The College of Management (COM) at LIU Post offers a new, 30-credit, specialized Master of Science in Data Analytics and Strategic Business Intelligence program (MDA) with a blend of data science, IT, and business courses to prepare students for the demand in industries for data-literate managers and data scientists with solid business knowledge and analytical skills.
The MDA program is designed for recent graduates or working professionals in their early or mid-career phases. The ideal student should have basic quantitative and IT knowledge and skills through prior coursework or working experience, and be highly motivated to learn how to deal with data as a strategic asset and apply IT and analytic methods to make data-driven business decisions.
The program is a STEM Designated Degree Program, which allows international students eligible to apply for a 24-month STEM OPT extension after their initial post-completion OPT.
Dr. Jiaman Wang, Chair
Each applicant should submit the following items for admission:
The curriculum provides students with fundamental data-driven analytical methods and skills to interpret and present digital data and produce practical and meaningful insights of customers, products, services, and marketplaces, which can lead to better, more informed business decisions, innovative business models and sustainable competitive advantages.
Towards the end of the program students will have the opportunity to apply classroom knowledge in real-life data analytics problems through the required course of Global Capstone Action Learning Internship.
Upon completing the program students should develop applied knowledge and interdisciplinary understandings of data asset, data collection, data management, data communication, data storage, data visualization, data mining, machine learning, data security, information privacy, and business intelligence in the industries of consulting, accounting, finance, marketing, IT, supply chain and logistics, gaming, sports, fashion, or health care.
The program takes place on the LIU Post campus with classes held during the evenings and weekends. Students may take the majority of the coursework in the fall and spring semesters, and complete the required analytics capstone internship in the following summer before graduation.
The program offers both full-time and part-time options. Full-time students can complete the program in one year. Part-time students may spread their studies over a longer time frame, and complete the program in two years.
Students take two graduate-level courses offered by the COM (6 credits) in the following graduate programs:
This career-oriented capstone course provides students in the latter stage of the program synthesizing, practical, in-depth field experience of working with business organizations on a real-world data analytics project based upon a learning contract approved by the program director and the mentor in the hosting or sponsor firm either in the United States or abroad. Each internship requires at least one month or 100 hours under supervision of a data analytics practitioner on site. At the end of the internship the student will prepare a substantial Capstone Project Report concerning his/her experience, and give a presentation to invited executives and program sponsors.
The MDA program is seeking internship sponsor/host organizations around the world. Please contact Dr. Ling Zhu, the program director, at firstname.lastname@example.org for the opportunity.
Ph.D. in MIS, The University of Arizona; J.D., Fordham University
Expertise: information privacy law, corporate IT strategy, blockchain technology and intellectual property, and global data protection policy
Ph.D. in Management Science and Engineering, Tsinghua University; Post-doctoral Fellowship in Operations Management, The University of Toronto
Expertise: business analytics, business statistics, service operations, and decision analysis
Ph.D. in Management Science, University of Pennsylvania
Expertise: business statistics, operations management, and optimization
Ph.D. in Marketing, Cornell University
Expertise: digital marketing
Ph.D. in Accounting, Rensselaer Polytechnic Institute; Ph.D. in Finance, University of New Orleans
Expertise: accounting and financial analytics
Ph.D. in Computer Science/Artificial Intelligence, University of Chicago
Expertise: Blockchain Technology in securities, machine learning, innovative tech enterprises, and entrepreneurship
Executives, representatives and scholars from the following institutions and organizations have been invited to serve on the advisory board of the program:
The COM and the Nespola Foundation, in the name of LIU Trustee Mr. Richard Nespola, have established the Nespola Scholars program to offer merit scholarships with limited monetary award to successful applicants in the MDA program.
To apply for the Nespola Scholars, the candidate should:
o Transportation (including any logistics services)
o Higher education (applying data analytics at the college level for purposes of student enrollment and retention in an era of demographic change)
o Sports (applications of sports analytics)
o Environment (application of environmental analytics in climate services, weather forecast, natural disasters, smart cities, sustainable growth, and renewable energy)
o Cancer research (applying big data analytics and cloud computing to cancer research)
A recipient of the scholarship must make a written commitment to matriculate in the MDA program. Before graduation, the recipient should prepare a two-to-five-page report and give a presentation to Trustee Nespola on his/her findings of transforming the industry through data analytics.
The Nespola Scholars will serve as student ambassadors for the MDA program, representing and promoting the program at various student and academic events held by the COM and LIU. After they graduate, the scholars will also serve as mentors for the next class of the scholar program.
The COM and Trustee Nespola will make arrangement and provide opportunities for the Nespola Scholars to meet leading industry executives and obtain mentorship in pursuing a career in data analytics.
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