This course provides an introduction to 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 government regulations. This course is designed to provide participants with an understanding of data-driven culture and how it can be applied to organizations of any size or industry. Students will gain practical knowledge and skills to help their organizations make better decisions based on data. Students study real world cases illustrating data governance in 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 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 data and consumer 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 have access to 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 strategy 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