Bachelor of Science in Computer Science

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 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 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.

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.

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.

Data drives more and more software, from social networks to self-driving cars. In order

to build applications using that data, engineers design systems to get the data from

where it's collected to where it's analyzed and consumed.

This module bridges what students learn in the Databases and Data Science modules,

connecting the theory of data science to the concrete how-to practice of handling data.

The course uses the fundamental constraints of processors, storage, and networks --

how fast can data be processed, how much can be stored, and how fast does it move --

as a frame for data engineering decision-making.

Students will design and build real data pipelines in this module. They'll use a range of

industry-standard tools and platforms, and learn to be savvy and scrappy in the tools

they choose. Students will learn to test their data pipelines in different ways, including

statistical tests, load tests, and monitoring.

This course provides students with the tools and techniques to secure systems across diverse computing environments, from on-premise servers to cloud platforms. Students begin by mastering advanced Linux administration skills, including piping, redirection, and system scripting. They then examine common threats to enterprise infrastructure, such as Active Directory exploits, and explore mitigation strategies grounded in security best practices.

The course expands into cloud computing, where students evaluate service models and deployment architectures through a security lens, focusing on access control, encryption, and configuration management. Emphasis is also placed on data privacy regulations, secure communication protocols, and regulatory compliance. The course concludes with hands-on system hardening projects in which students automate security enforcement and implement continuous monitoring for threat detection and system health.

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).

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 introduces students to the principles of secure system and network architecture, equipping them with the tools to identify structural vulnerabilities and design resilient infrastructure. Students explore foundational security frameworks and architectural models, including layered defense, network segmentation, and cloud-native security patterns.

Through case studies and hands-on exercises, students assess architectural weaknesses and evaluate how insecure design contributes to real-world breaches. Key topics include vulnerability management, secure IT architecture, cloud environment hardening, and zero trust principles. The course culminates in a project where students evaluate an enterprise architecture and propose security enhancements aligned with best practices.

This course introduces students to the field of Cyber Threat Intelligence (CTI) and its role in shaping organizational defense strategies. Students explore the evolving threat landscape, learning how to collect, evaluate, and apply threat intelligence using industry-standard models such as MITRE ATTACK.

Emphasis is placed on using threat intelligence to inform incident response, detect adversary behavior, and enhance system resilience. Students also examine the role of Governance, Risk, and Compliance (GRC) frameworks in operationalizing security strategy, ensuring regulatory alignment, and driving risk-based decision-making. The course culminates in an applied project where students analyze SIEM data and construct a strategic response plan integrating CTI and GRC principles.

This course prepares students to work with large-scale data in modern, cloud-based environments, integrating advanced data processing techniques with visualization and generative AI tools. Students learn to use PySpark to efficiently manipulate structured and semi-structured datasets across distributed computing platforms, combining Python, SQL, and Spark in cohesive data workflows. Students explore foundational libraries such as NumPy and Pandas for data wrangling and analysis, while also creating compelling visual narratives through Seaborn and other visualization tools. Emphasis is placed on the role of dashboards in communicating data insights, and students are introduced to generative AI techniques that enhance exploratory analysis and automation. The course culminates in a final project that brings together cloud-scale data pipelines, exploratory analysis, and interactive dashboards to communicate actionable insights.

Engineering for Development, Challenge Studio 1, and Challenge Studio 2 are 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 Engineering for Development, students will learn how to analyze the root causes of development challenges so that they are able to build effective technology solutions. The course aims to introduce students to selected global development challenges using the United Nations Sustainable Development Goals (SDGs) as the framework for selecting the areas of focus.

Each term, the course will focus on 1- 2 subject areas (e.g. Quality Education, Affordable and Clean Energy, Climate Action), which will serve as test cases for students to develop the skills required to effectively analyze and understand complex development issues. Students will examine the system level dynamics that are at the root of these challenges, and will also analyze and critique technology related solutions that have been developed to address these challenges.

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.

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.

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.

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 introduces students to foundational and applied techniques in natural language processing (NLP), time series forecasting, and neural network modeling. Students begin by exploring core NLP tasks such as text classification, vectorization, and tokenization, using real-world datasets to extract meaning from language-based data.

The course then turns to time series analysis, where students learn to manage temporal data, visualize trends, and build predictive models using established statistical and deep learning techniques. Finally, students gain hands-on experience with basic neural network architecture and implementation using the Keras framework, applying these models to language, time, and image data. The course culminates in a project where students design and evaluate three models, demonstrating technical fluency across diverse data domains.

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.

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 introduces students to the foundational concepts and techniques of machine learning, with a focus on supervised learning models and the data science workflow. Students begin by exploring the mathematical and statistical underpinnings of predictive modeling, including key ideas from statistical learning theory.

Using real-world datasets, students apply a range of classification and regression models, such as logistic regression, decision trees, and support vector machines. Emphasis is placed on data preprocessing, feature extraction, model evaluation, and deployment strategies. By the end of the course, students will have developed and deployed a machine learning model, demonstrating their ability to move from raw data to an operational predictive solution.

This course introduces students to statistical inference, equipping them with both the theoretical foundation and the practical tools needed to derive insights from data. Students begin by exploring probability distributions, confidence intervals, and hypothesis testing for single variables and proportions. The course then expands to inference methods for comparing two or more groups and analyzing relationships in multivariate datasets.

Students work with large-scale data using Python and PySpark, applying inference techniques to real-world datasets involving means, categorical variables, and proportions. Emphasis is placed on interpreting results in context and communicating findings effectively.

The course culminates in a final project in which students conduct a full inferential analysis using a multivariate dataset.

This course provides students with the tools and techniques to build, evaluate, and interpret regression models for real-world datasets. Students begin with simple linear regression and progress to multiple linear regression, learning how to assess model fit, interpret coefficients, and diagnose common modeling issues.

The course explores advanced modeling strategies, including interaction terms, polynomial transformations, and model selection techniques. Students also examine the bias-variance tradeoff and apply regularization methods such as Lasso and Ridge to prevent overfitting in high-dimensional datasets. Emphasis is placed on both statistical theory and computational implementation. The course culminates in a final project where students build and analyze a multiple regression model, drawing meaningful insights from data.

This course extends penetration testing skills into advanced and applied domains, with a focus on securing web and mobile applications, understanding modern cryptographic challenges, and addressing real-world attack surfaces in enterprise environments. Students begin with web application security, investigating common vulnerabilities, secure coding practices, and industry-standard testing tools.

Building on this foundation, students explore Active Directory attack methods and defenses, operational technology (OT) security best practices, and emerging topics such as AI-powered attack techniques and quantum-resistant cryptographic principles. The course concludes with an in-depth study of mobile device security, covering threat vectors, system configuration, and automated scan scripting. Students synthesize their learning in a comprehensive web application penetration testing project.

Back End Development builds on previous knowledge of web development and security, and equips students with knowledge of server-side development so that they can become professional back-end developers and build enterprise-scale applications. Students learn to develop and deploy server-side applications with greater scope and complexity.

In this project-based course, students deepen their understanding by building the back end for a cross-platform application. The project will require implementing advanced features that add complexity and uniqueness to a server’s structure. Examples of these include payment gateways, chat rooms, full text search, WebSockets, etc. Students will design and build out all of the API endpoints needed for the application and properly secure them for use in any web or mobile front-end application. In doing so, they will explore the differences and tradeoffs between web services, APIs, and microservices. They will learn best practices for code quality including unit testing and error handling. They will also learn to efficiently document their APIs.

Students will understand key Developer Operations (DevOps) practices including environment design, testing, development controls, and uptime management. They will implement modern DevOps workflows (e.g., containers, cloud virtual machines), and learn tradeoffs between different approaches. They will set up continuous integration and continuous delivery, and explore various strategies for automated testing and application monitoring.

This course equips students with the knowledge and hands-on skills needed to investigate, analyze, and respond to cybersecurity incidents. Students explore the entire incident response lifecycle, beginning with threat detection and investigation, and progressing to containment, eradication, and recovery.

Key topics include digital forensics, malware analysis, and memory forensics across Windows and Linux systems. Students also apply Python-based techniques to evaluate and secure APIs, integrating automation into the response process. Emphasis is placed on developing mitigation strategies, remediation plans, and thorough documentation to improve organizational resilience. The course culminates in a simulated incident response exercise where students apply their knowledge to a realistic cybersecurity scenario.

This course prepares students to design, deploy, and maintain large language model (LLM) applications using modern machine learning operations (MLOps) practices. Students explore the open-source MLOps stack to manage the full machine learning lifecycle, including model deployment, version control, monitoring, and iterative improvement.

The course emphasizes data-centric approaches to improving LLM performance through high-quality data preprocessing and curation. Students gain hands-on experience with fine-tuning pre-trained transformer models and apply prompt engineering techniques to optimize outputs for business use cases such as summarization, classification, generation, and task automation. By the end of the course, students will be equipped to operationalize and sustain advanced AI systems in production environments.