University of Split
Department of Professional Studies
DATA STRUCTURES AND
ALGOTIRHMS
COURSE SYLLABUS
1
COURSE DETAILS
Type of study
programme
Professional study - 180 ECTS
Study programme
COMPUTER SCIENCE
Course title
Data Structures and Algorithms
Course code
SIT019
ECTS
(Number of credits
allocated)
5
Course status
Optional
Year of study
Second
Semester
Second (spring)
Course Web site
http://moodle.oss.unist.hr/
Total lesson hours per
semester
Lectures
30
Practicals
0
Laboratory exercises & practical demonstration
30
Prerequisite(s)
None
Lecturer(s)
Department of Computer Science faculty:
Toma Rončević, lecturer.
Language of instruction
Croatian, English
2
COURSE DESCRIPTION
Course Objectives:
•
understanding basic data structures and algorithms
1. define basic static and dynamic data structures and relevant
standard algorithms for them: stack, queue, dynamically linked
lists, trees, graphs, heap, priority queue, hash tables, sorting
algorithms, min-max algorithm,
2. demonstrate advantages and disadvantages of specific algorithms
Learning outcomes
and data structures,
3. select basic data structures and algorithms for autonomous
On successful
realization of simple programs or program parts
completion of this
course, student should 4. determine and demonstrate bugs in program, recognise needed
basic operations with data structures
be able to:
5. formulate new solutions for programing problems or improve
existing code using learned algorithms and data structures,
6. evaluate algorithms and data structures in terms of time and
memory complexity of basic operations.
Introduction: arrays, structures, pointers, memory allocation, iteration
and recursion. Complexity analysis of algorithms. Singly and doubly
linked lists. Queue and stack and their basic operations. Trees, binary
Course content
search trees and basic operations. Hash tables. Graphs and basic
algorithms on graphs: depth first and breadth first search, Dijkstra’s
alghoritm. Priority queues. Sorting algorithms: quicksort, bubblesort,
selectionsort, mergesort. Min-max algorithm.
CONSTRUCTIVE ALIGNMENT – Learning outcomes, teaching and
assessment methods
Alignment of students activities with learning outcomes
Activity
Student workload
ECTS credits
Learning
outcomes
Lectures
30 hours / 1 ECTS
1,2,5,6
Practicals
Laboratory work
30 hours / 1
Self-study
78 hours / 2,6 ECTS
ECTS
3,4
1,2,3,4,5,6
3
Office hours and final exam
TOTAL:
12 hours / 0,4 ECTS
1,2,4,5,6
150 hours / 5 ECTS
1,2,3,4,5,6
CONTINUOUS ASSESSMENT
Performance
Grade ratio
Ai (%)
ki (%)
70 - 100
10
100
10
First mid-term exam
50-100
40
Second mid-term exam
50-100
40
Performance
Grade ratio
Ai (%)
ki (%)
Practical exam (written)
50 - 100
40
Theoretical exam (written and/or oral)
50 - 100
50
Previous activities
(include all continuous testing indicators)
50 - 100
10
Performance
Grade ratio
Ai (%)
ki (%)
Practical exam (written)
50 - 100
50
Theoretical exam (written and/or oral)
50 - 100
50
Continuous testing indicators
Class attendance and participation
Laboratory work
FINAL ASSESSMENT
Testing indicators – final exam (first and second
exam term)
Testing indicators – makeup exam (third and fourth
exam term)
4
PERFORMANCE AND GRADE
Percentage
Criteria
Grade
od 50% do 61%
basic criteria met
sufficient (2)
od 62% do 74%
average performance with some errors
good (3)
od 75% do 87%
above average performance with minor errors
very good (4)
od 88% do 100%
outstanding performance
excellent (5)
5