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.

Upon completion of this course, students are expected to be able to do the following:

• Utilize datasets to develop statistics and probability to predict future outcomes
• Implement appropriate models needed to analyze and critically evaluate business objectives
• Develop written and oral communication skills required to report on data-intensive business situations
• Organize data-intensive content in a professional setting
• Execute advanced analytics techniques

Examine ethical and legal considerations when working with healthcare data, including HIPAA, decisions made by providers, payers, drug companies, pharmacies, and medical product manufacturers. Students study real-world cases illustrating data governance in health care organizations, issues in patient privacy, data ownership, and restrictions on data analysis.

Upon completion of this course, students are expected to be able to do the following:

• Compare the relationship between healthcare data collection laws and analytic needs
• Evaluate project needs to determine the level of sensitivity required in data set analysis
• Explain the ethical and legal considerations with healthcare data and personal records
• Analyze the needs that different organizations have with data governance

This course explores performance management and technology management for the healthcare industry. This includes patient records, billing, operations, and organizations that support the healthcare industry. Students study healthcare informatics, patient portals, ERP systems, and data repositories such as data marts, data warehouses, and cloud computing. Common software used to collect and query data is also explored.

Upon completion of this course, students are expected to be able to do the following:

• Evaluate different decision support systems used in healthcare
• Differentiate between the various data requirements needed in healthcare organizations for analytics projects
• Execute simple SQL queries to pull data needed for an analytics project
• Create performance management metrics and measurements within the balanced scorecard framework
• Identify common entry points and technology in which users can access patient and operational data

This course covers the collection of data and the use of information as an asset in a healthcare organization. Students learn how to collect and transform data for use in an artificial intelligence project using R-Studio. Students study patterns using machine learning techniques and incorporate business strategies in their analysis.

Upon completion of this course, students are expected to be able to do the following:

• Assess data quality in patient and operational healthcare data
• Evaluate standards of excellence in data collection methods for artificial intelligence
• Build a quality dataset from raw data for use in machine learning models
• Create data mining predictions with categorical regression and data transformation
• Implement statistical models to study and improve model accuracy

Prerequisite: BIA 630

• An undergraduate degree from a regionally accredited institution
• A minimum GPA of 2.75 on a 4.0 scale
• If your overall GPA is below 2.75, you may receive conditional status in the program at the discretion of the program director

No prerequisite coursework or experience is required for this program.

You don’t need GRE/GMAT scores to apply for this program.

Transfer credits are not accepted for this program.

Students with transcripts from universities outside the United States, the United Kingdom, Ireland, New Zealand, Australia, and Canada (except for the Quebec Province) must take and pass English proficiency testing

• Test of English as a Foreign Language (TOEFL) with a minimum score of 72
• International English Language Testing System (IELTS) with a minimum score of 6.0
• Michigan Language Assessment Battery (MELAB) with a minimum score of 74%
• International Test of English Proficiency (ITEP) with a minimum score of 4.0 or Pearson Test of English with a score of 51
• Duolingo with a minimum score of 100

Whether you apply through our online application via hard copy, or in-person with a Saint Mary’s admissions counselor, your application must include the following items:

Official transcripts: An official transcript issued to Saint Mary’s University of Minnesota from the institution posting the applicant’s completed degree and other relevant transcripts documenting program prerequisites and potential transfer credits must be sent separately and directly from your institution. Saint Mary’s University of Minnesota accepts official transcripts electronically from previously attended institutions. Transcripts should be sent to oladmissions@smumn.edu or mailed to:

Transcript Processing Center
Saint Mary’s University of Minnesota
851 Trafalgar Court, Suite 420
Maitland, FL 32751

An up-to-date résumé: Include both your educational and professional experience.

Two references: Provide the names of two people (including title, organization, email, and phone number) who can serve as professional references, if needed, during the application review process. The best references are those who know you professionally, such as current or former supervisors.

Upcoming application deadlines for Saint Mary’s online Human Resource Management graduate certificate:

• Spring: December 18, 2023
• Summer: April 22, 2024
• Fall: August 20, 2024

Upcoming start dates for Saint Mary’s online Human Resource Management graduate certificate:

• Spring: January 8, 2024
• Summer: May 6, 2024
• Fall: September 3, 2024

Once you’ve submitted your application, expect to be notified about acceptance approximately two weeks after your submission date.