THEORY IS GREAT, BUT SKILLS GET THE JOB DONE.

Foundations of Data Management course provides students exposure to fundamental data management skills used in modern information systems that support various operational and functional areas within a business organization. Topics covered in the course have an emphasis on how data is fundamentally identified, organized, described and managed as the most valued asset within an organization. Course emphasis is also on applied learning of concepts and skills for relational data modeling and querying. This course will help prospective Data Science and Analytic business professionals to develop and apply data management skills that will be essential to the success in subsequent coursework.

This course provides students with a foundation in basic statistical analysis, focusing primarily on descriptive univariate statistics. Topics addressed in this course include variables and their properties, measurement scales, descriptive analyses of continuous and categorical variables, Central Limit Theorem, univariate and bivariate estimation, and hypothesis testing logic and procedures. Students will be exposed to hands-on computational examples using R and SPSS as they learn how to apply the various statistical concepts covered in this course to real-life business situations.

This course focuses on the effective communication of data analysis and its insights and implications. Students will learn the principles and techniques for information visualization and representation as well as verbal and written communication. Students will develop proficiency in several of the latest tools for visualization. Students will use real-world business scenarios to gain experience designing and building data visualization and communication.  Best practices will be highlighted and students will receive tailored individual coaching and feedback sessions to accelerate skill improvement.

As data analytic technologies became more advanced, it became progressively easier and easier to execute sophisticated analyses; also, as the volume of the available data exploded, more and more analyses began making use of very large sample sizes. Those trends have a direct impact on the validity and reliability of outcomes of statistical analyses, the investigation of which is the focus of this course. More specifically, in this course the students will take a closer look at topics such as the impact of sample size on statistical significance, and the relationship between practical materiality of findings and the more abstractly framed statistical significance.

This course will offer Business Analytics students with a broad base introduction to multivariate statistical methods, with particular emphasis on explanatory and predictive techniques. The commonly used dependence and interdependence techniques are discussed, including linear and logistic regression, decision trees and select machine learning approaches. The course is focused particularly on topics relating to technique selection, result interpretation and translation of analytic outcome into decision-guiding insights.

Introduces the key concepts, common data collection approaches, sources and methods of epidemiology and population-based health research. For all methods, issues associated with healthcare data quality control, validity and reliability will be covered. Medical insurance, vital records, epidemiological research data and other major sources of public health data will be discussed. This course will introduce students to common and newly emerging data collection sources and methods used in epidemiological research. It will also introduce students to the challenges corporate America is undergoing due to the increase in the prevalence of non-communicable diseases in the workplace, and what population health strategy solutions are implemented to mitigate cost and facilitate optimal population health (for self-insured and fully insured companies). The course will also prepare students to manage the collection and storage of biological specimens, understand the fundamental concepts and methods of biostatistics as applied predominantly to clinical research and public health analyses. Clinical trials will also be addressed.

This course will introduce the current structure and emerging trends shaping the US Healthcare System. Students will learn the complexities of the American healthcare system and the reasons it became so confusing and cumbersome. In addition, the foundation of healthcare data sources for the 3Ps (Providers, Patients, and Payers), the triple aim (Cost, access, and quality) and fundamentals of Health Information Technology (electronic health records, health information exchanges, clinical decision support, and the influence of big data and predictive analytics).  Students will also learn how healthcare performance is measured according to existing quality frameworks such as National Quality Forum (NQF), Healthcare Effectiveness Data and Information Set (HEDIS), and the Agency for Healthcare Research and Quality’s (AHRQ)

Students will develop a capstone project that will include collecting, analyzing and developing insights from healthcare data in an aspect of healthcare that is of interest to them. Faculty will work to pair students with partner companies or institutions to supply data and business challenges to use as the foundation of the capstone project or students can develop a research plan individually with their employer or another business, organization or institution. The Capstone experience will also provide to students critical skills needed to succeed in the healthcare analytics workforce today and to ensure they succeed and are valuable in the workforce today and in the future, as the healthcare business evolves, such as understanding of optimal project management skills, process and performance improvement techniques (Lean), and leadership skills.