Computer Science: Applied Computing MScAC (Data Science for Biology Concentration)

MScAC Program (Data Science for Biology Concentration)

Minimum Admission Requirements

  • Applicants are admitted under the General Regulations of the School of Graduate Studies. Applicants must also satisfy the Department of Computer Science's additional admission requirements stated below.

  • An appropriate bachelor’s degree from a recognized university in an area such as life sciences, biochemistry, medical sciences, computer science, biotechnology, biostatistics, engineering, or a related discipline.

  • A standing equivalent to at least B+ in the final year of undergraduate studies.

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

  • 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 excel in this concentration. Applicants should demonstrate a potential to conduct and communicate applied research at the intersection of computer science, statistics, and cell biology. 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.

  • Applicants whose primary language is not English and who have graduated from a university where the primary language of instruction is not English must submit results of the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) with the following minimum scores:

    • Internet-based TOEFL: 93/120 and 22/30 on the writing and speaking sections.

    • IELTS: an overall score of 7.0, with at least 6.5 for each component.

  • If students complete a portion of their degree in English, or part of their degree at another university where English is the language of instruction, applicants must still provide proof of English-language proficiency.

  • Three letters of support from faculty and/or employers, with preference for at least one such letter from a faculty member in biology or data science.

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

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

Program Requirements

  • Coursework. Students must successfully complete a total of 3.0 full-course equivalents (FCEs) including:

    • 1.0 FCE chosen from Cell and Systems Biology (CSB), Ecology and Evolutionary Biology (EEB), Molecular Genetics (MMG), or Statistical Sciences (STA) 1000-level or higher courses from the approved list below. A maximum of 0.5 FCE may be selected from EEB, MMG, and STA courses.

    • 1.0 FCE chosen from the Computer Science (CSC course designator) graduate course listings from the approved list below and in two different research areas.

    • 1.0 FCE in required courses:

      • CSC2701H Communication for Computer Scientists (0.5 FCE) and

      • CSC2702H Technical Entrepreneurship (0.5 FCE).

  • Course selections should be made in consultation with the Program Director. Appropriate substitutions may be possible with approval.

  • An eight-month industrial internship, CSC2703H (3.5 FCEs). The internship is coordinated by the department and evaluated on a pass/fail basis.

Program Length

4 sessions full-time (typical registration sequence: F/W/S/F)

Time Limit

3 years full-time

Approved CSB, EEB, MMG, and STA Courses

Course Code Course Title
CSB1018H Advanced Microscopy and Imaging
CSB1020H Topics in Cell and Systems Biology
CSB1021H Topics in Cell and Systems Biology
CSB1025H Methods in Genomics and Proteomics
CSB1472H Computational Genomics and Bioinformatics
EEB1460H Molecular Evolution
MMG1344H Foundational Computational Biology I
(exclusion: MMG1004H)
MMG1345H Foundational Computational Biology II
(exclusion: MMG1004H)
STA1008H Applications of Statistics
STA2005H Applied Multivariate Analysis
STA2016H Theory and Methods for Complex Spatial Data
(prerequisite: STA302H1)
STA2052H Statistics, Ethics, and Law
STA2053H Special Topics in Applied Statistics
(prerequisite: graduate-level statistical knowledge with permission of the instructor)
STA2080H Fundamentals of Statistical Genetics
STA2453H Data Science Methods, Collaborations, and Communication

Approved Computer Science Courses

Course Code Course Title
CSC2221H Introduction to the Theory of Distributed Computing
CSC2224H Parallel Computer Architecture and Programming
CSC2231H Special Topics in Computer Systems
CSC2240H Graphs, Matrices, and Optimization
CSC2306H High Performance Scientific Computing
CSC2412H Algorithms for Private Data Analysis
(prerequisite: CSC373H1 or equivalent, or permission of the instructor)
CSC2431H Topics in Computational Biology and Medicine
CSC2501H Computational Linguistics
CSC2506H Probabilistic Learning and Reasoning
CSC2508H Advanced Data Systems
CSC2511H Natural Language Computing
CSC2514H Human-Computer Interaction
CSC2515H Introduction to Machine Learning
(exclusion: ECE1513H)
CSC2516H Neural Networks and Deep Learning
(exclusion: MIE1517H)
CSC2520H Geometry Processing
CSC2524H Topics in Interactive Computing
CSC2526H HCI: Topics in Ubiquitous Computing
CSC2529H Computational Imaging
CSC2530H Computer Vision for Advanced Digital Photography
CSC2537H Information Visualization
CSC2547H Current Algorithms and Techniques in Machine Learning
CSC2556H Algorithms for Collective Decision Making
CSC2558H Topics in Multidisciplinary HCI
CSC2604H Topics in Human-Centred and Interdisciplinary Computing
(prerequisite: CSC311H1 or CSC2515H or equivalent)
CSC2626H Imitation Learning for Robotics