Software Engineering Technician – Artificial Intelligence

General Mathematics I

This general course in mathematics reviews and enhances topics in arithmetic, algebra, linear graphs, measurement, and statistics. A problem solving approach is used throughout the course, and there are many practical applications.

Introduction to Artificial Intelligence

This course introduces the core concepts of Artificial Intelligence, its related fields, and its applications. It covers the principles and fundamentals of AI, including its history and current trends. Students will explore the role of AI in various industries, particularly within Canadian business contexts, to understand how AI-powered solutions are applied to real-world problems. The course examines the relationship between AI and big data, highlighting how large data sets drive AI advancements and insights. Additionally, the course analyzes the role of intelligent agents in AI, explaining their functions and behaviors. A significant component of the course is dedicated to exploring various AI learning methods, including supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, and deep learning. Students will also examine artificial neural networks, focusing on their components, strengths, limitations, and applications. Lastly, the course delves into the Internet of Things (IoT) and its connection to AI, providing a comprehensive understanding of how these technologies integrate. The laboratory portion offers hands-on practice, allowing students to apply their knowledge to real-life AI challenges.

ITIL Foundations

The Information Technology Infrastructure Library (ITIL®) foundations course provides a practical understanding of Information Technology (IT) service management key concepts, key principles, common language, and practices that enable the ITIL service value system. This course introduces students to the basic principles and structure of ITIL and provides the preparation needed to challenge and successfully pass the ITIL V4.0 Foundation examination.

Principles of Information Security

Students are introduced to the fundamental concepts, techniques and principles of securing information in computing environments. Students will gain a solid understanding of the key principles, technologies, and security best practices that are crucial for protecting digital assets, protecting privacy of sensitive data, and appropriately managing security risks. Key elements of information security will be taught in depth, including threat and risk assessment, risk management, access control security, cryptography and encryption, network security, identity access management and incident response. Students will learn hands-on techniques that are critical to protect sensitive information from unauthorized exposure, modification, disclosure, and destruction.

Project Management

One of the most in-demand IT skills today isn't really an IT skill: it's Project Management. As more and more demands are placed on IT departments, there is more demand for Canadians who understand and can manage in a project-based environment. Using the principles as defined in accepted industry Project Management standards, this course provides a hands-on framework for how to initiate, plan, execute, monitor and control, and close a project. Through lectures, labs and assignments, students look at all stages of project development with an eye to the tips and tricks that do a successful project. In addition, this course prepares students to challenge the CompTIA Project+ or the Certified Associate in Project Management (CAPM)® certification exams. CAPM and PMI are registered marks of the Project Management Institute.

Workplace Communications

In this course, students write and speak in response to realistic workplace scenarios to build practical communication skills. They produce, revise, and edit written communication geared to the workplace and practice the oral communication and effective listening skills that will help them succeed in professional environments.

Introduction to Sustainable Development

This course introduces the concept of sustainable development to a broad audience. Students are presented with the factors that affect the environmental, economic and social sustainability of our communities. The course investigates the development of human populations and how their need for resources (e.g. energy, food, water) has compromised the natural ecosystems. Social and economic factors are explored in the context of social equality and sustainability in the local and global economy. The effects of individual lifestyle changes on the sustainability of communities are also examined.

Job Search and Success

This course provides student with skills and knowledge to help support their career search and succeed in the workplace. Students align their personal skill set and goals to guide them on their career paths. They will learn how to effectively conduct a job search, build a professional and well-tailored resume and cover letter, and develop and practice interview techniques. Students will also develop their personal brand to help support effective career networking and aid in their job search. Teamwork and collaboration in the workplace are also discussed. Self-reflection is used to inspire insight and support their professional career journey.

Data Science and Machine Learning

This course introduces the fundamentals of data science and machine learning. It covers key principles and concepts of data science, various data types, and common practices in data management. The course also explores the correlation between data science and business, providing insights into how Canadian businesses leverage data science for business intelligence and decision-making. Students will analyze various data science tools, focusing on Python programming language. Additionally, the course delves into data science methodology, guiding students through the common approaches to data science tasks and projects. A significant component of the course is understanding data preprocessing and feature engineering techniques, which are essential for preparing data for machine learning models. The laboratory portion is designed to provide practical applications of the topics covered to Canadian business and industry challenges.

Front-End Development II

This course is a continuation of CSD 1103, Front-End Web Development I, where JavaScript and jQuery are introduced. Students (1) implement JavaScript and jQuery to enhance the user experience; (2) implement various components of JavaScript including the Document Object Model, form validation, and object-oriented programming; (3) employ AJAX to allow server-side requests to exchange data with a web server; and (4) manage events using jQuery.

Natural Language Processing

This course introduces Natural Language Processing (NLP) and its key concepts. The theory part includes the use of classic machine learning methods to solve machine translation, language modeling, and sequence tagging. The laboratory portion is designed to provide students with the opportunity to work with a set of NLP problems and the opportunity to apply their knowledge to resolve them.

SQL and NoSQL Database Design

This course provides a comprehensive introduction to both SQL and NoSQL databases and their applications in artificial intelligence (AI) and machine learning (ML). Students will explore the core concepts of relational (SQL) and non-relational (NoSQL) databases, understanding the differences and use cases for each. The course covers SQL database design, data modeling, and the use of Data Definition Language (DDL) and Data Manipulation Language (DML) to create and manage databases. Students will also delve into the four types of NoSQL databases: Document-oriented, Key-Value Pair based, Column-oriented, and Graph-based databases, with a particular focus on MongoDB. Hands-on experience will be provided through practical projects and assignments that reflect real-world challenges faced by Canadian businesses and industries.

Social Media Analytics

In this course, students evaluate the foundations and strategic use of social media analytics to extract actionable insights. They analyze social media data sources, assess ROI frameworks, and implement advanced AI and machine learning techniques for data modeling, sentiment analysis, and content recommendation. Through hands-on labs, students collect, process, and visualize real-time data to uncover behavioral trends, enhance decision-making, and address privacy and ethical concerns in digital environments.

CI/CD & Configuration Management

General Education Elective

Visualization for AI and ML

This course introduces the core concepts of data visualization for Artificial Intelligence and Machine Learning. The theory part includes an introduction to the foundations of data visualization and data presentation. The laboratory portion provides students with hands-on practices and the opportunity to work with the most advanced visualization tools and libraries to make sense of data, present clear evidence of findings, and tell engaging stories through data graphics.

Advanced Python – AI and ML Tools

This course introduces advanced concepts of Python programming language. The theory includes designing, implementing, and using APIs and advanced modules for AI and ML. The laboratory portion is designed to allow students to work with a set of practical problems and apply their knowledge to real-life software application challenges. Students use Python modules to interact with a database, analyze image datasets, create visualizations, and use PySpark to handle big datasets using the Apache Spark data processing framework.

Data Mining and Analysis

Data mining is a powerful tool used to discover patterns and relationships in data. Students learn how to apply data mining principles to the dissection of large complex data sets, including those in very large databases or through web mining. Students also explore, analyze and leverage data and turn it into valuable, actionable information for an organization

Neural Networks and Deep Learning

In this course, students analyze the structure and theory of neural networks and evaluate their application across domains such as computer vision, speech recognition, and natural language processing. They differentiate core machine learning paradigms, assess key algorithms, and examine optimization and training strategies that support generalization and convergence. Students construct deep neural network architectures and implement advanced models using industry-standard frameworks like TensorFlow, Keras, and PyTorch. Through hands-on work and project-based learning, students gain the ability to evaluate model performance and deliver scalable, task-specific deep learning solutions.

Big Data Fundamentals – Data Storage and Networking

In this course, students will discuss and evaluate the advancement and growth of data storage networking. This course is delivered as a combination of lecture and demonstration, as well as hands-on practice; exercises for the participants will be employed. The course will cover the state of the various data storage technologies available today and how they can be used to develop data storage strategies for business.

Ethical Practices in AI and Data Science

This course explores the ethical foundations, challenges, and governance of artificial intelligence and data science. Students evaluate issues related to data ownership, self-determination, and algorithmic accountability; apply human rights impact assessment frameworks to AI risks; and assess how social values are embedded into system design through stakeholder collaboration. The course also analyzes regional and global AI regulatory models and proposes strategies to address future ethical dilemmas, including equity, sustainability, and digital divides. Through engagement with real-world case studies and emerging policy standards, students develop the capacity to navigate ethical complexity in applied AI contexts.

Capstone Project: Software Engineering with AI