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Data Structures and Algorithms, 6 credits

Datastrukturer och algoritmer, 6 hp

TDDC91

The course is disused.

Main field of study

Information Technology

Course level

First cycle

Course type

Programme course

Examiner

Erik Nilsson

Director of studies or equivalent

Ola Leifler

Education components

Preliminary scheduled hours: 52 h
Recommended self-study hours: 108 h

Possible deviation from course syllabus/programme syllabus

ECV = Elective / Compulsory / Voluntary

Course offered for		Semester	Period	Timetable module	Language	Campus	ECV
6CITE	Information Technology, M Sc in Engineering	3 (Autumn 2018)	1	3	Swedish	Linköping, Valla	C

Main field of study

Information Technology

Course level

First cycle

Advancement level

G1X

Course offered for

Information Technology, 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

Basic knowledge of programming in Java and basic knowledge in discrete mathematics and calculus.

Intended learning outcomes

The purpose of the course is to give the student tools to independently be able to construct computer programs that use time and memory in an efficient way. Upon completion of the course the student shall be able to:

demonstrate ability to analyze time and space complexity of iterative and simple recursive algorithms.
explain and use the most common abstract data types and sorting algorithms.
implement the most common abstract data types with different data structures and algorithms.
describe established methods for design (and analysis) of algorithms in general.

Course content

Basic notions
Mathematical foundations for analysis of algorithms
Fundamental abstract data types and data structures, such as lists, stacks, queues, search trees, hash tables and graphs
Efficiency analysis of algorithms
Sorting and searching
Algorithm paradigms

Teaching and working methods

The lectures present the theory. The tutorials are devoted to individual work with exercises illustrating the theory. The laboratory assignments concern computer implementation of the techniques presented in the lectures.

Examination

BAS1	Work in PBL-group	1 credits	U, G
DAT1	Computer examination	2 credits	U, 3, 4, 5
UPG1	Voluntary assignment	0 credits	U, G
LAB1	Laboratory work	2 credits	U, G
UPG2	Computer hand-in assignment	1 credits	U, G

UPG1 is comprised of a collection of voluntary programming assignments that, if they are solved, give bonus points towards the course's written examination (only the first offering).

Grades

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

Other information

Design and Analysis of Algorithms. Complexity Theory

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Ola Leifler

Examiner

Erik Nilsson

Course website and other links

http://www.ida.liu.se/~TDDC91/

Education components

Preliminary scheduled hours: 52 h
Recommended self-study hours: 108 h

Code	Name	Scope	Grading scale
BAS1	Work in PBL-group	1 credits	U, G
DAT1	Computer examination	2 credits	U, 3, 4, 5
UPG1	Voluntary assignment	0 credits	U, G
LAB1	Laboratory work	2 credits	U, G
UPG2	Computer hand-in assignment	1 credits	U, G

UPG1 is comprised of a collection of voluntary programming assignments that, if they are solved, give bonus points towards the course's written examination (only the first offering).

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

There is no course literature available for this course in studieinfo.

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

I = Introduce, U = Teach, A = Utilize

		I	U	A	Modules
1. DISCIPLINARY KNOWLEDGE AND REASONING
	1.1 Knowledge of underlying mathematics and science (G1X level)		X	X
	1.2 Fundamental engineering knowledge (G1X level)	X	X	X	LAB1
	1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)
	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	BAS1 LAB1
	2.2 Experimentation, investigation, and knowledge discovery			X	LAB1
	2.3 System thinking
	2.4 Attitudes, thought, and learning		X	X	BAS1 LAB1
	2.5 Ethics, equity, and other responsibilities
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
	3.1 Teamwork		X	X	BAS1 LAB1
	3.2 Communications
	3.3 Communication in foreign languages			X
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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