University of Toronto
The Master of Science in Applied Computing (MScAC) degree program prepares students for lifelong success as technical leaders in information technology.
The program begins with eight months of advanced courses focusing in computer science or data science, studying with Canada’s leading researchers. This is followed by an eight-month internship at an information technology company where you will apply research results to real-world problems.
University of Toronto’s program is similar to many in that it has a work-study component to it, but this program stands out for having the in-class portion and the internship portion be the same length of eight months each. This shows a focus on the practical, job-focused applications of the study over a purely theoretical approach.
Courses center on technical skills needed by working data scientists but also look at the human element, including technical entrepreneurship and communication skills for computer scientists.
University of Waterloo
Graduate programs incorporate elements of statistics, computer science, and optimization. The need for integrated graduate training across these disciplines is acutely felt across all industries. Waterloo’s aim is to provide breadth and depth in all three of these areas as they pertain to the emerging and world-recognized discipline of Data Science.
The Master of Data Science and Artificial Intelligence (MDSAI) is a coursework program with a co-op option. The degree requirements include nine graduate courses relevant to data science.
The Master of Mathematics (M.Math) in Data Science is a research-based Master’s program with thesis. Students are expected to complete the program in four to six terms. The principal degree requirements are four courses and a thesis.
Waterloo’s programs highlight two divergent paths in data science – the AI based approach and the pure math based approach. They diverge so far that they are two different degrees at Waterloo, each taking elements from the other but staying in its own lane.
Courses revolve around the structural elements of data science including computer science, statistics, optimization practices, and research training.
University of British Columbia
Data is everywhere. Continuously generated and collected across every domain, it is a vast and largely untapped resource of information with the potential to reveal insights about every aspect of our lives and the world we live in. However, the ability to uncover these insights is a highly specialized skill possessed by far too few.
UBC’s Master of Data Science program was designed to address this workforce gap by equipping students with the technical skills, practical experience, and most importantly, the confidence to seize opportunities in an ever-expanding field.
UBC’s program is one of the fastest of all, taking only 10 months. This means students get out into the workplace faster with their skills, but also means the program can be more intense than others due to its shorter time frame.
Courses cover the practical elements of data science including programming for data science, descriptive statistics and probability, building algorithms, and making statistical inferences.
Vancouver and Okanagan Valley
Smith School of Business at Queen's University
Realizing the promise of data analytics requires businesspeople who can find the opportunity in the numbers. That’s what students get in this program.
Global business leaders recognize the need to fill the growing talent gap of managers who can make business decisions with data. Teaching students how to unleash the potential of data gives them and the organizations they represent a competitive advantage.
Queen’s University’s program is flexible in terms of location, setting it apart from other programs. They have one program in Toronto where you can work full time and take courses in the evenings and on weekends. The other option is a full-time course distributed online and throughout their global campuses.
Courses focus on the holistic lens of big data, including data acquisition and management, ethics of AI, analytical decision making, and data in entrepreneurship.
Toronto, Kingston, Online
Data is the currency of the 21st century. The pace of its production increases exponentially each year. Its full potential has yet to be tapped but governments, businesses and individuals across the globe are increasingly faced with the challenges and the opportunities presented by data generation, storage, analysis, application and innovation. Addressing the rapidly evolving big data landscape, public and private sector organisations search for data analytics experts, a new generation of highly qualified personnel who are able to work with many different types, quantities and levels of complexity of data.
Data analytics is inherently interdisciplinary. The foundation for descriptive, predictive and prescriptive analytics lies in a set of tools and techniques from Statistics and Computer Science, as well as from the intersection of these two fields.
Western’s program may be the most “beginner” focused of all the programs. Its website language focuses more on creating data foundations to build the generalist practitioner – something that fits well when you consider Western is famous for its business school.
Courses look at practical elements of data science such as managing unstructured data, business skills for data scientists, and data consulting.
Schulich School of Business
The 12-month Master of Management in Artificial Intelligence (MMAI) is designed to meet the growing need for agile, talented individuals with both management skills and advanced applied knowledge of AI. The immersive curriculum offers students a technical foundation in natural language processing, algorithmic business analysis and modeling, paired with core business skills like interpersonal management and case analysis.
During the capstone Artificial Intelligence Consulting Project (AICP) students will have the opportunity to solve a significant business problem by designing an AI-centered approach. Over two terms, teams will work in the Schulich Deloitte Cognitive Analytics and Visualization Lab and at a client site, interacting with industry managers, technicians, suppliers and other stakeholders.
York’s program goes all-in on AI, putting it at the forefront of their entire degree. They then tie it directly into management, linking AI and business inextricably, a signal of the types of people they want to attract. This is further entrenched by their partnership with the Deloitte Cognitive Analytics and Visualization Lab for students to get hands-on industry learning.
Courses take a heavily-AI focused approach and look at things like AI fundamentals, societal implications of AI, and building AI algorithms for business.
Get the tools to take on one of the greatest management challenges in the age of digital technology and artificial intelligence: become an expert in analytics and data science. Students will acquire the skills needed for better decision making, making them more competitive and innovative.
Students learn concepts, models and advanced methods in optimization, modelling, statistics and machine learning as applied to management. Further, students learn management decision-making skills: building complex data or optimization models, analytics and problem solving using programming languages and appropriate software. During student, students can choose the supervised project track to gain real work experience, or explore a specific area of interest in the research-oriented thesis track.
HEC Montreal offers two paths for this program, geared towards the researcher or the practitioner/entrepreneur. Students can either take a path that is roughly 50:50 between courses and a research thesis or a second path is that primarily course based with approximately 20 percent of your time spent on a supervised project, whether case study, niche research, or an entrepreneurial endeavor.
Courses focus on in-depth analytics, financial economics, financial engineering, UX, and more – all using data to inform decision making.
Students will earn their degree in one of 13 academic disciplines at Carleton with a specialization in Data Science or a concentration in Business Analytics for the MBA. Participants will pursue a thesis, coursework-only or project option that is directly related to Data Science, that’s related to their original degree. Business students are also required to complete an internship. Depending on availability, students in other fields may also gain real-world experience through internships.
Data Science at Carleton is considered a specialization or concentration as a part of one of 13 participating Master’s programs at the university. This approach allows students to take as practical or theoretical an approach as they like, since participating programs vary from history to business to electrical Engineering.
Which courses you take will depend on the department in which you’re studying, but popular courses include business analytics, communications data science and data in health sciences. Programs require students to complete either coursework or a thesis component.
This unique one-year full-time or two-year part-time Master of Science (MSc) degree program enables students to develop interdisciplinary skills, and gain a deep understanding of technical and applied knowledge in data science and analytics. Graduates are highly trained, qualified data scientists who go on to pursue careers in industry, government or research.
Ryerson aims to give its students hands-on knowledge with data analytics in their chosen field. While the MSc is its own degree, Ryerson takes a very career-driven approach to teaching, using language like how they turn students into “unicorn” (i.e. highly sought-after) job candidates.
The program is comprised of both required and elective courses, including: Designs of algorithms; Management of big data and big data tools; Social media analytics; Advanced data visualization; and Natural language processing (text mining).
The Big Data Analytics Stream is a Master’s of Science degree focusing on the rapidly growing field of data science. A professional program that can normally be completed 16 months, this exciting new stream prepares graduates with the tools and techniques they need to work with, and understand big data.
The field of data science is an evolving one, requiring skills from application domains, mathematics and computer science. Studies in this program focus on the foundations of data modelling, the core mathematical concepts behind data analytics, and visualization in order to take raw data into a useful format.
Trent University’s program is deeply technical in how it presents itself. Course work focuses on modelling, principles, and data mining – probably the most arduous and complicated element of data science. It takes a purest approach, not necessarily connecting it to business right off the bat.
Courses look at the foundational principles of data science including data mining, data visualization, database management, and computational modelling.
Simon Fraser University
SFU’s School of Computing Science offers a cutting-edge Professional Master of Science in Computer Science with a specialization in Big Data. With its paid co-op practicum, the program prepares students to become data architects capable of generating powerful insights that enlighten and inform decision-makers.
SFU touts its professionally-focused program while also showcasing its ranking as a top tier research university with a top 50-globally computer science department. This combination means that students would get the intensity of research combined with the practicality of professional degrees.
Courses focus on cutting edge tech platforms, including: cloud computing (Amazon, Google, etc.); big data storage and analytics; building scalable machine learning models; and building distributed algorithms across matrices.
Burnaby, British Columbia