MS in Applied Computing in Data Science

at University Of Toronto

• Students must successfully complete six graduate level courses (totalling 3.0 Full Course Equivalents (FCEs)) as follows:
   

  • One course (0.5 FCE) chosen from the Department of Statistical Sciences course schedule. These must be STA-2000 level courses or higher. The course selection may also include a maximum of two courses (0.5 FCE) chosen from the STA-4500 level of six-week modular courses (0.25 FCE each)

  • STA2453 Data Science Methods, Collaboration and Communication (0.5 FCE)

  • Two courses (1.0 FCEs) chosen from the Department of Computer Science’s (CSC designator) course schedule.

  • Two required courses (1.0 FCEs): Communications for Computer Scientists (CSC 2701H) and Technical Entrepreneurship (CSC 2702H).
     

• An eight-month industrial internship, CSC 2703H (3.5 FCEs). The internship is coordinated by the department, and evaluated on a pass/fail basis. ‘Pass’ grades are awarded based on evaluations received from the industry/academic supervisors of the internship project and submission of an appropriately written final report, documenting the applied research internship.

• Completion of an appropriate undergraduate degree from a recognized university in a related area such as statistics, computer science, mathematics, or any discipline where there is a significant quantitative component.

• A standing equivalent of at least B+ (U of T 77-79%) in their final year (i.e., two terms) of undergraduate studies (the equivalent of 5 full credits, or 10 half credits).

• The program will consider admitting candidates without an undergraduate degree in computer science, statistics, or a related field, but who show a demonstrated aptitude to be an excellent data scientist. Applicants should be able to demonstrate a potential to conduct and communicate applied research at the intersection of computer science, statistics, and a domain area. Background academic preparation to be successful in graduate-level computer science and statistics courses typically, though not always, includes intermediate or advanced undergraduate courses in the following topics: 
   

  • Algorithms and Complexity, Database Systems, or Operating Systems. 

  • Statistical Theory/Mathematical Statistics, Probability Theory, or Regression Analysis.
     

• Applicants must satisfy the admissions committee of their ability to be successful in graduate courses in computer science, statistics, and an industrial internship in data science. Applicants may be asked to do a technical interview as part of the application process.

• Students who are otherwise qualified but lack the appropriate background may be granted conditional admission, pending successful completion of additional background material as judged by the admissions committee.

• Achieved English-language proficiency according to the School of Graduate Studies requirements.

• Applicants will be asked to respond to program-specific questions addressing their interest in the concentration and objectives for the program.

• Three letters of support from faculty and/or employers with preference for at least one such letter from a faculty member in Computer Science/Statistical Sciences.

• Applicants must indicate a preference for the concentration in Data Science in their application. Admission is competitive, and students who are admitted to the MScAC program are not automatically admitted to this concentration upon request.

The tuition fees for MS in Applied Computing in Data Science may vary from $50,000 to $80,000 annually for tuition fees. The fee's prices may get higher for International Students.