Bachelor of Science in Computer Science in Cyber Security

The course helps students develop an appreciation for programming as a problem-solving tool. It teaches students how to think algorithmically and solve problems efficiently, and serves as the foundation for further computer science studies.

Using a project-based approach, students will learn to manipulate variables, expressions, and statements in Python, and understand functions, loops, and iterations. Students will then dive deep into data structures such as strings, files, lists, dictionaries, tuples, etc. to write complex programs. Over the course of the term, students will learn and apply basic data structures and algorithmic thinking. Finally, the course will explore the design and implementation of web apps in Python using the Flask framework.

Throughout the course, students will be exposed to abstraction and will learn a systematic way of constructing solutions to problems. They will work on team projects to practice pair programming, code reviews, and other collaboration methods common to the industry. The course culminates in a final group project and presentation during which students demonstrate and reflect on their learning.

All materials are inclucded.

This module provides a foundation in building for the web. It helps students understand how the internet works, examines the role of the internet in their lives, and teaches them the basics of web development. The module prepares students for the advanced module in Web Application Development.

The module will cover the building blocks of web technologies. Students will learn HTML, intermediate CSS, and the basic concepts and use of JavaScript. The course covers a brief history of the internet and network technologies. Students will relate what they learn about the conceptual foundations of the web to their own experience of the web, recreating common design and interaction patterns seen across countless websites. The module will focus on collaboration, communication, and sharing. Web technology is fundamentally social; students will work together and build for real audiences.

The module culminates in a project in which students create a website using the tools they learned throughout the module.

Front End Web Development builds on previous knowledge of web development, and extends students’ familiarity with modern HTML, CSS, JavaScript, and Web APIs. Students learn to develop and deploy client-side web applications with greater scope and complexity. Complex frontend features require using HTML, CSS, and JavaScript together. Students will usually have taken Web Application Development (or similar course under advisement from their faculty) as a prerequisite for this course.

Students deepen their knowledge of the JavaScript language, covering in depth topics like scope and higher order functions. Students practice using modern build tools for package management, bundling, optimization, formatting and linting, and testing. Throughout the course, students will solve practice exercises and build projects, culminating in a final project using a JavaScript framework to build a complex web application.

Students will continue to apply technical communication skills by writing technical specs, drafting architecture diagrams, and documenting APIs. They will extend their communication practice through technical blogging on topics like tool comparisons, architecture choices, benchmarks, and frontend web design. Students will grow in independence by reading documentation to learn about novel language and browser features.

This course builds upon the foundational concepts introduced in Programming 1,

aiming to deepen students' understanding of programming with a focus on data access and management, incorporating advanced programming paradigms.

Key programming concepts such as data types, operators, variables, and control flows are revisited, now with an added emphasis on advanced techniques like recursivity, object-oriented programming, and event-driven programming. These paradigms enhance students' ability to structure and manage complex data interactions efficiently. Students learn to use Regular Expressions, a powerful tool for finding and extracting data from string and other data types. They are introduced to modern web protocols, and learn how to retrieve data from web services using Python and JSON, and how to access and parse data in XML. Students learn the basics of working with databases and the relationships between databases. They learn how to write queries in SQL, the

foremost programming language for generating, manipulating, and retrieving

information from a relational database.

Communicating for Success supports students in developing communication skills that are essential for success in their personal and professional lives. The course will focus on close reading, written communication, verbal communication, and non-verbal communication skills. An emphasis will be placed on weekly submissions, and peer and instructor feedback, to allow students to practice and improve their skills.

Students will learn how to effectively read and analyze texts as a precursor to developing their own written communication skills. They will then practice crafting clear communications by learning about topics such as writing structure and organization, grammar, audience awareness, and the iterative writing process. Next, students move on to verbal communication, and will learn how to confidently and skillfully deliver effective oral presentations. Finally, students will learn about the impact of non-verbal communication on how their messages are received.

The course will culminate in a project that will require students to develop and implement a strategy for communicating a technical topic to a non-technical audience.

In today's interconnected world, where technology permeates every aspect of our lives, protecting our digital assets and information has become paramount. The Introduction to Cyber Security module is designed to provide students with a comprehensive understanding of the fundamental concepts, principles, and practices of cyber security.

Through a combination of theoretical knowledge and hands-on practical exercises, students will develop the necessary skills to identify and mitigate various cyber threats, protect sensitive data, and safeguard computer systems and networks.

By the end of this module, students will have a solid foundation in cyber security principles, enabling them to pursue further studies in specialized areas of cyber security.

Optimizing Your Learning aims to transform incoming first year students into effective and empowered self-directed learners. In the modern world, long-term academic, professional, and personal success is driven by the ability of individuals to take control of their learning. Therefore, this course helps students to develop the knowledge, skills, and mindsets necessary to take ownership of their learning and build their self-efficacy. During the course, students will develop competence in skills that are most critical for effective self-directed and self-regulated learning (i.e. self-management, self-monitoring, and self-modification), while also learning how to use learning strategies to maximize their overall learning efficiency and efficacy. They will also utilize the Emotional Intelligence framework to explore their identity, self-image, motivation, and self-regulation skills, to support their development as self-directed learners. The course culminates in the creation of a personal learning charter that will help guide students in their learning throughout their undergraduate studies, which can also be applied to their learning activities in other realms of their lives.

This course is intended for students with little or no programming experience. It aims to help students develop an appreciation for programming as a problem solving tool and to provide a foundation in Python programming. Students will learn how to think algorithmically, solve problems efficiently, and prepare for further computer science studies. The course begins with an introduction to programming constructs in a simplified visual environment. Students will learn to specify commands, and to construct complex programs from simple instructions. The next part of the course introduces a formal programming environment, and students learn the basic syntax and structures of Python. Topics covered include variables, expressions, conditional execution, functions, loops, and iterations. Throughout the course, students will be exposed to abstraction and will learn a systematic way of constructing solutions to problems. They will work on team projects to practice pair programming, code reviews, and other collaboration methods common to industry.

The course culminates in a final group project and presentation during which students demonstrate and reflect on their learning.

In this course, students practice the skills necessary to work effectively on a professional software product team. By working in small teams to build a web application, they integrate the technical, communication, and collaboration skills built in previous courses.

Students build a multi-feature web application, either for a fictional client or an original idea of their own design. As they work together, they learn modern technical collaboration tools and practices. Topics covered include using version control for shared repository management, writing technical design documents, and conducting code reviews. They also practice project management skills by implementing the SCRUM framework, including sprint planning, reviews, and retrospectives. During each milestone, team members rotate taking on various roles including Scrum master, product owner, and technical lead. Throughout the course, students will also apply and refine the emotional intelligence, team development, and leadership frameworks previously learned. By the end of the course, students should understand and value the various roles within a software product development team, and be able to participate effectively on a product team.

There are no scheduled class sessions. Teams will submit their sprint retrospectives for feedback from peers and faculty.  The course culminates in a showcase where students present their final project to their peers and external stakeholders.

Industry Experience is a form of experiential learning that enables students to apply their academic knowledge in a professional context. Students work to build software that meets the needs of a professional organization by completing either (1) an

approved internship, or (2) a product studio.

During the online internship, students work on tasks that meet the needs of the

organization, guided by an on-site supervisor. Internships must entail significant,

substantial computer science. In the studio, external clients (e.g., businesses, non-

profits) sponsor a software development project completed by students. A typical end

result is a prototype of or a fully functional software system ready for use by the clients.

These projects are completed by teams of 4-6 students, who meet with the client

weekly to share progress and get feedback.

Students complete online modules under the supervision of a faculty advisor. Pre-work

includes instruction in communication, goal-setting, and professional development.

During the industry experience, students submit bi-weekly written reflections on their

personal goals, challenges, and, for the studio, team feedback. At the end of the term,

students obtain written feedback from their organization supervisor. They also submit

a final report which describes the problem statement, approaches/methods used,

deliverables, and skills gained. Industry Experience culminates in a final presentation

which is shared as a public blog post.

This course provides a foundation in building for the web. It helps students understand how the internet works, examines the role of the internet in their lives, and teaches them the basics of web development. The course prepares students for the advanced course in Web Application Development.

The course begins with a brief history of the internet and network technologies. Students will learn about the physical underpinnings of the internet, barriers to connectivity, and efforts to expand access (e.g., undersea cable projects, satellite projects). They will also explore the challenges of internet security and privacy.  Students will be encouraged to make these social explorations personal, and investigate the history, barriers, and opportunities for connectivity in their local regions. The course will also cover the building blocks of web application development. Students will learn fundamentals of HTML, intermediate CSS, and basic concepts and syntax of JavaScript.

The course culminates in a “Knowledge Share” project during which students create a website to educate a non-technical audience on a key aspect of the internet or emerging technology.

Engineering for Development, Challenge Studio 1, and Challenge Studio 2 are 3 courses that help students investigate the role that technology can play in solving some of the world’s most intractable social and economic development challenges.

Challenge Studio 2 builds on the final output from Challenge Studio 1, and supports students in creating a sustainable business model for the MVP that they developed in the previous course. This course is focused on putting the MVPs in the hands of real users, getting their feedback, and iterating and refining the product or service, while also developing a viable business model.

The course will utilize virtual studio time, where groups are able to work collaboratively on their MVPs, with the support of additional lectures, seminars, and learning resources on important topics such as product launch planning, user evaluation tools and frameworks, business canvas development, funding models, financial modelling and strategy, and pitching.

The course will culminate in a pitch showcase, where students are required to present their work to relevant stakeholders (e.g. industry leaders).

This course introduces scalable cloud-based data processing using PySpark, Python, SQL, and Spark to manipulate structured and semi-structured data. Learners will apply NumPy, Pandas, and PySpark to analyze large datasets and produce meaningful insights. The course further develops skills in visual storytelling through Seaborn and introduces generative AI and interactive dashboards for applied analytics. Students will also study secure architecture fundamentals, vulnerability assessment, secure network design, zero-trust principles, network segmentation, and cloud security considerations. The course concludes with an applied project integrating big-data analytics with secure architectural design.

Industry Experience 2 provides a form of experiential learning that enables students to apply their academic knowledge in a professional context. Students work to build software that meets the needs of a professional organization by completing either (1) an approved internship, or (2) a product studio.

During the online internship, students work on tasks that meet the needs of the organization, guided by an on-site supervisor. Internships must entail significant, substantial computer science. In the studio, external clients (e.g., businesses, non-profits) sponsor a software development project completed by students. A typical end result is a prototype of or a fully functional software system ready for an end user. These projects are completed by teams of 4-6 students, who meet with the clients or other end users weekly to share progress and get feedback.

Students complete online modules under the supervision of a faculty advisor. Pre-work includes instruction in communication, goal-setting, and professional development. During the industry experience, students submit bi-weekly written reflections on their personal goals, challenges, and, for the studio, team feedback. At the end of the term, students obtain written feedback from their organization supervisor.  They also submit a final report which describes the problem statement, approaches/methods used, deliverables, and skills gained. Industry Experience culminates in a final presentation which is shared as a public blog post.

This course teaches the fundamentals of data structures and introduces students to the

implementation and analysis of algorithms, a critical and highly valued skill for

professionals.

Students start by examining the basic linear data structures: linked lists, arrays, stacks,

and queues. They learn how to build these structures from scratch, represent

algorithms using pseudocode, and translate these into running programs. They apply

these algorithms to real-life applications to understand how to make complexity and

performance tradeoffs. Students will also learn how to develop algorithms for sorting

and searching, use iteration and recursion for repetition, and make tradeoffs between

the approaches. They will learn to estimate the efficiency of algorithms, and practice

writing and refining algorithms in a programming language.

This course emphasizes big-picture understanding and practical problem-solving in

preparation for technical interviews and professional practice. Throughout the course,

students will solve common practice problems, and participate in mock interview

sessions. As part of their regular assignments, they will also deepen their understanding

of these topics and practice technical communication by writing technical blog posts.

This course equips learners with foundational and applied statistical inference skills using Python. Students will work with probability distributions, confidence intervals, and hypothesis testing across proportions, means, categorical data, and multivariate datasets. The course advances into regression analysis, beginning with simple and multiple linear regression before exploring model diagnostics, transformations, interaction terms, model selection, and regularization methods such as Ridge and Lasso. Emphasis is placed on interpreting model outputs, assessing performance, and addressing the bias–variance tradeoff. A final applied lab reinforces learning through real-world regression modeling.

The threat of cybercrime is constantly evolving. New threats emerge as different technologies are created, innovated, and improved. The only way to be prepared to face these digital challenges is by knowing your enemy. Much like famous war tacticians throughout history, cybercriminals use common types of hacking techniques that are proven to be highly effective. Instead of entirely new hacking algorithms, they tend to find ways to innovate on commonly used methods like malware, SQL injection, cross site scripting (XSS), Zero-Day attacks and more. In this module, students will learn how to use and defend against dangerous but rudimentary attacks, such as SQL injection, XSS, and more. Students will develop a thorough understanding of hacking techniques by utilizing both a proactive approach through ethical hacking and a reactive approach that focuses on prevention, detection, and responding to attacks. Students will learn skills that will prepare them for situations they may encounter during their careers so they can meet them with confidence.

Engineering for Development, Challenge Studio 1, and Challenge Studio 2 are 3 courses that help students investigate the role that technology can play in solving some of the world’s most intractable social and economic development challenges.

In Challenge Studio 1, students will work in groups to design, develop, and test a solution to a development challenge of their choice. The focus of this course is to provide students with the tools and skills to create meaningful technology solutions (e.g. services, products) to a sustainable development problem. This course builds on the problem identification and analysis skills that were developed in Engineering for Impact, the product management skills that were developed in Product Management and Design, and the ethical engineering skills developed in Ethics in Tech.

At the end of Challenge Studio 1 students will submit a Minimum Viable Product (MVP) that is ready to go to market as their final project deliverable.

The course will utilize virtual studio time, where groups work together on the key incremental tasks that are required to allow them to successfully create their final project output. Studio time will be supported by lectures, seminars, and learning resources on useful skills such as human centered design, end user identification, requirements gathering, value creation, impact measurement, and creative thinking and innovation.

Network and Computer Security teaches students the principles and practices of security for software, systems, and networks. It aims to make students critical examiners and designers of secure systems. Students will learn the mathematical and theoretical underpinning of security systems, as well as practical skills to help them build, use, and manage secure systems.

The first part of the course is focused on applied cryptography. Students learn general cryptographic protocols and investigate real-world algorithms. The second part of the course covers software and system security, including access controls, trends in malicious code, and how to detect system vulnerabilities. There is a special focus on web security, and modern practices for building secure web architectures. The final section of the course focuses on network security and covers concepts of networking, threats, and intrusion protection.

Course projects will require students to think both as an attacker and as a defender, and write programs that examine security design.  Students will also examine recent security and privacy breaches. Working in pairs, they’ll conduct an in-depth investigation, and give a presentation to help classmates understand its technical underpinnings and social implications.

Data science is applicable to a myriad of professions, and analyzing large amounts of data is a common application of computer science. This course empowers students to analyze data, and produce data-driven insights. It covers all areas needed to solve problems involving data, including preparation (collection and integration), presentation (information visualization), analysis (machine learning), and products (applications).

This course is a hybrid of a computing course focused on Python programming and algorithms, and a statistics course focusing on estimation and inference. It begins with acquiring and cleaning data from various sources including the web, APIs, and databases. Students then learn techniques for summarizing and exploring data with spreadsheets, SQL, R, and Python. They also learn to create data visualizations, and practice communication and storytelling with data. Finally, students are introduced to machine learning techniques of prediction and classification, which will prepare them for advanced study of data science.

Throughout the course, students will work with real datasets (e.g., economic data) and attempt to answer questions relevant to their lives. They will also probe the ethical questions surrounding privacy, data sharing, and algorithmic decision making. The course culminates in a project where students build and share a data application to answer a real-world question.

This capstone course enables students to demonstrate their proficiency in the technical and human skills that they have acquired throughout their undergraduate studies. The capstone requires students to conceptualise, plan, and implement a software project to completion, and evaluate their project’s processes and outcomes.

The capstone builds on the initial project scoping work that was carried out in Capstone Research Methods, which culminated in students submitting a project proposal, and gaining formal approval for their capstone Project Proposal.

In this course, students will implement their proposed project with the support of a supervisor. Students with a common supervisor will be put into capstone advisory peer groups and will be required to meet with their group and supervisor regularly to update each other on their capstone progress and to provide feedback. Students will also have regular meetings with their capstone supervisor to provide additional support and guidance throughout the module.

Upon completion of their capstone projects, all students will be required to participate in a capstone symposium at the end of the term, where they will present their working projects/prototypes to internal and external stakeholders.

Artificial Intelligence (AI) aims to teach students the techniques for building computer systems that exhibit intelligent behavior. AI is one of the most consequential applications of computer science, and is helping to solve complex real-world problems, from self-driving cars to facial recognition. This course will teach students the theory and techniques of AI, so that they understand how AI methods work under a variety of conditions.

The course begins with an exploration of the historical development of AI, and helps students understand the key problems that are studied and the key ideas that have emerged from research. Then, students learn a set of methods that cover: problem solving, search algorithms, knowledge representation and reasoning, natural language understanding, and computer vision. Throughout the course, as they apply technical methods, students will also examine pressing ethical concerns that are resulting from AI, including privacy and surveillance, transparency, bias, and more.

Course assignments will consist of short programming exercises and discussion-oriented readings. The course culminates in a final group project and accompanying paper that allows students to apply concepts to a problem of personal interest.

This module will prepare students to apply and interview for internships and full-time positions in the software engineering industry.

Students will refine their personal brand, and craft effective resumes, LinkedIn profiles and portfolios. They will learn to communicate effectively in behavioral interviews, including how to conduct company and role research, and how to succinctly answer questions and share their background. They will learn to prepare for technical interviews. Key skills include the ability to walk an interviewer through one’s thought process, craft code on a whiteboard or document, and identify opportunities for improvement in one’s work.  Finally, students will learn to prepare to onboard to development job, and understand how to effectively navigate large codebases and organizations to make valuable contributions.

The module emphasizes learning by doing, and the majority of assessments will be in the form of feedback received from practice interviews with industry professionals.

This course aims to teach students the theoretical and practical methods for solving problems using machine learning. Machine learning is one of the fastest-growing areas of computer science. Its applications are reshaping society, from consumer products (e.g., voice assistants and recommendations) to life-sciences (e.g., personalized medicine and tumor detection). This course will build on the Data Science introductory course, and help students understand how, why and when machine learning methods work.

Students will be introduced to major paradigms in machine learning, and gain working knowledge of supervised and unsupervised learning techniques. Students will learn to solve common problems such as regression, classification, clustering, matrix completion and pattern recognition. They will learn how to train and optimize neural networks. They will explore modern software libraries that enable programmers to develop machine learning systems. Students will use these libraries along with publicly available data sets to build models, then learn how to evaluate and verify the results. Throughout the course, as they apply technical methods, students will also examine the societal context of machine learning and considerations of transparency, bias and privacy.

Course assignments will consist of short programming exercises and peer discussions of ethical considerations. The course culminates in a final group project and accompanying paper that allows students to apply concepts to a problem of personal interest.

The Capstone Research Methods course supports students in developing critical research skills that are needed for the successful completion of their capstone project (Applied Computer Science).

The course provides students with an overview of the research process and types of capstone projects that they can undertake, and includes a detailed exploration of relevant quantitative and qualitative research methods.

Students will develop skills in data gathering and analysis, researching and writing an effective literature review, creating a research proposal, and managing ethical considerations with regards intellectual property rights and research with human subjects.

At the conclusion of the course, students will be required to submit their formal capstone project proposal which should include details of their project scope, research question, hypothesis, and project plan. Their proposal must receive a passing mark before they are allowed to undertake the capstone course in the final term of the degree program.

This course examines the ethical questions that are emerging as a result of rapid technological change. It prepares students to become responsible technologists, and provides a basis for ethical decision-making in their professional work.

It focuses on the promise and potential ethical dilemmas that result from the latest developments of computer science. Topics addressed include ethical decision-making, privacy and confidentiality, safety, manipulation/deception, and the impact of computers on society.

Students are first introduced to a selection of classical ethical theories (e.g., Rights, Justice/Fairness, Utilitarian, etc.). These provide a vocabulary and framework for examining ethical issues in tech. These frameworks are applied to a selection of relevant ethical questions. Examples might include: should social media companies suppress the spread of fake news on their platforms? Is electronic privacy a right? Are there justifiable uses of state surveillance? Should AI be used to “predict” crime by police? What ethical codes should guide AI like self-driving cars? Students read case-studies addressing these questions, write positioning papers, and offer feedback on papers of their classmates. Each live class is a seminar, where students engage in facilitated discussion.

The class culminates in an ethical redesign project where students analyze an existing product that poses ethical questions, then propose specific changes (in software, hardware, UI design, processes, features, implementation) to reduce ethical risks posed by the product.

This course builds on Data Structures & Algorithms 1. Students will explore non-linear data structures, and implement and analyze advanced algorithms.

The course begins with a brief review of basic data structures and algorithms. Students deepen their understanding of searching and sorting, with a focus on describing performance. They learn about advanced data structures including priority queues, hash tables and binary search trees. Students build on their knowledge of graph theory to implement graph algorithms, and explore topics like finding the shortest paths in graphs, and applications of algorithms in maps, social networks, and a host of real-life applications. Other key topics include: divide and conquer recursion, greedy algorithms, dynamic programming algorithms, NP completeness, and case studies in algorithm design.

The course emphasizes big-picture understanding and practical problem-solving in preparation for technical interviews and professional practice. Students will solve common algorithmic problems and participate in mock interview sessions. As part of their regular assignments, they will write technical blog posts to deepen their understanding of these topics and to practice technical communication.

This module expands on the information gained in Fundamental Cyber Attacks and Defensive Tactics and is designed to provide an in-depth exploration of the complex world of cyber threats, attacks, and the cutting-edge defensive strategies employed to counter them. In today's rapidly evolving digital landscape, organizations face increasingly sophisticated cyber attacks that can cripple their operations, compromise sensitive data, and undermine their reputation. This module equips students with the knowledge and skills necessary to understand and effectively combat these threats.

Topics addressed in the module include Kali Linux, Metasploit, Owasp ZAP, Burp Suite, Maltego, and other ethical hacking tools. Students will gain experience with IDS/IPS and learn how to deter and catch hackers. Additionally, students will critically study the pros and cons of antivirus software and how to comprehensively defend endpoint machines.

This course introduces students to the principles of human-computer interaction (HCI). Students explore how humans process information (perception, memory, attention) in the context of designing and evaluating interfaces. This course complements programming coursework by helping students understand how to design more usable systems.

The course builds on previous knowledge of design thinking and expects students to apply the design thinking methodology as a starting point. The first part of the course focuses on designing for multiple platforms. Students create designs that solve a problem on multiple devices (e.g., web, mobile, wearables) and learn how to create a coherent design system as users move between devices. The second part of the course delves into design beyond visual user interfaces, and teaches students how to design for emerging technologies, for example, sensors, controls and ubiquitous computing. Throughout the course, students learn and apply a variety of evaluation methodologies used to measure the usability of design.

This is a project-based course and, in each part, students will work in a team to design, prototype and test a solution to a problem. Students will present their designs in class sessions, and practice giving and receiving meaningful critiques.