Course Outline

Description

This course provides a foundational introduction to data structures and algorithms — the essential tools for designing efficient computer programs. Students will learn how to represent, organise, and manipulate data in memory, while developing the ability to reason about algorithm performance in terms of time complexity and space complexity. Emphasis is placed on practical implementation in C++, supported by a strong conceptual understanding of the underlying structures and techniques.

Topics include:

  • Arrays and Vectors
  • Linked Lists
  • Stacks and Queues
  • Binary Search Trees and Balanced Trees (AVL Trees)
  • Priority Queues and Binary Heaps
  • Depth and Breadth First Searches
  • Backtracking Algorithms
  • Sorting Algorithms: Bubble Sort, Insertion Sort, Selection Sort, AVL Sort, Heap Sort, Merge Sort, and Quick Sort

By the end of the course, students will be equipped to design and implement efficient solutions to a wide range of computational problems.

Details

  • 9 credits at NQF5
  • 90 notional hours

Lecturers and Teaching Assistant

This course will be taught by two lecturers and supported by a teaching assistant.

Lecturer: Prof. Richard Klein

Office: UG07, Upper Ground, TWK Mathematical Sciences Building

Email:

Lecturer: Mr. Muhammad Sahal Goolam

Email:

Please always include COMS1017A in the subject of your email and make sure to provide your student number in all communication. This will help prioritise it in my mailbox.

Teaching Assistant: Mr. Refiloe Mopeloa

Email:

Timetable

For each diagonal, there is a double lecture and triple lab for IDSA each week:

Time Venue
Diagonal A Lecture Tue 10:15-12:00 SH6
Diagonal A Lab Thu 14:15-17:00 MSL
Diagonal C Lecture Thu 10:15-12:00 FNB5
Diagonal C Lab Mon 14:15-17:00 MSL

Learning Management System

We will use Moodle as the primary platform for this course. Please visit https://courses.ms.wits.ac.za/moodle/course/view.php?id=155 and log in using your student number and usual Wits password. You should already be enrolled — if not, please confirm your registration with the faculty.

Please additionally confirm that you have been assigned to the correct group based on Moodle. Alert us immediately if this is not the case, as it will impact your access and visibility of course materials and assessments.

Note: We will not use Ulwazi for this course.

Communication and Consultations

All official announcements will be posted on the Announcements Forum on Moodle. While Moodle will email these to you, it is your responsibility to check that you are receiving and reading them. It will be assumed that all students have seen an announcement 24 hours after it is posted.

We will also be using Discord for communication. Please join the course server at https://discord.gg/Xv9xrKK5M. Once you’ve joined, you will be prompted to enter your name as it appears on Moodle to verify your identity.

There is also a request-help channel that allows you to open a ticket for assistance. You may request assistance from a tutor or alternatively the AI tutor-bot, which will assist you with your query. This division is primarily to help us prioritise tickets that require tutor assistance. Once your query is resolved, please mark the ticket as closed.

Additionally, there is Questions & Answers Forum on Moodle for more general course-related queries. This allows other students to benefit from shared answers, and the forum is actively monitored by the teaching team.

As a general guideline for communication within the course:

Query Type Preferred Communication Channel
General course queries Moodle Q&A Forum or appropriate Discord channel
Individual course content queries Tutors (In-person or via Discord request-help channel)
Personal or sensitive queries Email to Mr. Muhammad Goolam and/or Mr. Refiloe Mopeloa

In general, all personal queries should be directed to Muhammad and/or Refiloe for a quick turnaround. If they are unable to resolve your query, they will escalate it to Prof. Richard Klein. If a query cannot be resolved through the above channels or requires special attention, you may email Prof. Richard Klein. This, however, should be a last resort and you should expect a delay in response. Please ensure that you have exhausted all other avenues before doing so.

Lab Attendance and QuickLabs

This course is a heavily practical course and the labs are an essential part of the learning process. There will be labs released weekly. The time allocated to complete these will vary so please pay careful attention to the deadlines. Lab attendance is compulsory and will be monitored.

In addition to the weekly labs, there will be a QuickLab at the start of each lab session, commencing from the first week. This is a short, timed, individual assessment that will test your understanding of the material covered in the previous lecture and/or lab. QuickLabs are designed to ensure that you are keeping up with the course content and to help prepare you for the lab tests and exams. These count towards SP requirements and it is imperative that you treat them seriously. A tentative schedule can be found here.

Textbook

This course does not require students to purchase a textbook — the content is widely available online and we will provide links to high-quality free resources. However, if you are funded or prefer to work from a physical book, the following are recommended:

Recommended Primary Text:

  • Mark Allen Weiss, Data Structures and Algorithm Analysis in C++ (Fourth Edition), Pearson, 2014. (Weiss 2014) A solid and accessible text that balances theory and practical implementation in C++.

Advanced Reference:

  • Cormen, Leiserson, Rivest, and Stein, Introduction to Algorithms, MIT Press, 2009. (Cormen et al. 2009) Widely known as CLRS, this book is more advanced and rigorous. It is also prescribed in the Honours-level algorithms course and is worth investing in if you plan to continue in Computer Science.

For C++ Programming:

  • Bjarne Stroustrup, Programming – Principles and Practice Using C++ (2nd Edition), Addison-Wesley, 2014. (Stroustrup 2014) An excellent and readable introduction to C++ by the language’s creator.

Other Resources

While textbooks are useful, they can be expensive — and there are many excellent free resources online that cover the course content thoroughly. I will link to specific resources during the semester, but you are encouraged to explore on your own as well. Useful resources include:

Algorithms and Data Structures

C++ Programming Help

Discussion and Troubleshooting

  • Stack Overflow Ask and search for specific programming errors.
  • YouTube Channels — Many tutorials and walkthroughs are available for C++, algorithms, and debugging.

If you find additional resources that help you understand the content, please consider sharing them on the Moodle Q&A forum or Discord.

Grading

The provisional mark breakdown is as follows:

Item Weight
Projects 5%
Labs 5%
QuickLabs 5%
Lab Tests 25%
Theory Tests 20%
Exam (Lab) 20%
Exam (Theory) 20%

Tentative Schedule

This provisional schedule and is subject to change. A detailed schedule can be found here.

Block 3 Lectures

Week Topics
1 C++ Revision: abstraction, functions, recursion, static/dynamic memory allocation, pointers and arrays, structures, classes, unit testing.
2 Searching (Linear and Binary Search) and Sorting Algorithms (Bubble, Insertion, and Selection Sorts)
3 Arrays & Vectors 1
4 Arrays & Vectors 2
5 Linked Lists
6 Linked Lists
7 Stacks & Depth First Search

Block 4 Lectures

Week Topics
8 Queues and Breadth First Search
9 Binary Search Trees
10 Binary Search Trees
11 Balanced Trees (AVL Trees)
12 Priority Queues & Binary Heaps
13 Sorting Algorithms (AVL Sort, Heap Sort, Mergesort & Quicksort)
14 Sorting Algorithms (AVL Sort, Heap Sort, Mergesort & Quicksort)

Assessment Dates

Test Diagonal A Diagonal C Topics
Lab 1 Thu 20 Aug 2026 Mon 24 Aug 2026 C++, Searching, Sorting, Vectors
Theory 1 Thu 03 Sep 2026 Mon 14 Sep 2026 C++, Searching, Sorting, Vectors, Linked Lists
Lab 2 Thu 17 Sep 2026 Mon 21 Sep 2026 Linked Lists
Theory 2 Thu 08 Oct 2026 Mon 12 Oct 2026 Stacks, Queues, DFS, BFS, BST
Lab 3 Thu 22 Oct 2026 Mon 26 Oct 2026 BST

Satisfactory Performance

This outline extends the general undergraduate computer science course outline which fully defines the various outcome codes for different courses.

  • FNQL: To qualify to write the final exams, you need to have completed atleast 80% of the QuickLabs and achieved a coursework average of at least 35% for the invigilated components of the course (the QuickLabs, lab tests and theory tests). An FNQL is a fail for the course.
  • FSUB: There are both practical (lab test 1, lab test 2, lab test 3, and the lab exam) and theory (theory test 1, theory test 2, and the theory exam) components to this course, you must achieve at least 35% for both components to pass the course, regardless of your overall average. An FSUB is a fail for the course.

Additional Support

  • Walk-In Centre: Above the weekly lab sessions, there is a walk-in centre on the first floor or MSL where you can receive additional support from tutors during the week. This runs during fixed time slots weekly. A schedule will be posted on Moodle.
  • PSG Peer-Coding sessions: These sessions run in MSL on Saturday mornings and are targeted at students who are struggling with programming and require further support and guidance. Please keep an eye on Moodle for announcements regarding these sessions.

Academic Integrity

Academic integrity is of utmost importance. Any form of academic dishonesty — including plagiarism, sharing answers, copying during invigilated assessments, or submitting work that is not your own — will be taken very seriously and may result in failing the course and/or being reported to the legal office.

While collaboration is encouraged during labs and projects, it’s essential to understand the line between them helping you learn, and someone doing the work for you. A good rule of thumb:

When you need help: They can look at your code to help you — but you should not look at theirs.

This ensures that you are the one doing the learning, thinking through the problem, and writing the solution. Copying someone else’s code — even with minor changes — is plagiarism, but worse, when you sit down alone in the tests you will not be able to do the work without them.

All lab, test, and project submissions will be monitored for plagiarism using a combination of automated tools and manual checks. Moodle and network activity logs will also be audited. Submitting material to platforms like Chegg or similar services is strictly prohibited and will be referred for disciplinary action.

On the Use of Language Models (e.g. ChatGPT, Copilot, Claude, Gemini, etc.)

Language models are powerful tools for learning — but like all tools, they must be used responsibly. A useful way to think about them is this:

Treat language models the same way you would treat a helpful friend.
You can ask them questions. You can brainstorm with them. You can learn from them.
But you should not ask them to do your work for you.

You are expected to learn how to code independently. If you rely on a model to write your solutions, you are not developing the skills you’ll need in invigilated assessments — or in future courses and your career. Use language models to learn, not to submit.

Debugging is also a fundamental part of becoming a good programmer. Don’t treat every error message as something to immediately copy and paste into a language model. Take the time to read your code, understand the error, and reason through the problem. This skill — reading and understanding your own code — is essential not just for tests, but for almost everything you will do in computing going forward.

If we suspect that a submission was generated (in full or part) by a language model, we may require you to complete a follow-up oral assessment. In such cases, your oral performance will determine your mark for that task.

References

Cormen, Thomas H, Charles E Leiserson, Ronald L Rivest, and Clifford Stein. 2009. Introduction to Algorithms. MIT press.
Dale, Nell B, Chip Weems, and Tim Richards. 2018. C++ Plus Data Structures. 6th ed. Jones & Bartlett Learning.
Goodrich, Michael T, Roberto Tamassia, and David M Mount. 2011. Data Structures and Algorithms in c++. 2nd ed. John Wiley & Sons.
Stroustrup, Bjarne. 2014. Programming: Principles and Practice Using c++. Pearson Education.
Weiss, Mark A. 2014. Data Structures & Algorithm Analysis in c++. 4th ed. Pearson Education.