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Data Analytics Courses
BDA211 Introduction to Applied Data Analytics
[3–0, 3 cr.]
This course introduces students to the fundamentals of descriptive analytics highlighting the broader issues relating to framing problems and identifying key performance measures.
BDA311 Data Driven Design Thinking
[3–0, 3 cr.]
This course introduces students to the iterative problem-solving process of discovery, ideation, and experimentation to gain insight and yield innovative solutions for virtually any type of organizational or business challenge.
BDA401 Methodologies and Model Building in Data Analytics
[3–0, 3 cr.]
In this course students will be introduced to a variety of predictive and prescriptive analytics tools. In this context students will explore the common pitfalls in interpreting results from data driven models, especially those associated with big data and real-time streaming data. Collectively, this course will help students internalize a core set of practical and effective machine learning methods and concepts and apply them to solve some real-world problems.
Pre-requisites: BDA211 Introduction to Applied Data Analytics
BDA8110 Business Analytics for Competitive Advantage
[3–0, 3 cr.]
This course examines real world examples of how insights gained through analytics to significantly improve a business or industry. Through our tour of real-world transformations driven by analytics, students will gain knowledge in the use of descriptive, diagnostic, predictive, and prescriptive analytics models.
BDA8120 Cognitive Analytics
[3–0, 3 cr.]
This course offers students an understanding of the “data” or the conscious/non-conscious information processing in our brains explaining the consumer’s decision-making on a non-conscious level. Neuro-metrical and biometrical measures, models and technologies help students understand and predict consumer behavior using non-traditional research methodologies.
Cognitive analytics is about understanding brain science (neuro-science, behavioral economics & social psychology) and exposing as well as measuring the consumer’s hidden data from the mind and body. Applying data mining techniques to Neuro Marketing data (mind-mining) will provide students with a much deeper and richer understanding of consumer preferences, choices and behavior.
BDA8140 Analytics Applications
[3–0, 3 cr.]
The applications studied in this course rely heavily on predictive and prescriptive analytics tools. Students will learn how to define business problems requiring prediction and then select the most appropriate forecasting strategy to meet the application. Similarly, students will learn how to frame a decision problem and then select and apply the appropriate data driven decision making strategy.
BDA8150 Big Data Analytics
[3–0, 3 cr.]
This course provides an understanding of the business value of big data, the importance of effective management of big data, and the development of technical competencies using leading-edge platforms for managing and manipulating structured and unstructured big data.
BDA8160 Machine Learning for Predictive Analytics
[3–0, 3 cr.]
Topics include data cleaning, and exploration; predictive models, linear and nonlinear regression models, decision tree analysis, and discriminant analysis; resampling techniques; clustering analysis and dimension reduction. Applications to marketing, operations, finance, and risk management.
BDA8170 Information Security for User Behavior Analytics
[3–0, 3 cr.]
This course covers key risks to information systems and business data. Students will apply data analytics techniques across different dimensions to provide effective information security analytics. Threats to normal user behavior are compared and contrasted by utilizing the user behavior analytics approach Normal behavior.
BDA8180 Healthcare Analytics
[3–0, 3 cr.]
This is an introductory course to the healthcare research fundamentals and methodologies. Topics include healthcare research design, data collection, data analysis, and operations research and operations management tools applied to the health care management sector.
BDA8190 Ethics in Analytics
[3–0, 3 cr.]
This course examines the ethical aspect of business analytics. It covers topics in data ethics regarding the moral obligations, values and principles governing data protection and privacy rights. It also explores how data driven decisions can have a profound impact on society and how businesses can refine their strategy to learn from past mistakes and built on previous successes.
BDA8200 Data Visualization
[3–0, 3 cr.]
An introductory course to data visualization. It covers visualization techniques applied to extract information that aids in the data driven decision-making process. Students will use various visualization tools to visually explore business problems, develop data visualizations, dashboards, and stories. Students will focus on communicating the results in an effective and informative manner.
BDA8970 Case Studies in Business Analytics (Capstone course)
[3–0, 3 cr.]
The capstone course focuses on the use and application of the various descriptive, predictive and prescriptive analytics methods for solving real life business problems. Students will apply the business analytics tools, knowledge and skills to solve an actual business problem. They will report and present their findings and recommendations. Emphasis is given to integrating the business analytics concepts and ideas to provide a perspective on how a company can apply fundamental data analysis to derive the actionable business decisions.
BDA8980 Research Project in Business Analytics
[3–0, 3 cr.]
This course entails the application of research methods to a current topic relevant to business analytics. The thesis must incorporate the student’s hypothesis, test methods, test results, and conclusions, in a report available to later researchers. In some cases, the faculty may authorize expanded research procedures resulting in high-quality publication.
QBA8100 Core Business Analytics & Statistics
[3–0, 3 cr.]
Covers statistical and business analytics tools useful for making effective managerial decisions in a disorganized and uncertain environment in all functional areas of business. Students learn the essential statistical topics of description, probability, inference and regression, and how to apply them using Microsoft Excel. They learn how to choose appropriate statistical methods in realistic business contexts and how to interpret and effectively communicate results. Students also learn how to use data visualization tools, pivot tables and charts, data tables, optimization models and Monte Carlo simulation.
