CSCI2520 Data Structures and Applications


Course code CSCI2520
Course title Data Structures and Applications
Course description This course formally examines the relationship between abstract data types and data structures. The implementation of abstract data types using various data structures will be discussed. Abstract data types including list, stack, queue, symbol table, tree and graph will be introduced. Introductory complexity analysis and big-O notation will be illustrated with simple algorithms such as searching and sorting.
本科旨在介紹抽象數據類型和數據結構之間的關係。內容將會探討如何應用多種數據結構來實現不同的抽象數據類型,包括鏈表,棧,隊列,符號表,樹和圖等。並通過查找和排序等簡單演算法來介紹一些基本的演算法複雜度分析以及大 O 符號表示。 
Unit(s) 3
Course level Undergraduate
Pre-requisite  CSCI1110 or 1120 or 1130 or 1510 or 1520 or 1530 or 1540 or ENGG1110 or ESTR1002 or 1100 or 1102 or (MATH2210 and 2220) or PHYS2351
Exclusion  CSCI2100 or ESTR2102
Semester 2
Grading basis Graded
Grade Descriptors A/A-:  EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-:  GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
Learning outcomes 1. To be able to implement the following data structures as abstract data types in a high level programming lanauge: stack, queue, hash table, list, binary search tree (including AVL tree), B-tree, graph (including minimum spanning tree and shortest path);
2. To be able to use appropriate data structures in different applications;
3. To be able to implement abstract data types;
4. To be able to analyse the complexity of simple algorithms (such as searching and sorting);
(for reference only)
Exam: 40%
Assignments: 40%
Mid-term: 20%
Recommended Reading List 1. Carrano, Data Structures and Abstractions with Java


CSCIN programme learning outcomes Course mapping
Upon completion of their studies, students will be able to:  
1. identify, formulate, and solve computer science problems (K/S); TP
2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V);
4. communicate effectively (S/V);
5. succeed in research or industry related to computer science (K/S/V);
6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); TP
7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);
8. practise high standard of professional ethics (V);
9. draw on and integrate knowledge from many related areas (K/S/V);
Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured