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Mary\u2019s University of Minnesota from the institution posting the applicant\u2019s completed degree and other relevant transcripts documenting program prerequisites and potential transfer credits must be sent separately and directly from your institution. Saint Mary\u2019s University of Minnesota accepts official transcripts electronically from previously attended institutions. Transcripts should be sent to oladmissions@smumn.edu or mailed to:\nTranscript Processing Center\nSaint Mary\u2019s University of Minnesota\n851 Trafalgar Court, Suite 420\nMaitland, FL 32751\n \n\n                    ","description":"\n                    \n                    \n\n    Program Highlights\n        \n  \n\n\n    \n        \nDigital Badging for all Graduate Certificate Courses\nApply Graduate Certificate Credits to an MS BIDA or MBA\nNo Prerequisite Courses or GRE\/GMAT Required\n\n    \n    \n\n        \n  \n\n\n    \n        Request Info        \n            \n        \n    \n\n    \n                ","hasCourse":[{"@type":"Course","@id":"https:\/\/www.smumn.edu\/degree\/graduate-certificate\/business-analytics\/#Highlight20260320195826705-17953_0_EducationalOccupationalProgram_hasCourse_Course","name":"\n                          BIA 630 Data Analysis and Business Modeling (3 credits)                          \n                              \n                          \n                          \n                              \n                          \n                        ","description":"\n                      This course introduces advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. Students are introduced to descriptive, predictive, prescriptive analytics, and optimization models. The course utilizes Microsoft Excel to engineer and analyze business models. Students identify the proper use of complete regression, optimization, and exponential smoothing models.\nUpon completion of this course, students are expected to be able to do the following:\n\nUtilize datasets to develop statistics and probability to predict future outcomes\nImplement appropriate models needed to analyze and critically evaluate business objectives\nDevelop written and oral communication skills required to report on data-intensive business situations\nOrganize data-intensive content in a professional setting.\nExecute advanced analytics techniques\n\n\n                    "},{"@type":"Course","@id":"https:\/\/www.smumn.edu\/degree\/graduate-certificate\/business-analytics\/#Highlight20260320195826705-17953_1_EducationalOccupationalProgram_hasCourse_Course","name":"\n                          BIA 640 Data Visualization and Storytelling (3 credits)                          \n                              \n                          \n                          \n                              \n                          \n                        ","description":"\n                      This course explores best-practice data visualization techniques. 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Students study effective strategies and visualization tools to communicate with business decision-makers.\nUpon completion of this course, students are expected to be able to do the following:\n\nSource appropriate data to create effective visualization\u2019s\n a compelling story with data to drive business action\nDesign persuasive data visualizations to communicate information to the viewer\nBuild visualizations that assist in error detection and data preparation\n\n\n                    "},{"@type":"Course","@id":"https:\/\/www.smumn.edu\/degree\/graduate-certificate\/business-analytics\/#Highlight20260320195826705-17953_2_EducationalOccupationalProgram_hasCourse_Course","name":"\n                          BIA 645 Communications and Content Strategies (3 credits)                          \n                              \n                          \n                          \n                              \n                          \n                        ","description":"\n                      This course focuses on explaining complex datasets, models, and analyses to stakeholders, including internal and external organizations and personnel of multiple disciplines. 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