COMP SCI 2201 - Algorithm & Data Structure Analysis

North Terrace Campus - Semester 2 - 2021

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.

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Course Details

Course Code	COMP SCI 2201
Course	Algorithm & Data Structure Analysis
Coordinating Unit	Computer Science
Term	Semester 2
Level	Undergraduate
Location/s	North Terrace Campus
Units	3
Contact	Up to 3 hours per week
Available for Study Abroad and Exchange	Y
Prerequisites	One of COMP SCI 1103, COMP SCI 1203, COMP SCI 2103, COMP SCI 2202 or COMP SCI 2202B
Incompatible	COMP SCI 2004
Assessment	Written exam and/or assignments.

Course Staff

Course Coordinator: Dr Mingyu Guo

Course Timetable

The full timetable of all activities for this course can be accessed from .

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.

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).
Learning & Teaching Activities
Learning & Teaching Modes

Lectures and workshops

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
- 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, tutorials, and SGDE activities.

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

Assessment Task	Weighting (%)	Individual/ Group	Formative/ Summative	Due (week)*	Hurdle criteria	Learning outcomes	CBOK Alignment**
Assignments	40	Individual	Summative	Weeks 2-12		1. 2. 3. 4. 5. 6. 7. 8.	1.1 1.2 2.3 4.1
Exam	60	Individual	Summative	E	min 40%	1. 3. 4. 5. 6. 8.	1.1 1.2 4.1
Total	100

* The specific due date for each assessment task will be available on MyUni.

This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.

**CBOK is the Core Body of Knowledge for ICT Professionals defined by the Australian Computer Society. The alignment in the table above corresponds with the following CBOK Areas:

1. Problem Solving

1.1 Abstraction

1.2 Design

2. Professional Knowledge

2.1 Ethics

2.2 Professional expectations

2.3 Teamwork concepts & issues

2.4 Interpersonal communications

2.5 Societal issues

2.6 Understanding of ICT profession

3. Technology resources

3.1 Hardware & Software

3.2 Data & information

3.3 Networking

4. Technology Building

4.1 Programming

4.2 Human factors

4.3 Systems development

4.4 Systems acquisition

5. ICT Management

5.1 IT governance & organisational

5.2 IT project management

5.3 Service management

5.4 Security management

Assessment Related Requirements

You are also encouraged to attend the tutorial sessions. Application for exemptions based on medical and/or compassionate grounds must be made to the course coordinator.

Assessment Detail

Submission

No information currently available.

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 .

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.
Student Support
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
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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Authorised by:	Deputy Vice-Chancellor and Vice-President (Academic)
Maintained by:	Course Outlines Administrator

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