国际学生入学条件
Applicants to Pace University graduate degree and certificate programs must hold a bachelor's degree from an accredited college or university if postsecondary education was completed in the United States. Applicants who are currently in their senior year at an undergraduate institution may apply for admission, but acceptance will be contingent upon receipt of a final transcript indicating all senior year grades and receipt of the bachelor's degree. Applicants who have attended institutions outside of the U.S. must hold a degree equivalent to a U.S. bachelor's degree.
A TOEFL score of 78 (Internet version)
An IELTS score of 6.5
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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:78
- 托福笔试总分:160
- 其他语言考试:Pearson PTE score of 52 is preferred
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申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
This program is STEM designated, which means you will be trained in areas of technology that are in high demand with United States employers.In today's data-driven world, analytics is a part of everyday company operation. The difficult part is finding employees trained in data analytics with the skills to not only understand and utilize analytics tools but to follow up their analysis with clear, definitive recommendations for the next step. Successful data analysis pulls from many different skill sets. In order to determine what data to collect, you need to understand the business and market landscape so you can determine what data would suit your purpose. You need to know how to gather that data, what tools are the best to analyse it, how to translate the results into a story the CEO will understand, and how to use that data to make recommendations for enterprise-level decision making that will heavily impact the direction of the organization. The career opportunities around Big Data have never been greater. The MS in Enterprise Analytics combines computer science, statistics, data warehousing, database design, and data mining into a program that includes advanced analytics skills, a foundation in data science technology architectures, database programming, and algorithmic thinking to source data, build queries, develop reports, and build enterprise applications that extract maximum value and recognize patterns or anomalies in Big Data sets.
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