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2. Master of Science in Business Analytics - Wikipedia

   en.wikipedia.org/wiki/Master_of_Science_in...

   A Master of Science in Business Analytics (MSBA) is an interdisciplinary STEM graduate professional degree that blends concepts from data science, computer science, statistics, business intelligence, and information theory geared towards commercial applications. Students generally come from a variety of backgrounds including computer science ...

3. Master in Data Science - Wikipedia

   en.wikipedia.org/wiki/Master_in_Data_Science

   A Master of Science in Data Science is an interdisciplinary degree program designed to provide studies in scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, [1] [2] similar to data mining.

4. Boston University Questrom School of Business - Wikipedia

   en.wikipedia.org/wiki/Boston_University_Questrom...

   The BU Questrom School of Business offers an online and residential Master of Business Administration (MBA), a Master of Science in Digital Innovation combined with an MBA (MBA/MSDi), a Master of Science in Mathematical Finance (MSMF), a Master of Science in Management Studies (MSMS) and two Doctor of Philosophy (Ph.D.) degrees.

5. Data science - Wikipedia

   en.wikipedia.org/wiki/Data_science

   Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]

6. MHA vs. MBA: How Are They Different? Comparing Salaries, Job ...

   www.aol.com/mha-vs-mba-different-comparing...

   Careers for MHA vs. MBA Understanding the differences between the two degrees can help you decide what to do with an MBA (or an MHA). Each can help prepare you for different roles.

7. Business analytics - Wikipedia

   en.wikipedia.org/wiki/Business_analytics

   The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available. [ 3 ] Previously, analytics was considered a type of after-the-fact method of forecasting consumer behavior by examining the number of units sold in the last quarter or the last year.