Bachelor of Data Science and Artificial Intelligence (Honours)

Description:

This course introduces the concepts of business intelligence (BI) as components and functionality of information systems. It explores how business problems can be solved effectively by using operational data to create data warehouses and then applying data mining tools and analytics to gain new insights into organizational operations.

• Hours: 42
• Credits: 3
• Pre-Requisites: BUS71010
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Data science has raised many novel ethical dilemmas related to data and how it is used in decision-making. This course will build on the concepts introduced in Law, Privacy, and Ethics to ensure that data science models do not simply replicate pre-existing biases. Students will learn how to apply ethical and practical concepts to identify bias in models, develop preventative measures to limit bias in the data analytic process, and implement procedures to ensure privacy and ethical use of data.

• Hours: 42
• Credits: 3
• Pre-Requisites: LAW71020
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Cloud computing is used almost exclusively in today's digital solutions, requiring a solid foundation in the core concepts of cloud computing technology. Hypervisors, hardware virtualization and common core components of cloud computing infrastructure form the focus of study. Students will develop the skills to install, configure and secure a virtual environment including compute, storage, and networking resources. Students will also be able to evaluate workload requirements to design a basic cloud infrastructure

• Hours: 56
• Credits: 4
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

To ensure high-quality data, companies need to gain broad commitment to data quality management principles and develop processes and programs that reduce data defects over time. This course introduces the key concepts, principles and terminology related to data quality and other areas in data management.

• Hours: 56
• Credits: 4
• Pre-Requisites: PROG71090 AND PROG71100
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Data is the most valuable asset for any organization and thus its security and protection must remain essential policy. This course will cover all internal and external threats associated with data breaches. Students will learn numerous strategies to secure the entire data pipeline. Topics will include security aspects for data confidentiality, availability, data integrity, implementation of security policies for securing data pipeline, risk mitigation and reporting.

• Hours: 42
• Credits: 3
• Pre-Requisites: INFO72050
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description: This course will focus on comprehensive theoretical understanding of group process, personal skill development and application through intensive team work. These skills are of critical importance in both professional and social settings. Through guided exploration and application of theoretical paradigms and practical strategies, students will achieve the necessary skills to succeed in and lead effective teams. The course consists in an intensive experiential approach – learning by doing – enabling participants to become effective, practiced team members with experience applying skills necessary for leadership, analysis and evaluation, problem solving, and conflict management. Individual and team activities enhance participants’ skills to work with a variety of personalities in diverse situations, and to effectively assume various professional roles within a team.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  The cost is included in the course fee. View the eText fee.

Description: Building on skills taught in ENGL71200, this course emphasizes collaborative, problem-solving, and entrepreneurial skills required for advanced scientific and technical communication. Students will work in writing teams, adapt technical content for different platforms, and learn to manage and report on ongoing technical communication tasks. They will identify technical research and documentation needs related to their field of study in order to produce a major course project.

• Hours: 42
• Credits: 3
• Pre-Requisites: ENGL71200
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

This course introduces students to the supervised learning approach for machine learning. Students will learn how to use and design labeled datasets to train algorithms. Students will also work on various algorithms to classify and predict data. Course content will include standard algorithms for classification, such as linear classifiers, support vector machines, decision trees, random forest and for regression such as linear, logistic, and polynomial regression.

• Hours: 42
• Credits: 3
• Pre-Requisites: MATH71010 AND PROG71090 AND STAT71000
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Not only SQL databases are increasing in popularity due to the growth of data. The benefits of scalability and performance make a NoSQL database a compelling choice. In this course, students will design and implement NoSQL databases using systems like CouchDB, MongoDB, Cassandra, or Hive. Students will also use the Hadoop framework to demonstrate the implementation of a NoSQL database in a large-scale storage and data processing model.

• Hours: 56
• Credits: 4
• Pre-Requisites: INFO71040
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Most organizations have multiple sources for receiving corporate data and as a result, data warehousing is a successful approach to integrate all this data. In this course, students will analyze the data needs of a corporation, understand, and implement the stage-by-stage process from data reporting to active warehousing which includes best practices to extract and transform the data into an integrated and comprehensive database.

• Hours: 56
• Credits: 4
• Pre-Requisites: PROG71100
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Artificial Intelligence (AI) technologies are changing at a rapid pace. This course will introduce students to the latest advancements in AI and how they are transforming business processes and society at large. Students will use AI theories to solve problems from various fields while considering the impacts that these advancements have on society.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

The co-op work term will provide students with college-approved work experience within a data science and artificial intelligence environment. Students will be provided an opportunity to: build skills (physical and procedural skills including accuracy, precision, and efficiency); assist in the acquisition of knowledge in and application of knowledge gained in the academic setting (concepts and terminology in a discipline or field of study); develop critical, creative, and dialogical thinking (improved thinking and reasoning processes); cultivate problem-solving and decision-making abilities (mental strategies for finding solutions and making choices); explore attitudes, feelings, and perspectives (awareness of attitudes, biases, and other perspectives, ability to collaborate); practice professional judgment (sound judgment and appropriate professional action in complex, context-dependent situations); and reflect on experience (self-discovery and personal growth from real-world experience)..

• Hours: 420
• Credits: 14
• Pre-Requisites: CEPR71050
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

This course covers the deployment of AI systems into an existing production environment. In this course, students will gain comprehensive knowledge and practical skills necessary for deploying AI systems in real-world scenarios. Students will learn about on-premise and cloud deployment options, cloud platforms and tools and their applicability for running AI applications. Students will learn to address challenges related to scalability, performance, security, ethical considerations, and other related issues for deploying AI systems.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Natural Language Processing (NLP) stands at the forefront of artificial intelligence, enabling computers to understand, interpret, and generate human language. This course aims to introduce fundamental tasks in natural language processing that uses algorithms to interpret and manipulate human language. It offers a deep dive into the theories, algorithms, and practical applications of NLP. Through a combination of theoretical lectures, hands-on projects, and real-world case studies, students will explore the fundamental principles of NLP and develop the skills necessary to building natural language processing (NLP) applications.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Computer vision is a rapidly evolving field that enables machines to interpret and understand visual information from the world around us. This course introduces computer vision, including fundamentals of image formation, camera imaging geometry, feature detection and matching, Multiview geometry, including stereo, motion estimation and tracking, and classification. Students will delve into the algorithms, techniques, and tools used to analyze and extract meaningful information from images and videos.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

Data Science and Artificial Intelligence raise novel ethical concerns and moral dilemmas. Students in this course will explore the interaction of the three elements of ethical data use (fairness, security and privacy) as well as ideas of consensual data use and bias detection. Students will learn to use a variety of approaches to ensure ethical use of data at every stage of the DS/AI pipeline: from data collection to final deployment of solutions.

• Hours: 42
• Credits: 3
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.

Description:

This course is the second half of a two-semester pairing of project courses that incorporates the design, implementation, testing and release of a large scale data science (or AI) project that integrates the technical knowledge and skills learned in previous and the current semesters. Student teams will continue the development and implementation of the projects started in the previous Capstone Project I course. The project deliverables will include team-based implementation, formal testing, validation and deployment of the solution, project management, critical thinking, and research and communications.

• Hours: 56
• Credits: 4
• Pre-Requisites:
• CoRequisites:
• Estimated required text and/or learning resource costs:  Not available at this time.