国际学生入学条件
Applicants must possess a bachelor's degree from an accredited institution, with a GPA of 2.85 or higher on a 4.0 scale.
Applicants who do not qualify for full matriculation and have an undergraduate GPA between 2.5 and 2.84, may be conditionally admitted at the discretion of the program director.
As data science is an interdisciplinary field, we welcome applicants from diverse professional backgrounds. However, applicants should have the following prerequisites:
One computer programming course
One college-level statistics course
Basic linear algebra
Basic database systems
Students with an insufficient background for direct admission into the Data Science M.S. program may be admitted if they take the required prerequisite course(s), with the approval of the director.
Submit GRE scores:
Graduates of foreign universities are required to take the GRE and submit their scores.
U.S. students with a GPA below 2.85 may, at the discretion of the dean, be asked to take the GRE or other diagnostic tests. Admission will be based upon consideration of test results, previous academic performance, and related employment, if applicable.
Copies of undergraduate transcripts for all schools attended. All final, official transcripts must be received prior to the start of your first semester.
Copy of college diploma or proof of degree
Official GRE scores, if required
IELTS score- 6.0
TOEFL (iBT) score- 79
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雅思考试总分
6.0
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雅思考试指南
- 雅思总分:6
- 托福网考总分:79
- 托福笔试总分:160
- 其他语言考试:Pearson PTE score- 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
我们的网络世界淹没了数据,影响了业务,政府,科学和医疗保健的开展方式。通过提供对数据科学方法和算法的基本了解,NYIT数据科学MS旨在帮助专业人员和研究人员对数据海啸进行分类。到我们的30学分计划结束时,毕业生将具备以下技能和知识:将数据转化为相关见解,从而通过以下方式做出更好的业务决策:运用数据科学概念和方法来解决业务和科学环境中的问题并有效地传达这些解决方案。使用计算理论,语言和算法以及数学和统计模型来制定和实施数据分析。在日常技术和业务活动中应用道德规范,以就数据管理工具的设计和使用做出道德决策。
Learn to master the ever-growing mountain of crucial data that businesses across the globe need help managing and understanding. Prepare for a solid career in data analytics, data management, and more. The Data Science, M.S. is designed for students with a computer science or related background interested in pursuing data analytics, machine learning, visualization, and more. You'll learn how to employ data science concepts and methods to solve problems in business and scientific contexts and communicate these solutions effectively. You'll study computing theory, languages, and algorithms, and use mathematical and statistical models to formulate and implement data analyses and learn to apply them ethically.<br>Our practical curriculum incorporates case studies in advanced topics of computer architecture from classic and modern processors, including large-scale systems. You may also participate in sponsored research projects alongside faculty members from funding sources such as the Department of Defense.
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