国际学生入学条件
High school Transcripts. Applicants who have attained a high level of academic achievement and who completely satisfy prerequisites will be considered for admission. The minimum requirements for consideration vary by program and admission category. All applicants are required to present a Grade 12 English course for admission consideration.
International Baccalaureate Diploma, with English HL or SL.
International Baccalaureate (IB) English - The minimum requirement is a score of at least 4 (predicted or final) in Higher or Standard Level English A: Literature or English A: Language and Literature. HL English B is not acceptable.
IELTS - The minimum requirement is an overall band of 6.5, with no band below 6.0.
TOEFL IBT - The minimum requirement is a total score of 89 with 22 on the Writing section.
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雅思考试总分
6.5
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雅思考试指南
- 雅思总分:6.5
- 托福网考总分:89
- 托福笔试总分:160
- 其他语言考试:Pearson Test of English (PTE) Academic - Overall score of 65 with no part below 60.
CRICOS代码:
申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
统计科学包含用于从数据中获取知识并理解与该知识相关的不确定性的方法和工具。本科课程的目的是:(1)为学生提供从数据中获取知识的一般框架
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.<br>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.<br>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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