COMP SCI 2201NA - Algorithm & Data Structure Analysis
Ngee Ann Academy - Quadmester 3 - 2017
General Course Information
Course Code COMP SCI 2201NA Course Algorithm & Data Structure Analysis Coordinating Unit School of Computer Science Term Quadmester 3 Level Undergraduate Location/s Ngee Ann Academy Units 3 Available for Study Abroad and Exchange N Prerequisites One of COMP SCI 1009, COMP SCI 1007, COMP SCI 1103, COMP SCI 1203, or COMP SCI 2202 Incompatible COMP SCI 2004 Course Description Program development techniques including basic ideas of correctness and proof; Notions of complexity and analysis; Recursion. Approaches to Problem Solving. Notion of abstract data type, representation of lists, stacks, queues, sets, trees and hash tables. Graphs and Graph Traversal.
Course Coordinator: Dr Bradley AlexanderLecturer: Dr Poh Kok Loo
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1 Skills in performing analysis of given recursive and iterative algorithms. 2 Understanding and performing simple proofs of algorithmic complexity and correctness. 3 An ability to understand and derive recurrences describing algorithms and properties of data structures. 4 An understanding of the implementation and efficiency of a range of data structures including, trees, binary heaps, hash-tables and graphs. 5 An understanding of a variety of well-known algorithms on some of the data structures presented. 6 The ability to implement and use these algorithms in code. 7 A foundational understanding of intractability. An understanding of proof techniques for NP-Completeness. 8 An ability to solve new analytic and algorithmic problems.
The above course learning outcomes are aligned with the Engineers Australia Stage 1 Competency Standard for the Professional Engineer.
The course is designed to develop the following Elements of Competency: 1.1 1.2 1.3 1.4 1.5 1.6 2.1 2.2 2.3 2.4 3.1 3.2 3.3 3.4 3.5 3.6
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
1-8 Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
1,2,8 Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
1-8 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
1,6,8 Intercultural and ethical competency
- adept at operating in other cultures
- comfortable with different nationalities and social contexts
- Able to determine and contribute to desirable social outcomes
- demonstrated by study abroad or with an understanding of indigenous knowledges
8 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
The textbook for this course is Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, Introduction to Algorithms, Third Edition, MIT Press.
Recommended ResourcesRecommended further reading:
Algorithms and Data Structures - The Basic Toolbox by Kurt Mehlhorn and Peter Sanders, Springer, 2008.(the full text is available on the Author’s website).
Online LearningCourse Website: https://forums.cs.adelaide.edu.au/forums/course/view.php?id=1031
Learning & Teaching Activities
Learning & Teaching ModesThe course is delivered in intensive mode according to the timetable here.
The delivery consists of lectures and worked exercises. Students are expected to participate actively in class and ask questions about content an assessment.
No information currently available.
Learning Activities Summary
No information currently available.
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
- Six written and programming assignments worth 5% each (30% total)
- One two hour written exam at end of term worth 70%
Assessment Related RequirementsStudents must score 50% overall to pass the course. If a student scores less than 40% in the exam component of the assessment then their mark will be capped to 44% F and any additional assessment would be granted at the discretion of the School.
Assessment DetailAssignment specifications will appear in MyUni as the course progresses. Students are expected to complete all parts of their assignments. Some assignments are allowed to be carried out in groups of up to two students. In these cases the marks will be shared.
More details of revising for the exam are given in the last module of the myuni pages for the course.
SubmissionAll assignments are to be submitted via the links for the assignment specifications in the MyUni pages of the course.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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This section contains links to relevant assessment-related policies and guidelines - all university policies.
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- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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