COMP SCI 7201 - Algorithm & Data Structure Analysis
North Terrace Campus - Semester 2 - 2023
General Course Information
Course Code COMP SCI 7201 Course Algorithm & Data Structure Analysis Coordinating Unit School of 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 7103, COMP SCI 7202, (COMP SCI 7210 and COMP SCI 7211), COMP SCI 7202B or COMP SCI 7208 Incompatible COMP SCI 7082 Course Description 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 Coordinator: Dr Muhammad Uzair
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.
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)
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.
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.
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.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
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.
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.
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:
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
The course will primarily utilize three activities to deliver the content:
Lectures: Lecture sessions will introduce and explain the foundational concepts that form the basis of algorithm and data structure analysis.
Lecture sessions will provide an in-depth coverage of topics related to time and space complexity, algorithm and data structure analysis techniques, and algorithmic proof and correctness. Interactive discussions will be encouraged to enhance the learning experience and promote a deeper comprehension of the subject matter.
Workshop Sessions: In workshop sessions, students will collaboratively solve problem sets that require algorithm and data structure analysis. Tutors will provide guidance in overcoming challenges and optimizing problem-solving approaches. Through group work and solution discussions, students will enhance their problem-solving skills, reinforce their understanding of algorithm analysis, and develop the ability to evaluate solutions for efficiency and correctness.
Assignments: Assignments will reinforce the concepts learned and foster problem-solving skills. Students will be given problem-solving assignments that require the application of algorithm and data structure. Assignments will strengthen students' ability to apply algorithm and data structure analysis to real-world problems.
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 SummaryThe 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
- 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 RequirementsThere are no specific requirements for this course beyond prerequisite knowledge and the ability to attend the lectures and tutorials.
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 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 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 Solving1.1 Abstraction1.2 Design
2. Professional Knowledge2.1 Ethics2.2 Professional expectations2.3 Teamwork concepts & issues2.4 Interpersonal communications2.5 Societal issues2.6 Understanding of ICT profession
3. Technology resources3.1 Hardware & Software3.2 Data & information3.3 Networking
4. Technology Building4.1 Programming4.2 Human factors4.3 Systems development4.4 Systems acquisition
5. ICT Management5.1 IT governance & organisational5.2 IT project management5.3 Service management5.4 Security management
Assessment Related RequirementsYou 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 DetailAssignments in this course consist of programming tasks that are related to the topics covered. These assignments are intended to be completed individually.
SubmissionSubmissions will be done online through either MyUni or the web submission system.
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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