MASTER OF SCIENCE IN DATA ANALYTICS

This course serves as the introduction to the Data Analytics master’s program. Its purpose is to introduce to students R and R Studio as a data analytic platform. Students will learn the basics of R by using it to review and learn basic statistical analyses.

Prerequisite: Admission to degree and/or certificate programs or permission from the Program Director.

This online course is an introduction to analytical programming in Python and Data Management using SQL. The course presents foundational material required for the Data Analytic MS Core Courses to ensure students are ready for that material. Some of the course material will be similar to that offered in a computer science course but will focus on the skills most often required by analysts rather than programming and database specialists.

Prerequisite: Admission to degree and/or certificate programs or permission from the Program Director.

This course provides an experiential introduction to current issues and methods in data mining, applications of some introductory data mining algorithms. A leading statistical and data mining software package, R will be used to apply techniques learned in the class to some modern and classic data sets. Topics include algorithms for supervised and unsupervised machine learning techniques.

Prerequisites: DATA600 Data Analysis using R; DATA604 Python Programming and Data Management.

The ability to communicate the results of data analyses is as important as the analyses themselves. This course will introduce topics important to data storytelling, to allow students to become better data presenters and critical viewers of data. Various visualization tools will be presented, and students will learn how best to report data to various types of audiences. Students will gain experience producing interactive, automatically updating data “dashboards.” Students will collaborate to produce collective visual stories, and provide critical reviews of each other’s work. Relevant areas of data ethics will also be introduced, including the protection of customer or patient privacy and the importance of conveying the uncertainty of results.

Prerequisites: DATA600 Data Analysis using R; DATA604 Python Programming and Data Management.

This course will provide an introduction to the advanced analytics and data mining models using health care datasets. The models taught will include different instances of generalized linear regression model, mixture models, time series models (AR, ARMA, ARIMA, TAR, Change Point). Software packages such as R and SAS will be used throughout the course.

Prerequisites: DATA600 Data Analysis using R, DATA604 Python Programming and Data Management; DATA610 Introduction to Data Mining Core course.

This course provides an experiential overview of current issues in data analytics from both analytic and computer sciences perspectives. The focus is on learning techniques for 1) scraping, cleaning, and manipulation of raw data from a variety of live sources and preparing them for analysis, 2) learning to manipulate and reorganize data to apply a variety of analytic tools, and 3) manipulate a variety of data features to enhance to predictive power of statistical models. The focus of this course will be primarily on techniques and tools used to extract data from various sources (primarily live and active data streams), prepare it for analysis, and then apply those analyses with a special emphasis on the understanding of feature engineering: the process of creating representations of data that increase the effectiveness of a predictive model.

Prerequisites: DATA600 Data Analysis using R, DATA604 Python Programming and Data Management; DATA610 Introduction to Data Mining Core courses.

This course will utilize Python to develop text and image processing techniques. Students will leverage industry standard methodologies for text and image processing. Students will gain experience with feature extraction and classification techniques in both visual and textual domains using neural networks and traditional machine learning models. This is a 3-credit hour project-based course.

Prerequisites: DATA604 Python Programming and Data Management; DATA612 Visualization, Storytelling and Ethics

This course will focus on healthcare data governance, management, and ethics. The course will explore the techniques involved with healthcare data capture, cleaning, storage, and security and examine methods to overcome challenges of managing healthcare data across multiple systems. A critical review of the ethical considerations of healthcare algorithm utilization will be conducted.

Prerequisite: None

The purpose of this course is to expose students to the health outcomes research and help them prepare for non-traditional career options, including pharmaceutical industry, managed care, or fellowships. It would entail learning to conduct research in the field of health outcomes and design a research study in a specific therapeutic area or condition. Students are encouraged to select a therapeutic area of interest before the class begins. They can work in groups or individually for the research projects, based on their preferences. They would be required to use SAS Enterprise Guide to conduct the statistical analysis. Overall, the class will help students learn & apply research methodology and statistics to the specific therapeutic area or condition of interest, from the health outcomes perspective.

Prerequisites: None

This course examines the current and future states of healthcare data and explores methods to leverage analytics to optimize healthcare outcomes and value. Students will learn standard healthcare terminologies and relational databases. Students will utilize SQL for analytic applications.

Prerequisite: DATA604 Python Programming and Data Management

In this course, students will collaborate with an institutional partner to apply healthcare analytics principles and techniques to a longitudinal project of mutual interest. Students may choose from a variety of focus areas including hospital and health systems, local health initiatives, technology and innovation, healthcare educational institutions, and industry. This course may be on campus, off-campus, hybrid, or online pending the nature of the scholarly project. The student will submit a project that successfully meets the course outcomes and is approved by the subject matter expert and Program Director.

Prerequisite: DATA 620 Utilization of Health Data; DATA622 Healthcare Data Literacy and Analytics; DATA624 Statistics and Research Methods for Healthcare Analytics.

This course covers how organizations can use analytics to gather and utilize information to evaluate risk, increase profitability, and generally improve business performance. The material emphasizes data-driven managerial decision making in a structured approach to problem solving. Topics include an introduction to big data, viewing data and analytics capabilities as strategic assets, and employing analytical models to solve problems.

Prerequisite: DATA604

This course reviews the statistical processes and analytical tools marketing managers may need and could employ in order to make decisions regarding marketing functions such as segmentation, targeting, positioning, among other functions. Further, this course reviews the best analytical processes marketing managers may use to evaluate customer lifetime values, customer buying behaviors, international market analyses, and digital marketing effectiveness. The statistical analyses covered include, but are not limited to cluster analyses, descriptive analyses, analyses of variance, regressions, structural equation modeling, and multilevel analyses.

Prerequisite: DATA604

This class covers the analysis of data related to accounting professionals. The focuses include analytic techniques for decision making and the examination of “big data” involving accounting information. Data analytics has become a relevant skill for all business managers and particularly accountants who often know both internal and external data better than anyone inside the organization.

Prerequisite: DATA604

As the last course in the MS in Analytics program, students will have the opportunity to leverage the learning from all core classes and apply their skills in a real-world project. Supply chains are dynamic and complex environments that involve making decisions on different levels and demand the use of real data for real business problems. A set of different analytics tools will be used to collect, organize, and analyze data that support key supply chain decisions, as well as the different challenges and opportunities they pose.

Prerequisites: DATA640, DATA642, DATA644