国际学生入学条件
Applicants may be considered for admission with conditionally classified standing if they have a BS degree in any engineering or science discipline from an accredited institution.For admission with classified standing, an applicant must possess a BSSE or BSCS degree from an accredited institution with a grade point average of 3.0 or better.IELTS - 6.5. TOEFL - 80 (Internet Based), 550 (Paper Based), 213 (Computer Based).
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IDP—雅思考试联合主办方

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:80
- 托福笔试总分:550
- 其他语言考试:PTE - 53
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
The MSSE program, offered by the Department of Computer Engineering, provides students with an educational experience that builds on traditional computer science and engineering and takes an integrative approach to software engineering. The program integrates the forces shaping software development, including emerging technologies, with the understanding of rapidly changing technologies and architectures and their influence on software engineering processes, where large-scale design is pre-eminent, service and component integration is the standard mode of development, and there is increased globalization of the software development workforce.<br><br>The Data Science specialization prepares students and professionals to investigate and summarize real-world data of all sizes, ask the right questions, find informative answers, and create visualizations that effectively communicate their results. Through a combination of theory and practical data analysis, students learn the foundations of extracting knowledge from data, verifying the utility of the information, and scaling their analysis to Big Data. The program emphasizes teamwork throughout the curriculum, as an essential part of preparing students for working in industry.<br><br>The specialization focuses on a variety of techniques and methods for analyzing data, including data preprocessing, exploratory analysis, unsupervised and supervised inference and learning, association analysis and pattern mining, Web search, text mining, recommender systems, social network and sentiment analysis, hypothesis testing, image recognition, time series analysis, deep learning, and data visualization. Students learn and practice the entire analytics process, from translating real-world objects into data to presenting information gleaned from the analysis.
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