Master of Information Technology - Data Science, Analytics, and Artificial Intelligence

The Data Science, Analytics, and Artificial Intelligence (DSAAI) master’s and PhD programs are a natural extension of the popular Collaborative Specialization in Data Science that was launched in 2015 which is offered jointly by several departments. Data Science is a field which has grown in huge leaps. The Collaborative Specialization is focused on interdisciplinary Data Science. In contrast, our full master’s and PhD programs are totally focused on the technical aspect of Data Science. The programs offer opportunities to undertake groundbreaking research in the field of data science and analytics from several directions such as statistics, AI methods and software engineering.

Data scientists and data analytics professionals focus on the collection, preprocessing, exploration, use and visualization of data, be it from sensors, transactional data, or other sources. Students will be exposed to the various methodologies that constitute these areas of DSAAI, and more.

One distinguishing feature of Carleton’s DSAAI program is that it is a joint program between the Faculty of Science and Faculty of Engineering and Design, spanning four academic units – thus, offering a wide disciplinary breadth, providing students with multiple perspectives.

The research areas that comprise the DSAAI include, but are not limited to:

• Data mining considering the algorithms that can be used to reveal patterns in complex data sets, which are then used to extract useable and relevant information. • Statistical measures such as predictive analytics that use extracted data to gauge the likelihood of future events given history. • Machine learning which is an application of artificial intelligence that permits the processing of vast quantities of data that exceed normal computing capacity and call for new approaches, to extract or learn useful algorithms and rules. • Analysis of both stored and streaming data in the context of various systems and applications. • Data analytics platforms and parallel processing techniques to improve the performance of data analysis. • Software systems.

Students will be able to conduct advanced research in this field and will acquire the skills necessary to optimize the relevance, reliability and impact of their research.