

Examination aids

The exam is with limited aids. Only the following aids are allowed:

  • textbooks, notes, lecture presentations, compendiums, and formula collections, etc.

Internet is not allowed during the exam. However, you may visit system DE-Digital Exam when answering the multiple-choice questions. If you wish to use course materials from itslearning, you must download the materials to your computer the day before the exam. During the exam course material may not be available to you.

ECTS value

7.5

Additional information

The reexam in August is an oral exam with external examiner and grades according to the 7-point grading scale.

Indicative number of lessons

88 hours per semester

Teaching Method

Planned lessons:
Total number of planned lessons: 88
Hereof:
Common lessons in classroom/auditorium: 44
Team lessons in classroom: 44

In the common lessons, the course subjects are introduced and covered in detail by the lecturer, while allowing for questions from participants.

In the team lessons, the participants work individually and in groups on exercises and programming tasks based on the subject. Help and feedback is given, and solutions are presented at the end.

Other planned teaching activities:
After the common lessons and before the corresponding team lessons, the participants work individually and in groups on understanding the subjects and on associated exercises and programming tasks.

Teacher responsible

Name E-mail Department
Rolf Fagerberg rolf@imada.sdu.dk Algoritmer

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at 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.