Key Insights: Masters of Computer Science in Canada

Are willing to study in Canada and planning to start your research for MS in Computer Science from Canada? We have prepared a quick guide to take you through the most important things to look forward, to further in the article. Here are some key things to know about pursuing a Master of Computer Science in Canada:

Entry requirements for MS in Computer Science in Canada:

You will typically need a 4-year?undergraduate degree?in Computer Science,?Engineering,?or a related field with a strong GPA (usually 3.0/4.0 or equivalent) and relevant coursework and projects. Some of the universities may require?GRE scores,?letters of recommendation,?statements of purpose,?etc.

The program structure of MS in Computer Science in Canada:

An?MSCS?in Canada is usually a 2-year program comprising coursework and a thesis or?capstone project. Core courses could include algorithms,?machine learning,?software engineering,?theory of computing, databases etc. You can then choose electives and focus on areas of interest like?AI, networking, security, HCI, etc.

Tuition fees: for MS in Computer Science in Canada

Annual tuition fees for an MSCS in Canada range from around CAD $25,000 to $50,000 for domestic and international students respectively. Living costs in cities are around CAD $15,000 to $20,000 per year. There are scholarships and?funding options?available for eligible students to help with the costs.

Post-graduation opportunities with Post Graduate Work Permit

There are many opportunities for MSCS graduates in software engineering,?data science,?and other tech roles in companies across major Canadian cities. Salaries for these positions range from around CAD $75,000 to $130,000 per year. International students can also explore?permanent residence?in Canada after graduating.

Application process: for MS in Computer Science in Canada

The?application process?involves submitting transcripts and references, a statement of purpose, CV/resume, GRE scores (if required), and an?application fee. The deadlines are usually between December and February for programs starting in September. Some universities allow admissions on a rolling basis.

Here are some statistics on MS in Computer Science programs in Canada:

There are over 200 universities in Canada that offer a?Masters?in Computer Science. Some of the top universities for?MSCS?are?University of Toronto,?University of Waterloo,?University of British Columbia,?McGill University,?University of Montreal, etc.

According to government data, around 14,000 Computer Science and?IT graduate degrees?(including MS, MEng, and PhD) were awarded in Canada in 2018. International students made up about 33% of graduate students in Computer Science and IT programs.

The?job outlook?for MSCS graduates in Canada is very positive. There is high demand for skills in?software engineering,?data science,?machine learning, and other specialized areas. Salaries for?tech jobs?in major cities range from around CAD $75,000 to $130,000 per year for MSCS graduates. Many international MSCS students stay on after graduation to work in Canada under a Post-Graduation Work Permit. This Canadian work experience can help in applying for?permanent residence?in Canada under?economic immigration programs.

Some of the most popular research areas for MSCS in Canada are:

• Artificial Intelligence?and?Machine Learning: This includes research in?deep learning,?reinforcement learning,?computer vision,?natural language processing, robotics, etc. Many leading universities like?University of Toronto, Montreal, and Alberta conduct pioneering research in?AI?and?ML.

• Software Engineering: This includes research in?programming languages,?software architectures,?formal methods, security, privacy, human-computer interaction, and more. Reputed universities like?University of Waterloo?and?McGill University?have strong?software engineering research programs.

• Theory of Computing: This includes research in algorithms,?computational complexity,?formal languages,?quantum computing, and other foundational areas. Universities like University of Toronto and?University of Montreal?have leading researchers and groups in?theoretical computer science.

• Data Science: This includes research in?data mining, databases,?data visualization, statistics, and other areas related to understanding and analyzing data. The University of British Columbia and University of Waterloo have excellent?data science research labs?and faculty.

• Networks and Distributed Systems: This includes research in?networking protocols, network security, networked applications, edge/fog computing,?cloud computing, and other areas. The?University of Calgary?and?University of Ottawa?have strong?research programs?in networks and distributed systems.

• Graphics and?Vision: This includes research in computer graphics, visualization, augmented/virtual reality, and computer vision. The University of British Columbia and University of Toronto have leading research groups in graphics, imaging, and visualization.

Here are some of the latest breakthroughs in theoretical computer science research from Canadian universities:

• Researchers at the?University of Toronto?developed new techniques to solve?hard computational problems?faster. They showed how to solve some NP-hard problems in quasi-polynomial time, a significant theoretical advance.

• Scientists at the?University of Waterloo?invented a new?machine learning model?that can learn to solve?complex games?with?limited data?more efficiently than previous approaches. Their "artificial curiosity" system could have applications in real-world problems with limited data.

• A team at?McGill University?proposed a new?quantum algorithm?that could lead to speed ups over the best known?classical algorithms?for a variety of problems. Their "quantum?approximate optimization algorithm" could have applications in?machine learning,?material design, and other areas.

• Researchers at the?University of Montreal?developed a method to verify the output of?quantum computations?without fully running the algorithm. Their "quantum verification" technique could allow for more efficient checks on the results of?quantum algorithms?and better testing of?quantum hardware.

• Scientists at the?University of Calgary?introduced a new method for?quantum machine learning?that could enable?quantum systems?to learn how to solve complex problems with limited data, an ongoing challenge for?quantum ML. Their "?Sequential Quantum Classification" method shows promising results for classifying data with limited samples.

Here are a few more details on some of these breakthroughs:

University of Toronto?quasi-polynomial?time result: The researchers showed how to solve NP-hard problems like?vertex cover?and dominating set in quasi-polynomial time, meaning time that is a polynomial multiplied by a?logarithmic factor. This theoretical advance could lead to significant efficiency improvements for some hard problems.

Waterloo "artificial curiosity" system: The researchers developed a framework for learning to solve?complex strategy games?with limited data. The system develops an "intrinsic?reward function" to explore the game and discover?optimal strategies, getting better with less data than previous approaches. This could apply to real-world problems with?limited data?like?personalized education?or recommendations.

McGill quantum approximate optimization algorithm: The researchers proposed a?quantum algorithm?that can find?approximate solutions?to?hard optimization problems?faster than?classical algorithms. It could provide speed ups for?machine learning,?material design, drug design, and other problems. They proved that their quantum algorithm can achieve an?exponential speed up?over the best known classical algorithms for finding approximate solutions.

Montreal quantum verification technique: The researchers developed a method to verify the results of a?quantum computation?without fully running the algorithm. Their technique analyzes the "quantum state" output from parts of the computation to check if it matches the?expected state, which can be more efficient than re-running the full algorithm. This could allow for better testing of?quantum hardware?and more efficient checks on the outputs of?quantum algorithms.

Calgary sequential quantum classification: The researchers introduced a new?quantum machine learning algorithm?that can learn how to classify data with limited samples. Their "sequential?quantum classification" method shows promising results for classifying data with few samples, which is a key challenge for?quantum machine learning. This could lead to?quantum systems?that can learn complex tasks with limited data in the future.

Please read this blog if you are looking for some assistance in writing SOP for MS in Computer Science.