Master of Science
in Data Science,
Business Analytics

An introduction to statistical modeling and data analysis. This course uses R programming to explore data variation, model data, and evaluate models. You’ll also learn to analyze and evaluate different types of regression models and error analysis methods.

In this course, you’ll learn to apply data analytics to facilitate modern knowledge discovery techniques. Coursework will focus on different forms of data, gap analysis, model building, and interpretation as foundations for analytical study.

This course applies the data management process to analytics. You’ll explore and learn the processes of acquiring and auditing data, assembling data into a modeling sample, performing basic data integrity checks, cleansing data, feature engineering, and data visualization.

An examination of data mining methods and predictive modeling. Through a variety of case studies and practical industry applications, you’ll explore design objectives, data selection and preparation, classification and decision tree methods, and predictive modeling.

In this course, you’ll apply methods for analyzing continuous data for knowledge discovery. Analytic continuous data concepts and methods are developed with practical skills in exploratory data analysis. Areas of focus will include descriptive statistics, goodness-of-fit tests, correlation measures, single and multiple linear regression, and analysis of variance and covariance. Coursework will use case studies and real world data to leverage statistical assessment and interpretation.

This course explores and applies methods for analyzing categorical data for knowledge discovery. Analytic categorical data analysis concepts and methods are developed with practical skills in exploratory data analysis. Areas of focus include descriptive statistics of discrete data, contingency tables, and methods of generalized linear models. Instruction will use case studies and real world data to leverage statistical assessment and interpretation.

An examination of advanced applications of data analytics for knowledge discovery. This course explores several advanced techniques in data analytics, including methods for longitudinal data, factor and principal components analysis, multivariate logistic regression, and multivariate analysis of variance (ANOVA). Coursework will use case studies and real world data to leverage statistical assessment and interpretation.

Performance management (PM) and supply chain management (SCM) require metrics and indicators to measure value, weaknesses, and opportunities. Using data to set objectives and measure performance through analytics has been a proven method for proactive problem-solving and business success. This course focuses on the application of data analytics to performance and process management. Topics of study include data improvement, data quality assessment, data cleansing and normalization, and process improvement.

New technologies have opened new arenas in prediction and marketing. This course introduces a variety of predictive analytics topics and explains their role in enterprise marketing. You’ll apply predictive analytics applications to derive an organization’s strategic direction. Market and product analysis will be used to illustrate the development process. Finally, you’ll learn to interpret analysis results in terms of business impact.

The uncertainties of the financial world affect the outcomes of sound investments. Probabilistic analytic models help to alleviate the ambiguity inherent in investment decision-making. In this course, you’ll explore probability theories, tools, and model-building as you learn to construct, process, and present probabilistic information. Topics of focus include: conditioning and probability trees, random variables, distributions expectation, and problem-solving for axioms of probability.

Every step of an online transaction should be considered with security in mind. In this course, you’ll examine the protocols and practices that maintain proper security and privacy while confidential data is accessed, used, transmitted, and stored. You’ll also consider how to mitigate threats and establish ethical practices that alert organizations to potential security breaches.

An introduction to and investigation of advanced topics in AI (artificial intelligence) and optimization in various state-of-the-art applications.

The first of three capstone courses, this class comprises the first stage of your master’s thesis project. Through your research of analytic project design, problem framing, team-building, collaboration, and technical presentation, you’ll propose a data science project to advisors and stakeholders. Your team’s submission should include strategic and technical aspects of data acquisition, data cleaning, and analytic methodology.

This course is a continuation of your master’s-level research in analytic project implementation, technical writing, and project presentation. Your team’s data science project will include strategic and technical aspects of data acquisition, data cleaning, and analytic methodology for presentation to your project advisors and stakeholders.

In this course, your team will complete and present your master’s-level data science project. The finished project will include strategic and technical aspects of data analysis and visualization, and will be presented in a written thesis to your project advisors and stakeholders.