COMP SCI 2201 - Algorithm & Data Structure Analysis

North Terrace Campus - Semester 1 - 2022

This course provides an introduction to program development techniques with a focus on basic ideas of correctness and proof. The course introduces, among others, notions of complexity and analysis, recursion, abstract data types, representation of lists, stacks, queues, sets, trees and hash tables, graphs and Graph Traversal. The course allows students to experience different approaches to problem solving.

• General Course Information
Course Details
Course Code COMP SCI 2201 Algorithm & Data Structure Analysis Computer Science Semester 1 Undergraduate North Terrace Campus 3 Up to 3 hours per week Y One of COMP SCI 1103, COMP SCI 1203, COMP SCI 2103, COMP SCI 2202 or COMP SCI 2202B COMP SCI 2004 This course provides an introduction to program development techniques with a focus on basic ideas of correctness and proof. The course introduces, among others, notions of complexity and analysis, recursion, abstract data types, representation of lists, stacks, queues, sets, trees and hash tables, graphs and Graph Traversal. The course allows students to experience different approaches to problem solving.
Course Staff

Course Coordinator: Associate Professor Qi Wu

Course Timetable

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

• 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.

This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

University Graduate Attribute Course Learning Outcome(s)

Attribute 1: Deep discipline knowledge and intellectual breadth

Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.

1-8

Attribute 2: Creative and critical thinking, and problem solving

Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.

1,2,8

Attribute 3: Teamwork and communication skills

Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.

1-8

Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.

1,6,8

Attribute 5: Intercultural and ethical competency

Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.

8

Attribute 8: Self-awareness and emotional intelligence

Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

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
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

No information currently available.

No information currently available.

Learning Activities Summary

No information currently available.

• Assessment

The University's policy on Assessment for Coursework Programs is based on the following four principles:

1. Assessment must encourage and reinforce learning.
2. Assessment must enable robust and fair judgements about student performance.
3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
4. Assessment must maintain academic standards.

Assessment Summary
 Assessment Task Weighting (%) Individual/ Group Formative/ Summative Due (week)* Hurdle criteria Learning outcomes CBOK Alignment** Assignments 35 Individual Summative Weeks 2-12 1. 2. 3. 4. 5. 6. 7. 8. 1.1 1.2 4.1 Exam 60 Individual Summative NA min 40% 1. 3. 4. 5. 6. 8. 1.1 1.2 4.1 SGDE 5 Group Summative Week 12 4. 5. 6. 8. 2.3 2.4 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

Due to the current COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.

The exam (60%) will be replaced with three open book theory-quizzes which will be released at week 6, week 9 and week 13.
Each theory-quiz includes multiple problems in the format of multiple-choices, open-ended questions and essay, which will cover all the topics in the past four weeks.

Each one is treated as an Individual assignment and each one accounts for 20% of the whole course, and there will be no hurdle. You will only have one day to finish the quiz and there will be no extension.

Replacement will be offered at the discretion of the Course Coordinator only in cases that would qualify for a replacement assessment under the standard University of Adelaide Modified Arrangements for Coursework Assessment policy.
Assessment Detail

No information currently available.

Submission

No information currently available.

Grades for your performance in this course will be awarded in accordance with the following scheme:

M10 (Coursework Mark Scheme)
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.

• 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
• 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 studentâ€™s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.

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