COMP2521 19T0 Data Structures and Algorithms


COMP2521 19T0
Data Structures and Algorithms

Summer Semester, 2019
6 UoC — UG

Course Staff

Dr John Shepherd
<[email protected]>
Teaching Staff

For confidential course enquiries, email [email protected].

Course Summary

The goal of this course is to deepen your understanding of data structures and algorithms and how these can be employed effectively in the design of software systems. It is an important course in covering a range of core data structures and algorithms that will be used in context in later courses. You explore these ideas in lectures, tutorials, lab classes, and assignments. Assessment involves practical lab exercises, assignments, and practical and theory exams. At the end of the course, we want you to be a solid programmer, with knowledge of a range of useful data structures and programming techniques, capable of building significant software systems in a team environment, and ready to continue with further specialised studies in computing.

Topics: An introduction to the structure, analysis and usage of a range of fundamental data types and the core algorithms that operate on them, including: algorithm analysis, sorting, searching, trees, graphs, files, algorithmic strategies, analysis and measurement of programs. Labs and programming assignments in C, using a range of Unix tools.

Course Aims

The aim of this course is to get you to think like a computer scientist. This certainly sounds like a noble goal… but what does it really mean? How does a scientist, let alone a computer scientist, actually think?

What many types of scientists try to do is understand natural systems and processes: a geologist, for example, tries to understand the structure of the earth; a biologist tries to understand living organisms; a chemist tries to understand materials and reactions, and so on.

Computer scientists don’t, as the name might suggest, simply try to understand the structure and behaviour of computers, but are more concerned with understanding software systems (and the interaction between the software and the hardware on which it runs). Also, unlike other scientists, computer scientists frequently build the objects that they study.

During this course, we’ll be looking at ways of creating, analysing and understanding software. Ultimately, you should be able to answer the question,“is this piece of software any good?” and be able to provide sound reasons to justify your answer.

This course follows on from introductory C programming courses: COMP1511, COMP1917, or COMP1921. We cover additional aspects of the C programming language that were not covered in those courses, and also look at some programming tools which were not covered (in detail) earlier. However, this course is not simply a second C programming course: the focus is on the ideas and abstractions behind the data structures and algorithms that are used.

COMP2521 is a critical course in the study of computing at UNSW, since it deals with many concepts that are central to future studies in the area. Whether you are studying Computer Science, Software Engineering, Bioinformatics, Computer Engineering, or even a discipline outside the realm of computing, understanding a range of algorithms and data structures and how to use them will make you a much more effective computing problem solver in the future.

Assumed Knowledge

The official pre-requisite for this course is either COMP1511 or COMP1917 orCOMP1921.

Whether or not you satisfy the pre-requisite, we assume that:

  • you can program in the C programming language, and are familiar with arrays, strings, pointers, dynamic memory allocation, recursion;
  • you are able to design, implement, debug, test and document small C programs (up to several hundred lines of code); and
  • you are familiar with the Linux environment on CSE computers.

Installing Linux, possibly as a virtual machine, on your own computer would be a major bonus.

Student Learning Outcomes

After completing this course, students will:

  1. be familiar with fundamental data structures and algorithms
  2. be able to analyse the performance characteristics of algorithms
  3. be able to measure the performance behaviour of programs
  4. be able to choose/develop an appropriate data structure for a given problem
  5. be able to choose/develop appropriate algorithms to manipulate this data structure
  6. be able to reason about the effectiveness of data structures and algorithms for solving a given problem
  7. be able to package a set of data structures and algorithms as an abstract data type
  8. be able to develop and maintain software systems in C that contain thousands of lines of code

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, assignments
scholarship: capable of independent and collaborative enquiry lab work, assignments
scholarship: rigorous in their analysis, critique, and reflection tutorials
scholarship: able to apply their knowledge and skills to solving problems tutorials, lab work, assignments
scholarship: ethical practitioners all course-work, by doing it yourself
scholarship: capable of effective communication blog, tutorials
scholarship: digitally literate everywhere in CSE
leadership: enterprising, innovative and creative assignments
leadership: collaborative team workers lab work, assignments
professionalism: capable of operating within an agreed Code of Practice all prac work

Teaching Rationale and Strategies

Computer Science is, to a large extent, a practical discipline, and so COMP2521 has a large emphasis on practice. We uses the standard set of practice-focussed teaching strategies employed by most CSE foundational courses:

  • lectures introduce concepts and demonstrate examples;
  • tutorials reinforce concepts and provide additional examples;
  • lab exercises provide examples of using various technologies; and
  • assignments allow you to solve larger problems;

The only way to develop the skills to do effective software development is by practising them. If you slack off on the assignments and lab exercises (or, worse, rely on someone else to do them for you), you’re wasting the course’s most valuable learning opportunities.


Lectures aim to convey basic information about the course content and to model the practices and techniques involved in software development, including demonstrations and exercises where we examine the practice of developing and analysing programs.

Each week, there will be five hours of lectures during which theory, practical demonstrations and case-studies will be presented. Lectures convey a small amount of information about the course content, but their main aim is to try to stimulate you to think about concepts and techniques. Feel free to ask questions at any stage, but otherwise please respect people around you who are trying to listen and (shhhhhh!) keep quiet.


The most important components of the course, however, are the tutorials, labs and assignments.

Tutorials aim to clarify and refine the knowledge that you got from lectures, and from reading the text book and notes, and to develop analysis and understanding via practical case studies.

There will be a number of exercises set for each tutorial class; the aim of the time is not to simply get the tutor to give you the answers; instead, focus on just one or two of the exercises, and work through them in detail, discussing fine details, alternative approaches, etc. You can use the questions that weren’t examined in as much depth, or at all, for your own study.

You must be active and ask questions in tutorials! Ideally, students should run the entire tute themselves, with the tutor being a moderator, and occasionally providing additional explanations or clarifications.


Laboratory exercises provide a venue to put together and explore ideas, and to practice problem solving, program development, and analysis.

Each week, there will be several small exercises to work on, released in the week preceding the class. The exercises will need to be submitted, and will be assessed by your tutor. During the lab, your tutor will provide feedback on your approach to the problem, and on style in your solution.

Except for in the first week, labs will be done in pairs, and you and you partner should discuss the exercises before going to the lab, to maximise the usefulness of the class.


Assignments provide opportunities to work on substantial (hundreds of lines of code) programming exercises.

The first assignment is an individual assignment; the second will be completed in groups. We expect all members of a pair or group to contribute to the assignments; part of your assignment mark will be tied to this.

As noted above, assignments are the primary vehicle for learning the material in this course. If you don’t do them, or, worse, simply copy and submit someone else’s work, you have wasted a valuable learning opportunity.


The University requires us to assess how well you have learned the course content, and the usual primary approach is via a final exam. An exam is the ultimate summative assessment tool; it gives you a chance, at the end of the course, to demonstrate everything that you’ve learned. Labs and assignments are a learning tool, not an assessment tool: in an ideal world, I would award no marks for them; however, to give a more concrete incentive to do them (in a timely fashion), there are marks tied to them.

Item Due Marks LOs
Prac Exams weeks 5, 8 10.0% 1..8
Lab Exercises each week 10.0% 1..7
Assignment 1 week 3 10.0% 1..7
Assignment 2 week 8 15.0% 2..5,7,8
Final Exam exam period 55.0% 1..8


Week Lectures Tutes/Labs Assessment
1 introduction
review: linked lists
review: ADTs
review: structured data
linked list revision
ADT revision
assn1 released
2 algorithm analysis
benchmarking and profiling
3 sorting trees assn1 due
4 graph representation
graph traversal
algorithmic complexity
assn2 released
5 graph algorithms more sorting prac exam 1
6 search trees
balancing trees
7 hash tables
graph algorithms
8 revision balanced trees
hash tables
assn2 due
prac exam 2

Course Resources

COMP2521 follows the contents of the pair of books:

  • Algorithms in C, Parts 1-4: Fundamentals, Data Structures, Sorting, Searching (3rd Edition) by Robert Sedgewick, Addison-Wesley
  • Algorithms in C, Part 5: Graph Algorithms (3rd Edition) by Robert Sedgewick, Addison-Wesley

These two books are available as a bundle from the UNSW bookshop. They are expensive, but are useful well beyond this course, and will serve as a useful reference on the bookshelf of any serious programmer, despite their horrific style.

You may also be able to find on-line resources related to the text books. Robert Sedgewick has a series of videos on the topics in this course, but unfortunately they all seem to be in Java (which he has used for the new edition of his book). If you find any useful on-line resources, let us know; we’ll add them to the Resources section of the course web site (with credit to the finder).

This website also has links to auxiliary material/documentation that you’ll need for the course. Solutions for all tutorial questions and lab exercises will also be made available. I’ll review assignment and exam solutions in the lectures.

Academic Conduct and Integrity

Student Conduct

The Student Code of Conduct (informationpolicy) 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’s privacy or causing any person to fear for their personal safety is serious misconduct and can lead to severe penalties, up to and including suspension or exclusion from UNSW.

If you have any concerns, you may raise them with your lecturer, or approach theSchool Ethics OfficerGrievance 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 up to and including 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:

Please take the time to read and understand these documents. Ignorance is not accepted as an excuse for plagiarism. In particular, you are responsible for the safe-keeping of your assignment files such that they are not accessible by anyone but you by setting proper permissions on your CSE home directory and/or on online code repositories.

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.

The pages below describe the policies and procedures in more detail:

You should also read the following page which describes your rights and responsibilities in the CSE context:

Course Evaluation and Development

Student feedback on this course, and on the effectiveness of lectures in this course, is obtained via electronic survey (MyExperience) at the end of each semester. Student feedback is taken seriously, and continual improvements are made to the course based in part on this feedback. Students are strongly encouraged to let the lecturer in charge know of any problems as soon as they arise. Suggestions and criticisms will be listened to openly, and every action will be taken to correct any issue or improve the students’

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