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Data Structures, 6 credits

Datastrukturer, 6 hp

TND004

Course starting semester

Main field of study

Computer Science and Engineering Media Technology and Engineering

Course level

First cycle

Course type

Programme course

Examiner

Aida Nordman

Director of studies or equivalent

Camilla Forsell

Education components

Preliminary scheduled hours: 60 h
Recommended self-study hours: 100 h

Possible deviation from course syllabus/programme syllabus

ECV = Elective / Compulsory / Voluntary

Course offered for		Semester	Period	Timetable module	Language	Campus	ECV
6CIEN	Electronics Design Engineering, M Sc in Engineering	6 (Spring 2019)	2	3	Swedish	Norrköping, Norrköping	E
6CMEN	Media Technology and Engineering, M Sc in Engineering	6 (Spring 2019)	2	3	Swedish	Norrköping, Norrköping	C

Main field of study

Computer Science and Engineering, Media Technology and Engineering

Course level

First cycle

Advancement level

G2X

Course offered for

Electronics Design Engineering, M Sc in Engineering
Media Technology and Engineering, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

Programming in C++

Intended learning outcomes

The aim of the course is to give students the tools to independently be able to create programs that solve practical problems dealing with large amounts of data, taking into account efficient use of time and memory. Upon completion of the course the student should fulfill the following learning outcomes.

To propose specific data structures and algorithms to address practical problems.
To motivate objectively the choices made, concerning chosen data structures, and relate to the known scientific results in the field.
To analyze the trade offs, regarding efficiency in several aspects, of different data structures proposed for addressing a practical problem.
To implement and use the data structures and algorithms in application programs.

Course content

Algorithm analysis. Recursion. Lists, stacks and queues. Trees and tree traversals. Binary search trees, threaded trees and balanced trees. Hashing and hash tables. Priority queues and binary heaps. Sorting och searching. Indexed files. Graphs and graph traversals. Fundamental graph algorithms.

Teaching and working methods

Lectures, lessons, and laboratory work.

Examination

UPG1	Optional assignment	0 credits	U, G
LAB1	Laboratory work	3 credits	U, G
TEN1	Written examination	3 credits	U, 3, 4, 5

The optional assignment consists of two quizzes. Results on the quizzes will give credits to the computer examination (and re-examination until the month of October after the course is completed).

Grades

Four-grade scale, LiU, U, 3, 4, 5

Department

Institutionen för teknik och naturvetenskap

Director of Studies or equivalent

Camilla Forsell

Examiner

Aida Nordman

Course website and other links

http://weber.itn.liu.se/~aidvi05/courses/14/index.html

Education components

Preliminary scheduled hours: 60 h
Recommended self-study hours: 100 h

Course literature

Data Structure and Algorithm Analysis in C++, Mark Allen Weiss, Addison Wesley, 4th edition, year 2014.

Code	Name	Scope	Grading scale
UPG1	Optional assignment	0 credits	U, G
LAB1	Laboratory work	3 credits	U, G
TEN1	Written examination	3 credits	U, 3, 4, 5

The optional assignment consists of two quizzes. Results on the quizzes will give credits to the computer examination (and re-examination until the month of October after the course is completed).

Student rights and obligations (login required)
Linköping University common rules and regulations (in Swedish)

Data Structure and Algorithm Analysis in C++, Mark Allen Weiss, Addison Wesley, 4th edition, year 2014.

Download

Download course literature list as PDF

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize

		U	A	Modules
1. DISCIPLINARY KNOWLEDGE AND REASONING
	1.1 Knowledge of underlying mathematics and science (G1X level)		X
	1.2 Fundamental engineering knowledge (G1X level)
	1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)	X	X	LAB1 TEN1
	1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)
	1.5 Insight into current research and development work
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
	2.1 Analytical reasoning and problem solving	X	X	LAB1 TEN1
	2.2 Experimentation, investigation, and knowledge discovery	X	X	LAB1 TEN1
	2.3 System thinking	X	X	LAB1 TEN1
	2.4 Attitudes, thought, and learning	X	X	LAB1 TEN1
	2.5 Ethics, equity, and other responsibilities	X	X	LAB1 TEN1
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
	3.1 Teamwork		X	LAB1
	3.2 Communications		X	LAB1
	3.3 Communication in foreign languages
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
	4.1 External, societal, and environmental context
	4.2 Enterprise and business context
	4.3 Conceiving, system engineering and management
	4.4 Designing
	4.5 Implementing
	4.6 Operating
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
	5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development
	5.2 Economic conditions for knowledge development
	5.3 Identification of needs, structuring and planning of research or development projects
	5.4 Execution of research or development projects
	5.5 Presentation and evaluation of research or development projects

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