Examines typical software packages present in information centres and other business environments. Includes selected topics from the following areas: operating systems; network administration; communication software; word processing; spreadsheets; database management systems and graphics.

This course provides a foundational introduction to data-driven problem-solving. Students will learn to read data, produce insightful graphs, perform hypothesis testing, and apply Bayesian statistics – all with hands-on programming experience. Delves into the core principles of pattern recognition, examining techniques for identifying recurring structures and relationships within data. This will form a crucial foundation for understanding more advanced subjects such as machine learning, where these techniques are extensively applied. Specifically, the course will explore concepts like clustering, dimensionality reduction, and model evaluation, illustrating how these approaches build upon the analytical foundations developed in this course.

Introduction to the analysis of textual data with a foundation on natural language processing and computational linguistics. Students will learn to develop information extraction pipelines and evaluate performance.  

Advanced software packages and programming languages developed for mathematical computations: symbolic, graphical, numerical and combinatorial. Students will be involved in implementing and testing various algorithms. 

This is a case-studies based course in which students learn to analyse data in a modern statistical computing environment. The course promotes the use of graphical and other exploratory techniques as a crucial first step in data analysis. Students will be exposed to practical problems often encountered during the data analysis process. The importance of summarizing and communicating results effectively will be emphasized through the strong project-oriented component of the course. 

This course introduces the fundamentals of the discipline of cybersecurity risk management. Topics include the evolution of information security into cybersecurity, technical aspects of cybersecurity, threat vectors, security domains, standards, frameworks, critical infrastructure, controls developed to manage cybersecurity risk and cybersecurity ethics. Students will be required to complete a cybersecurity risk management related project or case study. This course will provide a practical foundation for students seeking a career in cybersecurity after graduation. Note: This course contributes to the Competence in English Writing requirement.

Data mining (aka knowledge discovery) is an interdisciplinary area of computer science with the goal of extracting new knowledge and insights from big and complex data sets. The course introduces essential pattern recognition methodologies leveraging machine learning and rule-based techniques. Supplementary tasks involving processing, cleaning, integration, and transformation of data are also covered. An etymology of data mining is provided to help students compare and contrast knowledge discovery with contemporary data analytics and decision support methodologies.

Discussion of Data Analysis topics at an advanced level chosen jointly by student, advisor and Department Chair. Topic of course to be entered on the student’s transcript.

Discussion of Data Analysis topics at an advanced level chosen jointly by student, advisor and Department Chair. Topic of course to be entered on the student’s transcript.

Application of correct and appropriate methods of data analysis in one or more areas. A project proposal is required with a final report in which the student describes clearly and concisely the work done, the results obtained, and a careful interpretation of the results in form and language meaningful to workers in the subject area. Students in the Certificate in Data Analytics should choose an industry-related or applied project involving a large amount of data. It should be noted that such a project may require extra time in order to become familiar with the data at hand.