COMP SCI 7201 - Algorithm & Data Structure Analysis
North Terrace Campus - Semester 2 - 2017
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General Course Information
Course Details
Course Code COMP SCI 7201 Course Algorithm & Data Structure Analysis Coordinating Unit Computer Science Term Semester 2 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites COMP SCI 7080 or COMP SCI 7202 Incompatible COMP SCI 7082 Assumed Knowledge COMP SCI 7202 Restrictions Master of Computing and Innovation, Graduate Diploma in Computer Science and Graduate Certificate in Computer Science students only. Assessment May include reports, practical assignments, and exam. Course Staff
Course Coordinator: Dr Mingyu Guo
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On 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 .
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
1,2,5,6,8 -
Learning Resources
Required Resources
Textbook
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 Resources
Recommended further reading:
1. 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).
2. Data Structures and Algorithms in Java by Michael T. Goodrich, Irvine Roberto Tamassia, and Michael H. Goldwasser, Wiley, 6th Edition, 2014. (available in the library).Online Learning
Course website:
Course forum: -
Learning & Teaching Activities
Learning & Teaching Modes
Lectures and tutorials.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
The workload is approximately 12 hours per week during semester time. This consists of an average of 2.5 hours of contact time and the remaining time for study and working on tutorial submissions.Learning Activities Summary
The following details the topics to be introduced by the lectures. The tutorial topics will broadly follow this schedule.
- Introduction to complexity of algorithms, asymptotic notations
- Integer arithmetic
- Recursive and Karatsuba multiplication
- Priority queues and heaps
- Linear-time sorting algorithms
- Binary search trees and average case analysis
- AVL trees and skip-lists
- Hashing and hash tables
- Graphs and their representations
- Breadth-first-search and depth-first-search
- Strongly connected components
- Shortest path problem
- Dynamic programming
- Minimum spanning trees
- Complexity classes: P versus NP
Specific Course Requirements
There are no specific requirements for this course beyond prerequisite knowledge and the ability to attend the lectures and tutorials.Small Group Discovery Experience
There is no small group discovery experience component. -
Assessment
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.
Assessment Summary
The course assessment consists of three components:
- a written exam, 70% of the marks for the course;
- written submissions to assignments (some, optionally, done in teams), 30% of the marks for the course.
Component Weighting CBOK Areas Final written exam 70% 1,2,8 Assignments 30% 1,2,4,7,8,9,11
Details of the Australian Computer Society's Core Bode of Knowledge (CBOK) can be found in .
Below are the mappings to learning outcomes and graduate attributes:
Component Weight Learning Outcomes Graduate Attributes Final written exam 70% 1,2,3,4,5,6,7,8 1,2,3,4,5 Assignments 30% 1,3,4,5,6,8 1,2,3,4,5,6 Assessment Related Requirements
You are also encouraged to attend all tutorial sessions. Application for exemptions based on medical and/or compassionate grounds must be made to the course coordinator.Assessment Detail
The written exam will be centrally administered by examinations and held at the end of semester. Each assignment will be based on materials presented at that stage of the course and on readings drawn from reference materials. Three assignments will be given; each being worth 5-10% of the course mark. Some assignments will be based on group work. Assignments will be marked within two weeks after a submission deadline. Brief written feedback will be provided along with marks.Submission
All program code based assignments must be submitted using the School of Computer Science online Submission System. All hand written assignments must be submitted using the School of Computer Science boxes for assignments. Details are included in each assignment description on the course forum. The University policy on plagiarism applies on all submissions.Course Grading
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 .
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Student Feedback
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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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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