Contents
- Course Details
- Course Summary
- Assumed Knowledge
- Student Learning Outcomes
- Teaching Strategies
- Teaching Rationale
- Student Conduct
- Academic Honesty and Plagiarism
- Assessment
- Course Schedule
- Resources for Students
- Course Evaluation and Development
Course Details
| Course Code | COMP9024 |
| Course Title | Data Structures and Algorithms |
| Convenor | Aaron Quigley |
| Admin | Mei-Cheng Whale |
| Lectures |
Monday 15:00-17:00 → log into
Moodle COMP9024 (2022 T1)
→ Live lectures
(Weeks:1-9)
Wednesday 15:00-17:00 → log into Moodle COMP9024 (2022 T1) → Live lectures (Weeks:1-10) (note: flexibility week in 2022 T1 21 Mar - 27 Mar) |
| Consultations |
Expected Dates
Thursday 14:00-15:00 (Online), Course Forum, Email (these will be confirmed in Week 1) Friday 15:00-16:00, 17:00-18:00 (Online Help Labs) (these will be confirmed in Week 1) |
| Units of Credit | 6 |
| Course Website | http://www.cse.unsw.edu.au/~cs9024 |
| Handbook Entry | http://www.handbook.unsw.edu.au/postgraduate/courses/current/COMP9024.html |
(as of Feb 2nd) Please note that all components including lectures and consultations will be online in T1. The course and all of its assessments, including the final exam, can be completed online. As the situation improves we will try to bring in more face to face components but for now this is largely online for T1 2022.
Course Summary
Data structures are about how data is stored inside a computer for effective and efficient use. An algorithm is a step-by-step process for solving a problem within a finite amount of space and time. Data structures and algorithms are not only important in software design, but also in hardware design. Being proficient in data structures and algorithms are essential for good software developers, hardware developers, and system architects.
The actual content is taken from a list of subjects that constitute the basis of the tool box of every serious practitioner of computing: data types and data structures, abstract data types, dynamic data structures, analysis of algorithms and a variety of fundamental algorithms for graphs, trees and text processing.
Assumed Knowledge
There are no prerequisites for this course. However, the course goes at a fast pace through the necessary programming fundamentals at the beginning, and students may find it helpful if at the start of the course they
- are able to design, implement and test programs written in a procedural language;
- know simple data types (characters, numbers, strings, arrays);
- are able to use fundamental control structures and programming techniques (conditionals, loops, recursion).
These may have been acquired in the course COMP9021.
Student Learning Outcomes
After successfully completing this course, students will know fundamental data structures and algorithms, and they will be able to reason about their applicability, effectiveness and efficiency.
This course contributes to the development of the following graduate capabilities:
| Graduate Capability | Acquired in |
| scholarship: understanding of their discipline in its interdisciplinary context | lectures |
| scholarship: capable of independent and collaborative enquiry | problem sets, assignments, in-class (live or recorded) quizzes |
| scholarship: rigorous in their analysis, critique, and reflection | in-class (live or recorded) exercises, problem sets, assignments |
| scholarship: able to apply their knowledge and skills to solving problems | problem sets and assignments |
| scholarship: capable of effective communication | forum |
| scholarship: information literate | lectures, problem sets, assignments |
| scholarship: digitally literate | lectures, problem sets, assignments |
| professionalism: capable of independent, self-directed practice | problem sets and assignments |
| professionalism: capable of operating within an agreed Code of Practice | all course-work, by doing it yourself |
| global citizens: culturally aware and capable of respecting diversity and acting in socially just/responsible ways | interaction with your fellow students |
Teaching Strategies
- Lectures introduce concepts and show examples
- Problem sets reinforce concepts, provide additional examples and allow students to solve problems
- Assignments further reinforce concepts and allow students to solve larger problems
Teaching Rationale
Lectures will include exercises where we examine the practice of understanding and applying specific data structures and algorithms. Problem sets aim to deepen analysis and understanding via additional examples, problems and programming exercises. The large assignment gives you the chance to practice what you have learnt on a larger problem.
Student Conduct
The Student Code of Conduct ( Information , Policy ) sets out what the University expects from students as members of the UNSW community. As well as the learning, teaching and research environment, the University aims to provide an environment that enables students to achieve their full potential and to provide an experience consistent with the University's values and guiding principles. A condition of enrolment is that students inform themselves of the University's rules and policies affecting them, and conduct themselves accordingly.
In particular, students have the responsibility to observe standards of equity and respect in dealing with every member of the University community. This applies to all activities on UNSW premises and all external activities related to study and research. This includes behaviour in person as well as behaviour on social media, for example Facebook groups set up for the purpose of discussing UNSW courses or course work. Behaviour that is considered in breach of the Student Code Policy as discriminatory, sexually inappropriate, bullying, harassing, invading another one's privacy or causing any person to fear for their personal safety is serious misconduct and can lead to severe penalties, including suspension or exclusion from UNSW.
If you have any concerns, you may raise them with your lecturer, or approach the School Ethics Officer , Grievance Officer , or one of the student representatives.
Academic Honesty and Plagiarism
Plagiarism is defined as using the words or ideas of others and presenting them as your own. UNSW and CSE treat plagiarism as academic misconduct, which means that it carries penalties as severe as being excluded from further study at UNSW. There are several on-line sources to help you understand what plagiarism is and how it is dealt with at UNSW:
In particular you should take note of the new Plagiarism Policy:
Make sure that you read and understand these. Ignorance is not accepted as an excuse for plagiarism. In particular, you are also responsible that your assignment files are not accessible by anyone but you by setting the correct permissions in your CSE directory and code repository, if using. Note also that plagiarism includes paying or asking another person to do a piece of work for you and then submitting it as your own work.
UNSW has an ongoing commitment to fostering a culture of learning informed by academic integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at UNSW is defined as using the words or ideas of others and passing them off as your own.
The pages below describe the policies and procedures in more detail:
UNSW has an Academic Integrity Course running on Moodle. All students will need to complete this course if they have not done this before.
Assessment
| Component | Maximum Mark |
| Lab work (weekly; weeks 2-5, 7-10) | 8 × 2 = 16 |
| Midterm test (week 6) | 12 |
| Assignment (due Monday week 10) | 12 |
| Final Exam (exam period) | 60 |
Your final overall mark will be the sum of your marks for each component provided that you pass the final exam.
To pass the course, the sum of your marks must be 50 or higher and the mark for the final exam must be 25 or higher.
Course Schedule
| Elementary data structures and algorithms in C | week 1 |
| Analysis of algorithms | week 2 |
| Dynamic data structures | week 3 |
| Graph data structures and algorithms | week 4-5 |
| Search tree data structures and algorithms | week 7-8 |
| String algorithms, Approximation | week 9 |
| Randomised algorithms, Course review | week 10 |
Resources for Students
The recommended textbooks associated with this course are
- Robert Sedgewick, Algorithms in C, Parts 1–4 3rd edition, Addison Wesley, 1998.
- Robert Sedgewick, Algorithms in C, Part 5 3rd edition, Addison Wesley, 2002.
The following introduction to the C programming language is recommended as a supplementary textbook:
- Alistair Moffat, Programming, Problem Solving, and Abstraction with C 5th edition, Pearson, 2003.
Course Evaluation and Development
This course is being continuously improved and we will conduct a survey through UNSW's myExperience process at the end of session to obtain feedback on the quality of the various course components. Your participation in the survey will be greatly appreciated. Students are also encouraged to provide informal feedback during the session, and to notify the lecturer-in-charge of any problems as soon as they arise. In 2020 feedback was very favourable, including positive comments on a change due to previous feedback that resulted in the addition of a weekly assessment component. We will continue to have these weekly programming assignments & quizzes in this offering.
Resource created Wednesday 02 February 2022, 10:23:18 AM, last modified Saturday 19 March 2022, 08:00:47 PM.