Bachelor of Data Analytics

With the proliferation of data analysis products, data capturing services, and solutions being offered, many organizations are now focused towards using business intelligence techniques to harness the power of data. In this course, students will learn technology-driven skills to gather business and web analytics, implement effective analysis techniques and apply business intelligence strategies to achieve data-driven decisions.

• Hours: 42
• Credits: 3
• Pre-Requisites: PROG71090
• CoRequisites:

• Hours: 14
• Credits: 1
• Pre-Requisites:
• CoRequisites:

This course introduces students to the concept of discovering data patterns for effective data analysis using graphical and non-graphical techniques. In this course, students will learn how to perform explorative data analysis for both univariate and multivariate methods. Topics will include approaches to cover concepts of spotting anomalies, assumption verifications, hypothesis testing, and summarizing datasets.

• Hours: 56
• Credits: 4
• Pre-Requisites:
• CoRequisites:

Organizations these days are morphed into data driven organizations and thus, database administration has become vital. In this course, students will develop their skillset in effective database administration activities by learning how to maintain database integrity, monitor storage structure health, maintain stability of databases, accessibility strategies, troubleshooting, and database security

• Hours: 56
• Credits: 4
• Pre-Requisites: INFO71040
• CoRequisites:

This course introduces the Agile concepts to manage projects. Students will explore Agile values and principles along with how Lean has influenced Agile. In this course, students will learn how to perform key agile functions such as user story creation, estimation, backlog and sprint planning, sprint execution, and scrum, as well as the use of information radiators to monitor sprint progress.

• Hours: 42
• Credits: 3
• Pre-Requisites: BUS71010
• CoRequisites:

The co-op work term will provide students with college-approved work experience within a data analytics 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:

Data analytics is assisting most businesses to achieve their business goals in this hyper-competitive environment. There are numerous applications of Data Analytics from major areas such as marketing, digital advertising, security, research and development, logistics, search techniques and many more. In this course, students will learn about these applications using case studies, use cases, business success stories and examples to relate their data analytics skills with real-world challenges and solutions.

• Hours: 42
• Credits: 3
• Pre-Requisites: INFO72030 AND STAT71000
• CoRequisites:

This course introduces students to one of the main types of Machine Learning: Unsupervised Learning. Unsupervised learning denotes machine learning approaches that can be applied without label information. As such, the aim is to extract patterns or statistical regularities in data, and finding good features is key for the successful application of machine learning models. Students will learn several clustering and dimension reduction algorithms for unsupervised learning to find patterns in unlabeled data.

• Hours: 42
• Credits: 3
• Pre-Requisites: INFO73030
• CoRequisites:

The use of data has become vital for businesses. This course outlines how data-driven approaches can be integrated within a business context and how operational decisions can be made using data-driven methods to adapt the data to various languages and regions without engineering changes and loss of information.

• Hours: 56
• Credits: 4
• Pre-Requisites: ETHS72020 AND INFO72090
• CoRequisites:

Managing the results of machine learning (ML) and artificial intelligence (AI) products requires regular maintenance, organization and redeployment of the products created from data analysis (also known as models). This course will implement data engineering and machine learning operations (MLOps) on real-word data to mimic these activities based on a realistic case study.

• Hours: 56
• Credits: 4
• Pre-Requisites: INFO73030
• CoRequisites:

This course is the second half of a two-semester pairing of project courses that integrates the technical knowledge and skills learned in previous semesters. Students will continue the development and implementation of the projects started in the previous Capstone I project course. The project deliverables will include implementation of the project, project management, critical thinking, research, and communications.

• Hours: 56
• Credits: 4
• Pre-Requisites: INFO73130
• CoRequisites: