MS in Applied Business Analytics

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Industry-focused program prepares you for careers in Big Data Management

Disruptive technologies and globalization have created a huge demand for well-trained business intelligence and analytics professionals. The master of science in applied business analytics in the Cotsakos College of Business’ program addresses this growing need for well-trained professionals with advanced analytical skills. The program is designed to empower students with the knowledge, tools, skills, and industry preparedness they need to lead in the creation of Big Data-driven insights and solutions.

View requirements and course descriptions in the University Course Catalog
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Admission Requirements

In addition to the University’s admission requirements:

  • Baccalaureate degree from an accredited institution (Submission of all official transcripts showing conferral of degree)
  • International students must provide evaluated transcripts by any NACES approved agency. 3 year BA/BS degrees accepted.
  • TOEFL, ELS or IELTS is required for international students whose prior degrees were not taught with English instruction.
  • Min GPA of 3.0
  • Resume
  • GMAT or GRE are optional and are not required

Application Deadline

Recommended: April 15 of each year for a summer start. (will review on rolling basis)

*International students are advised to apply by December 1st: Summer (May intake) and must be full time.

Contact Us

Dr. Rajiv Kashyap
Professor
kashyapr@wpunj.edu
973-720-3850

Program Highlights

  • 30 credits
  • Can be completed in 15 months/one calendar year
  • Part-time or full-time tracks available
  • No GMAT or GRE required
  • Courses are offered in the evenings in hybrid or face-to-face formats.

Integrating math, technology, statistics and business domain expertise, the program curriculum will address emerging Big Data challenges, including the "Internet of Things", business intelligence, optimization, descriptive - predictive - prescriptive analytics, data mining, data warehousing, multivariate analytics, social network and text analytics, and machine learning. Coursework will prepare students to complete industry-relevant projects using real data and a wide variety of industry tools such as R, Tableau, Python, Hadoop, Spark, and others.