The field of Data Science is a combination of statistics and computer science methodologies that enable ‘learning from data’. A data scientist extracts information from data, and is involved with every step that must be taken to achieve this goal, from getting acquainted with the data to communicating the results in non-technical language. The Data Science Specialist program prepares students for work in the Data Science industry or government and for graduate studies in Data Science, Computer Science, or Statistics. Students in the program will benefit from a range of advanced courses in Computer Science and Statistics offered by the University of Toronto, as well as from a sequence of three integrative courses designed especially for the program.
The Data Science Specialist program comprises three fundamental and highly-integrated aspects. First, students will acquire expertise in statistical reasoning, methods, and inference essential for any data analyst. Seconds, students will receive in-depth training in computer science: the design and analysis of algorithms and data structures for handling large amounts of data, and best practices in software design. Students will receive training in machine learning, which lies at the intersection of computer and statistical sciences. The third aspect is the application of computer science and statistics to produce analyses of complex, large-scale datasets, and the communication of the results of these analyses; students will receive training in these areas by taking integrative courses that are designed specifically for the Data Science Specialist program. The courses involve experiential learning: students will be working with real large-scale datasets from the domain of business, government, and/or science. The successful student will combine their expertise in computer and statistical science to produce and communicate analyses of complex large-scale datasets.
Skills that graduates of the program will acquire include proficiency in statistical reasoning and computational thinking; data manipulation and exploration, visualization, and communication that are required for work as a data scientist; the ability to apply statistical methods to solve problems in the context of scientific research, business, and government; familiarity and experience with best practices in software development; and knowledge of current software infrastructure for handling large data sets. Graduates of the program will be able to demonstrate the ability to apply machine learning algorithms to large-scale datasets that arise in scientific research, government, and business; create appropriate data visualizations for complex datasets; identify and answer questions that involve applying statistical methods or machine learning algorithms to complex data, and communicating the results; present the results and limitations of a data analysis at an appropriate technical level for the intended audience.
This is a limited enrolment program. Students must have completed 4.0 credits and meet the requirements listed below to enrol.
For students admitted to Arts & Science in the Year 1 Computer Science (CMP1) admission category:
Variable Minimum Grade
A minimum grade is needed for entry, and this minimum changes each year depending on the number of applicants. At least 20 spaces will be available each year for students applying from Year 1 Computer Science (CMP1). The following courses must be completed:
To ensure that students admitted to the program will be successful, applicants will not be considered for admission with a grade lower than 70% in CSC110Y1, MAT137Y1, and STA130H1, or lower than 77% in CSC111H1. ( MAT157Y1 grades will be adjusted to account for the course's greater difficulty.) Obtaining these minimum grades does not guarantee admission to the program.
For students admitted to other Arts & Science Year 1 admission categories:
- Students who do not have the Computer Science Admission Guarantee must complete a supplementary application to be considered for the program.
Variable Minimum Grade
A minimum grade is needed for entry, and this minimum changes each year depending on available spaces and the number of applicants. The following courses must be completed:
To ensure that students admitted to the program will be successful, applicants with a grade lower than 70% will not be considered for admission. ( MAT157Y1 grades will be adjusted to account for the course's greater difficulty.) Obtaining these minimum grades does not guarantee admission to the program.
- Requests for admission will be considered in the first program request period only.
- Due to the limited enrolment nature of this program, students are strongly advised to plan to enroll in backup programs.
- Students admitted to the program after second or third year will be required to pay retroactive deregulated program fees.
(13.0-13.5 credits, including at least 1.5 credits at the 400-level)
First year (3.0-3.5 credits)
MAT137Y1/ MAT157Y1, MAT223H1/ MAT240H1 ( MAT240H1 is recommended), STA130H1, ( CSC108H1, CSC148H1)/ ( CSC110Y1, CSC111H1)
Note: Students with a strong background in an object-oriented language such as Python, Java or C++ may omit CSC108H1 and proceed directly with CSC148H1. There is no need to replace the missing half-credit for program completion; however, please base your course choice on what you are ready to take, not on "saving" a half-credit. Consult with the Computer Science Undergraduate Office for advice on choosing between CSC108H1 and CSC148H1.
Students in this program have the option to enrol in the Arts & Science Internship Program (ASIP) stream.
Second year (3.5-4.0 credits)
MAT237Y1/ MAT257Y1, STA257H1, STA261H1, CSC207H1, ( CSC165H1, CSC236H1)/ CSC236H1/ CSC240H1 ( CSC240H1 is recommended), JSC270H1 (Data Science I)
Note: CSC240H1 is an accelerated and enriched version of CSC165H1 plus CSC236H1, intended for students with a strong mathematical background, or who develop an interest after taking CSC165H1. If you take CSC240H1 without CSC165H1, there is no need to replace the missing half-credit for program completion; however, please base your course choice on what you are ready to take, not on "saving" a half-credit. Consult the Computer Science Undergraduate Office for advice on choosing between CSC165H1 and CSC240H1. CSC236H1 may be taken without CSC165H1 for students who completed CSC111H1.
Later years (6.5 credits)
- STA302H1, one of STA303H1 or STA305H1, STA355H1, CSC209H1, CSC263H1/ CSC265H1 ( CSC265H1 is recommended), CSC343H1, CSC373H1, JSC370H1 (Data Science II)
- STA314H1/ CSC311H1/ CSC411H1
- 2.0 credits from the following list, including at least 1.0 credit at the 400-level (see below for additional conditions): STA303H1/ STA305H1 (whichever one was not taken previously), STA347H1, CSC401H1, STA414H1/ CSC412H1, CSC413H1/ CSC421H1, any 400-level STA course; JSC470H1 (Data Science III); CSC454H1, CSC490H1, CSC491H1, CSC494H1, CSC495H1
The choices from 3 must satisfy the requirement for an integrative, inquiry-based activity by including at least 0.5 credit from the following: JSC470H1 (Data Science III); CSC454H1, CSC490H1, CSC491H1, CSC494H1, CSC495H1, STA490Y1, STA496H1, STA497H1, STA498Y1, STA499Y1. Students who complete the Arts & Science Internship Program (ASIP) stream or PEY Co-op will also meet this requirement.
Note: September 2021 or January 2022 will be the last opportunity for Faculty of Arts & Science students to register for PEY Co-op. Students in Year 3 in the Fall/Winter 2021-2022 session will be the last group of Faculty of Arts & Science students eligible to participate in PEY Co-op. Students starting Year 2 in Fall 2021 or later will only be eligible to participate in the Arts & Science Internship Program stream.
Students will be advised to develop domain expertise in at least one area where Data Science is applicable, by taking a sequence of courses in that area throughout their program. Examples of such areas will be provided to students by program advisors and will form the basis for a later proposal for program Focuses (to be approved through internal Arts & Science governance procedures).