600

Probability and statistical techniques as applies to financial markets. Topics will replicate those for CFA® Level I program and typically include correlation and regression in the prediction and evaluation of investment performance.

This course discusses the use of social media analytics tools in order to assess if the current social media plan is successful in achieving its set goals. You will learn how to setup and use some of the leading web analytics tools available such as Google Analytics, Woopra, SocialAnalytics, Webtrends Analytics, Twitter Analytics, Facebook Analytics and/or Visistats. These tools use several techniques including server log files, cookies, web bugs, and page tagging. The course will also cover the key performance indicators for website which are referred to as Website metrics.

This course introduces the use of current information technology for healthcare and health data systems. It is designed to give the student an understanding of the different types of data captured, analyzed, maintained and processed for medical studies.

This course examines the current legal environment for confidentiality of healthcare data. It introduces the laws, regulations, policy and procedures for protecting sensitive patient data. The students learn risk assessment and how to address potential threats in a healthcare setting. Security policy and procedure development methods to secure the healthcare data as required by current laws are discussed in detail.

Designed for the in-depth study of the healthcare systems, this course teaches systems analysis and design specifically for the healthcare data. The students learn how to identify business problem statements for healthcare organizations, how to identify data requirements, how to gather data for detailed systems analysis. Systems development techniques to address business problems by improving existing information systems or developing new information systems are explained. Data manipulation concepts for health information systems are introduced. 

This course introduces students to the current data mining and business intelligence tools for informed decision making. The tools to process and analyze increasingly complicated data sets are explained. Real-life scenarios from finance, CRM, operations, social media marketing, information systems and other disciplines are studied in detail. Specifically decision trees, classification, clustering, segmentation, decision support systems, search algorithms, data mining, factor and discriminant analysis and optimization concepts for both structured and unstructed data are discussed.

This course will allow students to demonstrate proficiency in business analytics with a semester project. The students are expected to employ the skills presented throughout the curriculum in an organized manner to solve realistic business data management problems. Mastery of skills for the student’s identified concentration is expected.