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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 and 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\/artificial-intelligence\/#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. 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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\/artificial-intelligence\/#Highlight20260320195826705-17953_1_EducationalOccupationalProgram_hasCourse_Course","name":"\n                          DIGA 620 Data Engineering (3 credits)                          \n                              \n                          \n                          \n                              \n                          \n                        ","description":"\n                      The course utilizes the data processing requirements necessary to implement technology-based analytics. 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The course also investigates the legal and ethical considerations of data management.\nUpon completion of this course, students are expected to be able to do the following:\n\nPerform extract, transform, and load (ETL) processes using structured and unstructured data formats\nAssess data for error and implement techniques to improve data integrity\nDetermine appropriate data formats for given situations\nDesign and document processes for converting raw data into a product suitable for analysis\nIdentify legal and ethical issues related to the processing and dissemination of data\n\n\n                    "},{"@type":"Course","@id":"https:\/\/www.smumn.edu\/degree\/graduate-certificate\/artificial-intelligence\/#Highlight20260320195826705-17953_2_EducationalOccupationalProgram_hasCourse_Course","name":"\n                          BIA 680  Intelligence Fundamentals (3 credits)                          \n                              \n                          \n                          \n                              \n                          \n                        ","description":"\n                      This course focuses on the framework for utilizing the Python programming language for artificial intelligence applications. 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Audience analysis, and effective strategic communication are studied. Students identify and analyze problems. Professionalism in both oral and written communication is expected.\nUpon completion of this course, students are expected to be able to do the following:\n\nConduct stakeholder analysis and communications results\nIdentify business problems and create analytical approaches to solve them\nWrite reports based on best-practice data analysis frameworks\nDemonstrate best practice communication techniques to visualize, explore, and act on data science findings\n\n\n                    "},{"@type":"Course","@id":"https:\/\/www.smumn.edu\/degree\/graduate-certificate\/artificial-intelligence\/#Highlight20260320195826705-17953_4_EducationalOccupationalProgram_hasCourse_Course","name":"\n                          BIA 681 Deep Learning Methodology (3 credits)                          \n                              \n                          \n                          \n                              \n                          \n                        ","description":"\n                      Note: Students may choose either BIA 645 or BIA 681.\nThis course introduces students to the complex nature of deep learning. 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