Statistics Second Year Book Pdf

[Mariam Obregon]

ThePersistence and Retention report series examines first-year persistence and retention rates for beginning postsecondary students. The persistence rate is measured by the percentage of students who return to college at any institution for their second year, while the retention rate represents the percentage of students who return to the same institution. Students attaining a credential in their first year are accounted for in persistence and retention rates. The report is designed to help institutions understand trends and patterns in this important early success indicator, and identify disparities by institutional type, state, degree level, starting enrollment intensity, major field, and student demographic characteristics such as age, gender, and race and ethnicity. The report includes a data dashboard to enable viewers to analyze, visualize, and interact with the longitudinal data, which are also available for download.

Statistical Science is the science of learning from data. Statistical science plays a large role in data science, which broadly encompasses computational and statistical aspects of managing and learning from large and complex datasets. Statistical theory and methodology have applications in almost all areas of science, social science, public health, medicine, engineering, finance, technology, business, government and industry. Statisticians and data scientists are involved in solving problems as diverse as understanding the health risk of climate change, predicting the path of forest fires, understanding the role of genetics in human health, and creating a better search engine. New ways of collecting, organizing, visualizing, and analyzing data are increasingly driving progress in all fields and have created demand for people with data expertise.

The Department of Statistical Sciences offers specialist, major, and minor programs in Statistics and a specialist program in Data Science and a specialist and a major program in Actuarial Science (please refer to the Actuarial Science section of the academic calendar for more information on Actuarial Science programs). All Statistics programs offer training in statistical methods, theory, computation, and communication, as well as an understanding of the role of statistical science to solve problems in a variety of contexts. The specialist program in Statistical Science: Theory and Methods emphasizes probability and statistical theory as underlying mathematical frameworks for data analysis. The specialist program in Statistical Science: Methods and Practice has greater emphasis on collaborative statistical practice. Students in this program combine their study in statistics with a focus in a discipline that relies on statistical methods. The specialist program in Data Science is offered jointly with the Department of Computer Science. Students in this program acquire expertise in statistical reasoning and methods, in the design and analysis of algorithms and data structures for handling big data, in best practices for software design, and in machine learning. The major program in Statistics offers the most flexibility in the choice of courses. This program gives students a broad understanding of the methods and computational and communication skills appropriate for effective statistical problem solving. The minor program in Statistics is designed to provide students with some exposure and skills in statistical methods which is intended to complement programs in other disciplines that involve quantitative research.

Variable Minimum Grade

A minimum grade is needed for entry, and this minimum may change each year depending on available spaces and the number of applicants. Eligibility is based on the following criteria:

Obtaining this minimum grade does not guarantee admission to the program. If students have completed more than one of the above courses at the time of admission, the minimum grade will be based on the higher course grade.

Note: Students enrolled in this program cannot be simultaneously enrolled in or complete any Computer Science or Statistics programs, including the Computer Science Minor, Statistics Minor, and Data Science Specialist; nor the Focus in Data Analytics within the Economics Major or Specialist; nor the Focus in Data Science in Business within the Rotman Commerce specialist programs.

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.

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) within 12 months of beginning their studies:

* STA130H1 is restricted to first-year students, therefore students are strongly encouraged to take STA130H1 in their first year. STA261H1 will be used in place of STA130H1 for program admission purposes if a student has not completed STA130H1 or if they have completed both STA130H1 and STA261H1 by the time they are being considered for admission.

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.

Students in this program have the option to request enrolment in the Arts & Science Internship Program (ASIP) stream. Students can apply for the ASIP stream after Year 1 (Year 2 entry) or after Year 2 (Year 3 entry). Full details about ASIP, including student eligibility, selection and enrolment, are available in the ASIP section of the Arts & Science Academic Calendar. Please note that space is more limited for Year 3 entry and students applying for Year 3 entry must have been admitted to the Data Science Specialist in the Summer after Year 2.

Transfer credits (except for those attained through a University of Toronto exchange program) cannot comprise more than 1.0 credit at the 300-/400-level, and cannot be used to satisfy the requirement for an integrative, inquiry-based activity. In addition, transfer credits (except for those attained through a University of Toronto exchange program) cannot comprise more than 0.5 credit of the 400-level CSC or or STA or JSC courses required.

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).

Note:

-If you do not complete STA130H1 in your first year of study, this requirement must be fulfilled by completing a 300 or 400-level 0.5 credit STA course to replace STA130H1. Please note that the 300 or 400-level STA course used to replace STA130H1 cannot be a course that is already being used to meet a program completion requirement.

Statistical Science encompasses methods and tools for obtaining knowledge from data and for understanding the uncertainty associated with this knowledge. The purposes of the undergraduate programs are to: (1) equip students with a general framework for obtaining knowledge from data; (2) give students skills that they are able to flexibly apply to a variety of problems; and (3) to provide students with the ability to learn new methods as needs, data sources, and technology change.

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